Mechanical cycle assist device, data processing method, electronic device, and storage medium

Abstract

The embodiment of the invention provides a mechanical circulation auxiliary device, a data processing method, electronic equipment and a storage medium. The mechanical circulation auxiliary device comprises a control device, a driving assembly, a power assembly and a pipeline assembly. The control device is configured to obtain first parameter setting data corresponding to the operation setting parameters and control the driving assembly to work based on the first parameter setting data so as to drive the power assembly to pump blood, the control device is associated with a user interface, and the user interface is configured to display cardiovascular parameter monitoring data corresponding to the cardiovascular parameters and / or display the cardiovascular parameter monitoring data corresponding to the cardiovascular parameters. Displaying data change relation prediction information between the cardiovascular parameters and the operation setting parameters; wherein the cardiovascular parameters comprise at least one of a heart index, a blood injury parameter, a pressure volume ring and ventricular elasticity; the data change relation prediction information is used for representing the data change condition of the cardiovascular parameters under the condition that the mechanical circulation auxiliary device has different operation setting parameters.

Description

Technical Field

[0001] The present invention relates to the field of medical devices, and in particular, to a mechanical circulatory assist device, a data processing method, an electronic device, and a storage medium. Background Art

[0002] With the progress of technology, Mechanical Circulatory Support (MCS) devices have been increasingly used in heart failure patients. Mechanical circulatory assist devices play a crucial role in a variety of clinical scenarios, such as acute cardiogenic shock, severe left ventricular dysfunction, pediatric heart failure, and long-term cardiac support. Mechanical circulatory assist devices can include: short-term assist devices such as implantable artificial hearts and Extracorporeal Membrane Oxygenation (ECMO) devices, and long-term assist devices such as Left Ventricular Assist Devices (LVADs) and Right Ventricular Assist Devices (RVADs).

[0003] At the same time, mechanical circulatory assist devices exhibit design differences that hinder personalized treatment and affect clinical outcomes, limiting the optimization of care. During the use of mechanical circulatory assist devices, it is necessary to monitor the cardiovascular parameters of the target object, and then optimize the operating setting parameter data of the mechanical circulatory assist device to improve the working effect of the mechanical circulatory assist device. However, the monitoring of the cardiovascular parameters of the target object usually has the characteristics of high invasiveness, suboptimal accuracy, or non-persistence. Summary of the Invention

[0004] In view of this, embodiments of the present disclosure provide a mechanical circulatory assist device, a data processing method, an electronic device, and a storage medium.

[0005] According to a first aspect of the embodiments of the present disclosure, a mechanical circulatory assist device is provided. When in use, the mechanical circulatory assist device is connected and coupled to the cardiovascular system in a target object, and there are a drainage position and a return position between the mechanical circulatory assist device and the cardiovascular system;

[0006] The mechanical circulatory assist device includes a control device, a drive assembly, a power assembly, and a pipeline assembly. One end of the pipeline assembly is located at the drainage position, and the other end of the pipeline assembly is located at the return position;

[0007] The control device is configured to: obtain first parameter setting data corresponding to the operation setting parameters, and control the driving component to operate based on the first parameter setting data, so as to drive the power component to pump blood, wherein the power component drives the blood to flow into the pipeline component from the drainage position and flow out from the return position;

[0008] The control device is associated with a user interface, and the user interface is configured to:

[0009] display cardiovascular parameter monitoring data corresponding to the cardiovascular parameters, and / or display prediction information on the data change relationship between the cardiovascular parameters and the operation setting parameters;

[0010] wherein, the cardiovascular parameters include at least one of cardiac index, blood damage parameter, pressure-volume loop, and ventricular elasticity; the prediction information on the data change relationship is used to characterize the data change situation of the cardiovascular parameters when the mechanical circulatory assist device is under different operation setting parameters.

[0011] In some embodiments, the user interface is further configured to: based on the prediction information on the data change relationship, display parameter setting recommendation information corresponding to the operation setting parameters.

[0012] In some embodiments, the prediction information on the data change relationship is presented as a prediction data statistical chart, and the prediction data statistical chart includes cardiovascular parameter data corresponding to respective multiple operation setting parameter data;

[0013] wherein, the multiple operation setting parameter data include the first parameter setting data and multiple second parameter setting data, the first parameter setting data is the setting data currently corresponding to the operation setting parameters, and the second parameter setting data is different from the first parameter setting data;

[0014] The multiple cardiovascular parameter data include the cardiovascular parameter monitoring data and multiple cardiovascular parameter prediction data, and the cardiovascular parameter prediction data corresponds to the second parameter setting data one by one.

[0015] In some embodiments, the parameter setting recommendation information includes recommended setting data corresponding to the operation setting parameters;

[0016] The recommended setting data is associated with the operation setting parameter data corresponding to the target data, and the target data is the data meeting the conditions among the multiple cardiovascular parameter prediction data.

[0017] In some embodiments, the parameter setting recommendation information includes first identification information corresponding to the recommended setting data, and the user interface is further configured to:

[0018] Display the second identification information corresponding to the first parameter setting data;

[0019] In the case where the first identification information and the second identification information indicate that the first parameter setting data does not match the recommended setting data, issue a parameter adjustment prompt message and / or a risk prompt message.

[0020] In some embodiments, the operating setting parameter includes a rotation speed, and the data change relationship prediction information includes a first data statistical chart for characterizing the data change trend between the cardiac index and the rotation speed;

[0021] The first data statistical chart includes cardiac index data corresponding to multiple rotation speed setting data;

[0022] Among them, the multiple rotation speed setting data includes a first rotation speed setting data and multiple second rotation speed setting data. The first rotation speed setting data is the target rotation speed currently set by the mechanical circulatory assist device, and the first rotation speed setting data is different from the second rotation speed setting data;

[0023] The multiple cardiac index data includes the cardiac index monitoring data corresponding to the first rotation speed setting data and multiple cardiac index prediction data, and the multiple cardiac index prediction data corresponds to the multiple second rotation speed setting data one by one.

[0024] In some embodiments, the cardiac index includes a native cardiac index and a total cardiac index. The first data statistical chart includes a first trend line and / or a second trend line. The first trend line is used to characterize the data change trend between the native cardiac index and the rotation speed, and the second trend line is used to characterize the data change trend between the total cardiac index and the rotation speed.

[0025] In some embodiments, the blood injury parameter includes a hemolysis index, and the first data statistical chart further includes a third trend line for characterizing the data change trend between the hemolysis index and the rotation speed.

[0026] In some embodiments, the first data statistical chart further includes a recommended rotation speed range, and the rotation speed setting data within the recommended rotation speed range satisfies at least one of the following conditions:

[0027] The native cardiac index data corresponding to the maximum rotation speed setting data within the recommended rotation speed range is greater than a first threshold;

[0028] The total cardiac index data corresponding to the minimum rotation speed setting data within the recommended rotation speed range is greater than or equal to a second threshold;

[0029] The maximum rotational speed setting data within the recommended rotational speed range is less than the first rotational speed threshold, where the first rotational speed threshold is the rotational speed setting data corresponding to when the native cardiac index equals the first threshold in the first trend line, and there is a first difference between the first rotational speed threshold and the maximum rotational speed setting data; optionally, the first threshold is 0;

[0030] The hemolysis index data corresponding to the maximum rotational speed setting data within the recommended rotational speed range is less than the third threshold.

[0031] In some embodiments, the first parameter setting data includes first rotational speed setting data, and the user interface is further configured to perform at least one of the following operations:

[0032] When the first rotational speed setting data does not fall within the recommended rotational speed range, display a rotational speed adjustment prompt message;

[0033] When the first rotational speed setting data is greater than the maximum rotational speed setting data, display at least one of an over - support risk prompt message, a suction risk message, and a blood damage risk message;

[0034] When the first rotational speed setting data is less than the minimum rotational speed setting data, display an under - support risk prompt message.

[0035] In some embodiments, the operating setting parameter includes rotational speed, and the data change relationship prediction information includes a second data statistical chart, where the second data statistical chart is used to characterize the data change trend between the pressure - volume loop and the rotational speed;

[0036] The second data statistical chart includes pressure - volume loop curves corresponding to multiple rotational speed setting data respectively, and the pressure - volume loop curves are used to characterize the change relationship between the ventricular pressure and the ventricular volume corresponding to the ventricle;

[0037] Among them, the multiple rotational speed setting data includes first rotational speed setting data and multiple second rotational speed setting data, where the first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data;

[0038] The multiple pressure - volume loop curves include a pressure - volume loop monitoring curve corresponding to the first rotational speed setting data and multiple pressure - volume loop prediction curves, and the multiple pressure - volume loop prediction curves correspond one - to - one with the multiple second rotational speed setting data.

[0039] In some embodiments, the cardiovascular parameter further includes ventricular elastance, and the second data statistical chart is further used to characterize the data change trend between the ventricular elastance and the rotational speed;

[0040] The second data statistical chart includes ventricular elasticity lines corresponding to multiple rotation speed setting data respectively, and the slope of the ventricular elasticity line is associated with the ventricular elasticity;

[0041] Among them, the multiple rotation speed setting data include a first rotation speed setting data and multiple second rotation speed setting data. The first rotation speed setting data is the target rotation speed currently set by the mechanical circulatory assist device, and the first rotation speed setting data is different from the second rotation speed setting data;

[0042] The multiple ventricular elasticity lines include a ventricular elasticity monitoring line corresponding to the first rotation speed setting data and multiple ventricular elasticity prediction lines, and the multiple ventricular elasticity prediction lines correspond to the multiple second rotation speed setting data one by one.

[0043] In some embodiments, the second data statistical chart further includes recommended rotation speed information, and the rotation speed setting data in the recommended rotation speed information satisfies at least one of the following;

[0044] The pressure-volume loop curve corresponding to the rotation speed setting data in the recommended rotation speed information satisfies the pressure-volume loop condition;

[0045] The ventricular elasticity line corresponding to the rotation speed setting data in the recommended rotation speed information satisfies the ventricular elasticity condition;

[0046] The user interface is further configured to:

[0047] In the case where the first rotation speed setting data does not match the recommended rotation speed information, display a rotation speed adjustment prompt message and / or a risk prompt message for indicating ventricular systolic performance.

[0048] In some embodiments, the operating setting parameter includes a rotation speed, the cardiovascular parameter includes ventricular elasticity, and the data change relationship prediction information includes a third data statistical chart, and the third data statistical chart is used to characterize the data change trend between the ventricular elasticity and the rotation speed;

[0049] The third data statistical chart includes ventricular elasticity data corresponding to multiple rotation speed setting data respectively;

[0050] Among them, the multiple rotation speed setting data include a first rotation speed setting data and multiple second rotation speed setting data. The first rotation speed setting data is the target rotation speed currently set by the mechanical circulatory assist device, and the first rotation speed setting data is different from the second rotation speed setting data;

[0051] The multiple ventricular elasticity data include ventricular elasticity monitoring data corresponding to the first rotation speed setting data and multiple ventricular elasticity prediction data, and the multiple ventricular elasticity prediction data correspond to the multiple second rotation speed setting data one by one.

[0052] In some embodiments, the third data statistical chart further includes recommended rotational speed information, and the ventricular elasticity data corresponding to the rotational speed setting data within the recommended rotational speed information satisfies the ventricular elasticity condition;

[0053] The user interface is further configured to:

[0054] In the case where the first rotational speed setting data does not match the recommended rotational speed information, display a rotational speed adjustment prompt message and / or a risk prompt message for indicating ventricular systolic performance.

[0055] In some embodiments, the operating setting parameter includes rotational speed, the blood damage parameter includes a hemolysis index, the data change relationship prediction information includes a fourth data statistical chart, the fourth data statistical chart is used to characterize the data change trend between the hemolysis index and the rotational speed, and the fourth data statistical chart includes hemolysis index data corresponding to multiple rotational speed setting data;

[0056] Wherein, the multiple rotational speed setting data includes a first rotational speed setting data and multiple second rotational speed setting data, the first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data;

[0057] The multiple hemolysis index data includes the hemolysis index monitoring data corresponding to the first rotational speed setting data and multiple hemolysis index prediction data, and the multiple hemolysis index prediction data correspond one-to-one to the multiple second rotational speed setting data.

[0058] In some embodiments, the fourth data statistical chart further includes a recommended rotational speed range, and the hemolysis index data corresponding to the rotational speed setting data within the recommended rotational speed range is lower than a third threshold;

[0059] The user interface is further configured to perform at least one of the following operations:

[0060] In the case where the first rotational speed setting data does not fall within the recommended rotational speed range, display a rotational speed adjustment prompt message;

[0061] In the case where the first rotational speed setting data is greater than the maximum rotational speed setting data of the recommended rotational speed range, display a hemolysis risk prompt message.

[0062] In some embodiments, the cardiovascular parameter monitoring data is presented as a monitoring data statistical chart, the monitoring data statistical chart includes the monitoring data corresponding to the cardiovascular parameters at multiple time nodes, and the monitoring data statistical chart is used to display the change of the physiological state of the cardiovascular system during the support process of the mechanical circulatory assist device.

[0063] In some embodiments, the monitoring data statistical chart includes at least one of the following:

[0064] A systolic performance statistical chart of the heart, the systolic performance statistical chart of the heart includes ventricular elasticity monitoring data corresponding to a plurality of the time nodes, and / or, a pressure-volume loop monitoring curve corresponding to a plurality of the time nodes, and the systolic performance statistical chart of the heart is used to characterize the change of the systolic performance of the heart during the support process of the mechanical circulatory assist device;

[0065] A cardiac index monitoring data chart, the cardiac index monitoring data chart includes cardiac index monitoring data corresponding to a plurality of the time nodes, and the cardiac index monitoring data chart is used to characterize the change of the cardiac output during the support process of the mechanical circulatory assist device;

[0066] A blood injury monitoring data chart, the blood injury monitoring data chart includes blood injury parameter monitoring data corresponding to a plurality of the time nodes, and the blood injury monitoring data chart is used to characterize the blood injury during the support process of the mechanical circulatory assist device.

[0067] In some embodiments, the user interface is further configured to:

[0068] When the monitoring data statistical chart indicates the performance recovery of the cardiovascular system, display weaning advice information;

[0069] And / or, when the monitoring data statistical chart indicates the performance deterioration of the cardiovascular system, display risk warning information.

[0070] In some embodiments, the control device is further configured to execute:

[0071] Obtain a blood circulation model, and the blood circulation model is used to simulate the blood flow situation after the mechanical circulatory assist device is connected to the cardiovascular system;

[0072] Obtain the physiological parameter data associated with the cardiovascular system, and obtain the operation setting parameter data of the mechanical circulatory assist device;

[0073] Drive the blood circulation model to run based on the physiological parameter data and the operation setting parameter data, and output the cardiovascular parameter monitoring data, and / or, the data change relationship prediction information.

[0074] In some embodiments, the control device is specifically configured to execute:

[0075] Obtain the cardiovascular model corresponding to the cardiovascular system and the mechanical circulatory support model corresponding to the mechanical circulatory support device, where the mechanical circulatory support model includes a reduced-order model obtained by reducing the order of the computational fluid dynamics model corresponding to the mechanical circulatory support device;

[0076] Couple the cardiovascular model and the mechanical circulatory support model according to the drainage position and the return position to obtain a blood circulation model.

[0077] In some embodiments, the control device is specifically configured to execute:

[0078] Fit the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0079] Traverse multiple sets of parameter setting data corresponding to the operating setting parameters;

[0080] When traversing to the current set of parameter setting data, drive the blood circulation model to run based on the model parameter data and the parameter setting data, and output the cardiovascular parameter data corresponding to the cardiovascular system;

[0081] Determine the data change relationship prediction information according to the cardiovascular parameter data corresponding to the multiple sets of parameter setting data.

[0082] In some embodiments, the cardiovascular model is a lumped parameter model, the circuit structure in the lumped parameter model is used to characterize the cardiovascular system, the lumped parameter model includes a variable capacitor for simulating the ventricle, and the lumped parameter model and the mechanical circulatory support model are connected through pressure-flow coupling;

[0083] The control device is specifically configured to execute:

[0084] Fit the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0085] Drive the cardiovascular model to run based on the model parameter data, and output the pressure gradient data between the drainage position and the return position;

[0086] Input the pressure gradient data and the parameter setting data into the mechanical circulatory support model to drive the blood circulation model to run, and output the flow data and blood damage data of the mechanical circulatory support model;

[0087] Input the flow data into the lumped parameter model to update the current change in the circuit structure;

[0088] Determine the native cardiac output data based on the current data of the variable capacitor;

[0089] Determine the pressure change data of the ventricle based on the voltage change data of the variable capacitor;

[0090] Determine the volume change data of the ventricle based on the charge change data of the variable capacitor;

[0091] Determine the total cardiac output data according to the native cardiac output data and the flow data;

[0092] Determine at least one of the pressure-volume loop data and the ventricular elastance data based on the pressure change data of the ventricle and the volume change data of the ventricle

[0093] Determine the native cardiac index data based on the native cardiac output data;

[0094] Determine the total cardiac index data based on the total cardiac output data.

[0095] According to a second aspect of the embodiments of the present disclosure, there is provided a data processing method for a mechanical circulatory assist device, which is connected and coupled to the cardiovascular system in a target object's body when in use, and there are a drainage position and a return position between the mechanical circulatory assist device and the cardiovascular system; the mechanical circulatory assist device includes a control device, a drive assembly, a power assembly, and a pipeline assembly, one end of the pipeline assembly is located at the drainage position, and the other end of the pipeline assembly is located at the return position; the method includes: obtaining first parameter setting data corresponding to the operation setting parameters, and controlling the drive assembly to work based on the first parameter setting data to drive the power assembly to pump blood, wherein the power assembly drives the blood to flow into the pipeline assembly from the drainage position and flow out from the return position; the method includes:

[0096] Display the cardiovascular parameter monitoring data corresponding to the cardiovascular parameters and / or display the data change relationship prediction information between the cardiovascular parameters and the operation setting parameters on the user interface associated with the mechanical circulatory assist device;

[0097] Wherein, the cardiovascular parameters include at least one of cardiac index, blood damage parameter, pressure-volume loop, and ventricular elastance; the data change relationship prediction information is used to characterize the data change situation of the cardiovascular parameters when the mechanical circulatory assist device is under different operation setting parameters.

[0098] According to a second aspect of the embodiments of the present disclosure, there is provided an electronic device, including a processor, a memory, and an executable program stored on the memory and capable of being run by the processor. When the processor runs the executable program, it performs the steps of the mechanical circulatory assist device control method as described in the second aspect.

[0099] According to a third aspect of the embodiments of the present disclosure, there is provided a storage medium, on which an executable program is stored. When the executable program is executed by a processor, it implements the steps of the mechanical circulatory assist device control method as described in the second aspect.

[0100] The beneficial effects brought by the technical solutions provided in this application at least include:

[0101] Embodiments of the present disclosure provide a mechanical circulatory assist device, a data processing method, an electronic device, and a storage medium.

[0102] By displaying the cardiovascular parameter monitoring data, the mechanical circulatory assist device can enable medical staff to judge the effect of the mechanical circulatory assist device on assisting the cardiovascular system when the mechanical circulatory assist device runs with the first parameter setting data, facilitating medical staff to discover problems in a timely manner and adjust the operation setting parameters of the mechanical circulatory assist device in a timely manner, reducing or avoiding adverse effects on the target object, and improving the safety and effectiveness of the mechanical circulatory assist device. By displaying the prediction information on the data change relationship between the operation setting parameters and the cardiovascular parameters, medical staff can view the changes in the cardiovascular parameters when the mechanical circulatory assist device runs with different operation setting parameter data without actually adjusting the operation setting parameters of the mechanical circulatory assist device, facilitating medical staff to select appropriate operation setting parameter data for the mechanical circulatory assist device from it, and judging whether the assisting effect of the current operation setting parameter data is reasonable, reducing the safety risk brought by setting unreasonable operation setting parameter data to the target object. Combining the cardiovascular parameter monitoring data and the data change relationship prediction information, the user can evaluate the rationality of the currently adopted first parameter setting data, thereby improving the accuracy of setting the operation setting parameters. Description of the Drawings

[0103] Figure 1 It is FIG. 1 of the structural schematic diagram of a mechanical circulatory assist device shown according to an embodiment;

[0104] Figure 2 It is FIG. 2 of the structural schematic diagram of a mechanical circulatory assist device shown according to an embodiment;

[0105] Figure 3 It is FIG. 3 of the structural schematic diagram of a mechanical circulatory assist device shown according to an embodiment;

[0106] Figure 4It is a schematic flowchart of a method for monitoring cardiovascular system performance shown according to an embodiment;

[0107] Figure 5 It is a schematic flowchart of a method for monitoring cardiovascular system performance shown according to an embodiment;

[0108] Figure 6 It is a schematic flowchart of a method for monitoring cardiovascular system performance shown according to an embodiment;

[0109] Figure 7 It is a schematic flowchart of a method for monitoring cardiovascular system performance shown according to an embodiment;

[0110] Figure 8 It is a schematic structural diagram of a blood circulation model shown according to an embodiment;

[0111] Figure 9 It is a schematic flowchart of a method for monitoring cardiovascular system performance shown according to an embodiment;

[0112] Figure 10 It is a schematic structural diagram of a blood circulation model shown according to an embodiment;

[0113] Figure 11 It is a schematic flowchart of a method for monitoring cardiovascular system performance shown according to an embodiment;

[0114] Figure 12 It is a schematic flowchart of a method for monitoring cardiovascular system performance shown according to an embodiment;

[0115] Figure 13 It is a schematic diagram of an average arterial pressure data curve shown according to an embodiment.

[0116] Figure 14 It is a schematic diagram of a fitting shown according to an embodiment, Diagram 1.

[0117] Figure 15 It is a schematic diagram of a fitting shown according to an embodiment, Diagram 2.

[0118] Figure 16 It is a schematic diagram of a fitting shown according to an embodiment, Diagram 3.

[0119] Figure 17 It is a schematic flowchart of a method for monitoring cardiovascular system performance shown according to an embodiment;

[0120] Figure 18 It is a schematic flowchart of a method for monitoring cardiovascular system performance shown according to an embodiment;

[0121] Figure 19It is a schematic structural diagram of a mechanical circulatory assist device shown according to an embodiment;

[0122] Figure 20 It is Diagram 1 of a schematic diagram of a cardiac index data curve shown according to an embodiment;

[0123] Figure 21 It is Diagram 2 of a schematic diagram of a cardiac index data curve shown according to an embodiment; Figure 22 It is Diagram 1 of a schematic diagram of a cardiac index data curve and a hemolysis index data curve shown according to an embodiment;

[0124] Figure 23 It is Diagram 2 of a schematic diagram of a cardiac index data curve and a hemolysis index data curve shown according to an embodiment;

[0125] Figure 24 It is Diagram 1 of a schematic diagram of a pressure-volume loop curve shown according to an embodiment;

[0126] Figure 25 It is Diagram 2 of a schematic diagram of a pressure-volume loop curve shown according to an embodiment;

[0127] Figure 26 It is Diagram 3 of a schematic diagram of a cardiac index data curve shown according to an embodiment;

[0128] Figure 27 It is a schematic diagram of a hemolysis index data curve shown according to an embodiment;

[0129] Figure 28 It is a schematic flowchart of a performance test method for a mechanical circulatory assist device shown according to an embodiment;

[0130] Figure 29 It is a schematic flowchart of a method for predicting the in-vivo effect of a mechanical circulatory assist device shown according to an embodiment;

[0131] Figure 30 It is a schematic structural diagram of a monitoring device for cardiovascular system performance shown according to an embodiment. Detailed Embodiments

[0132] To make the technical solutions and beneficial effects of the present invention more obvious and understandable, the following provides a detailed description by way of specific embodiments. Among them, the drawings are not necessarily drawn to scale, and local features may be enlarged or reduced to more clearly show the details of the local features; unless otherwise defined, the technical and scientific terms used herein have the same meanings as those in the technical and scientific fields to which this application belongs.

[0133] The embodiments of the present disclosure are not exhaustive. They are only illustrations of some embodiments and do not constitute specific limitations on the protection scope of the present disclosure. Without contradiction, each step in an embodiment can be implemented as an independent embodiment, and the steps can be combined arbitrarily. For example, the solution after removing some steps in an embodiment can also be implemented as an independent embodiment, and the order of the steps in an embodiment can be exchanged arbitrarily. Additionally, the optional implementation manners in an embodiment can be combined arbitrarily; moreover, the embodiments can be combined arbitrarily. For example, some or all of the steps of different embodiments can be combined arbitrarily, and an embodiment can be combined arbitrarily with the optional implementation manners of other embodiments.

[0134] In each embodiment of the present disclosure, without special instructions and logical conflicts, the terms and / or descriptions among the embodiments are consistent and can be cited from each other. The technical features in different embodiments can be combined to form new embodiments according to their internal logical relationships.

[0135] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended as a limitation on the present disclosure.

[0136] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular form, such as "a", "an", "the", "above", "said", "aforementioned", "this", etc., can mean "one and only one", or can also mean "one or more", "at least one", etc. For example, in the case of using articles such as "a", "an", "the" in English translation, the noun after the article can be understood as a singular expression form or a plural expression form.

[0137] In the embodiments of the present disclosure, "a plurality of" means two or more.

[0138] In some embodiments, terms such as "at least one (at least one, at least one item, at least one)", "one or more", "a plurality of", "multiple", etc. can be replaced with each other.

[0139] In some embodiments, notations such as "at least one of A and B", "A and / or B", "in one case A, in another case B", "one case A, another case B", etc. may, according to the circumstances, include the following technical solutions: In some embodiments, A is performed (A is performed independently of B); in some embodiments, B is performed (B is performed independently of A); in some embodiments, one is selected from A and B for execution (A and B are selectively executed); in some embodiments, both A and B are performed (both A and B are executed). The same is true when there are more branches such as A, B, C, etc.

[0140] In some embodiments, notations such as "A or B" may, according to the circumstances, include the following technical solutions: In some embodiments, A is performed (A is performed independently of B); in some embodiments, B is performed (B is performed independently of A); in some embodiments, one is selected from A and B for execution (A and B are selectively executed). The same is true when there are more branches such as A, B, C, etc.

[0141] Prefix words such as "first", "second", etc. in the embodiments of the present disclosure are only used to distinguish different described objects, and do not impose limitations on the position, order, priority, value, content, etc. of the described objects. For the statements of the described objects, refer to the description in the context of the claims or embodiments. There should be no redundant limitations due to the use of prefix words. For example, if the described object is "field", the ordinal numbers before "field" in "the first field" and "the second field" do not limit the position or order between the "fields", and "first" and "second" do not limit whether the "fields" they modify are in the same message, nor do they limit the order of "the first field" and "the second field". Again, for example, if the described object is "level", the ordinal numbers before "level" in "the first level" and "the second level" do not limit the priority between the "levels". Again, for example, the value of the described object is not limited by the ordinal number and can be one or more. Taking "the first device" as an example, the value of "device" can be one or more. In addition, the objects modified by different prefix words can be the same or different. For example, if the described object is "device", "the first device" and "the second device" can be the same device or different devices, and their types can be the same or different; again, for example, if the described object is "information", "the first information" and "the second information" can be the same information or different information, and their contents can be the same or different.

[0142] In some embodiments, "including A", "containing A", "used to indicate A", "carrying A" can be interpreted as directly carrying A or indirectly indicating A.

[0143] In some embodiments, terms such as "……", "determining...", "in the case of...", "when...", "when...", "if...", "if..." can be mutually replaced.

[0144] In some embodiments, terms such as "greater than", "greater than or equal to", "not less than", "more than", "more than or equal to", "not less than", "higher than", "higher than or equal to", "not lower than", "above", etc. may be interchangeable with each other, and terms such as "less than", "less than or equal to", "not greater than", "less than", "less than or equal to", "not more than", "lower than", "lower than or equal to", "not higher than", "below", etc. may be interchangeable with each other.

[0145] In addition, each element, each row, or each column in the tables of the embodiments of the present disclosure can be implemented as an independent embodiment, and any combination of any element, any row, and any column can also be implemented as an independent embodiment.

[0146] In view of this, the embodiments of the present application provide a method for monitoring the performance of the cardiovascular system to at least solve some or all of the above technical problems.

[0147] The mechanical circulatory assist device may include an in-vivo mechanical circulatory assist device and an extracorporeal mechanical circulatory assist device, etc. The power component of the in-vivo mechanical circulatory assist device, such as the blood pump impeller, is located inside the target object, and the power component of the extracorporeal mechanical circulatory assist device, such as the blood pump impeller, is located outside the target object.

[0148] In some embodiments, an extracorporeal mechanical circulatory assist device 100 (such as an LVAD) as Figure 1 and Figure 2 shown, includes a control device 110, a drive assembly 120, and a power assembly 130.

[0149] The control device 110 can be used for at least one of the following: interacting with a user (such as a medical staff), controlling the rotational speed of the power component through the drive assembly to provide different degrees of circulatory assistance, monitoring the state of the power component, and monitoring the physiological state of the target object. For example, a controller can be provided in the control device 110 to control the rotational speed and / or flow rate of the power component.

[0150] In a possible implementation, the control device 110 is provided with a user interface for interacting with the user. Specifically, the user interface includes a display screen for displaying a graphical user interface. The user interface is used for interacting with the user. For example, the user interface can obtain the input information of the user and display the interaction information to the user. The user interface can interact with the user through means such as sound, light, and electricity.

[0151] The drive assembly 120 may include a motor, and the power assembly 130 may include a blood pump. The blood pump includes a housing and an impeller (not shown). A blood inlet 131 and a blood outlet 132 are provided on the housing. The motor is used to drive the impeller to rotate. The blood pump is used to assist the heart in promoting blood circulation by the rotation of the impeller. The blood inlet 131 is used to connect to a drainage cannula (not shown), for example, connected to the drainage cannula through a pipeline (not shown) to drain blood from the body of the target object. The blood inlet 131 is used to connect to a return cannula (not shown), for example, connected to the return cannula through a pipeline (not shown) to return blood to the body of the target object. The above-mentioned drainage cannula, return cannula, and pipeline may be pipeline components in the extracorporeal mechanical circulation assist device 100.

[0152] In a possible implementation, the motor may be configured as a magnetic levitation motor. The actuator of the magnetic levitation motor may include a rotation actuator and a suspension actuator. The rotation actuator is used to drive the impeller to rotate through magnetic coupling. The suspension actuator is used to suspend the impeller. When the motor is configured as a magnetic levitation motor, the impeller does not physically contact or rub against any other components during operation, thereby reducing mechanical damage to the blood.

[0153] In some embodiments, an in-vivo mechanical circulation assist device 200 is as Figure 3 shown, including a control device 210, a drive assembly 220, and a catheter pump 230. The drive assembly 220 includes a housing and a motor located inside the housing.

[0154] The catheter pump 230 further includes a coupler, a catheter, a drive shaft, a pump head assembly 231, and a flexible support. In use, the drive assembly 220 is generally located outside the subject (the subject may be a human body), and the pump head assembly 231 can be inserted into the body of the target object. The specific installation position may be the left ventricle, for example Figure 3 shown, for assisting the heart in pumping blood to reduce the burden on the heart. The pump head assembly 231 can assist the left ventricle in working and pump the blood in the left ventricle into the aorta. Of course, the pump head assembly 231 can also be inserted into other target positions of the subject as expected through an interventional operation. For example, the pump head assembly 231 is inserted into the right ventricle. The in-vivo mechanical circulation assist device 200 is used to assist the right ventricle in working. Furthermore, the pump head assembly can also be inserted into a blood vessel or other organ.

[0155] The flexible support member is connected to the distal end of the pump head assembly. During the process of inserting the pump head assembly 231 into the installation position within the subject's body, the flexible support member can guide the insertion of components such as the pump housing. After inserting components such as the pump head assembly 231 into the desired position within the human body, during the operation of the in vivo mechanical circulatory assist device, the flexible support member can maintain the posture of the pump head assembly 231 within the heart, thereby not damaging the patient's tissues. In some embodiments, the distal end of the flexible support member is a flexible end, and the flexible end can support on the inner wall of the ventricle in a non-invasive or non-damaging manner, separating the blood inlet of the pump head assembly 231 from the inner wall of the ventricle.

[0156] The above catheter is a hollow structure, and a drive shaft is disposed inside the catheter. The transmission mode between the motor and the drive shaft can adopt magnetic coupling or eddy current coupling. The motor is connected to the proximal ends of the catheter and the drive shaft through a coupler and is configured as a power component to provide power. The coupler can be detachably mounted on the drive assembly 220. Usually, a perfusion port is also provided on the coupler, and external perfusion fluid can be injected into the catheter from the perfusion port to flush or lubricate components such as the bearings in the catheter pump.

[0157] When the coupler engages with the drive assembly 220, the power output end of the motor is coupled to the proximal end of the drive shaft to drive the drive shaft to rotate, and the distal end of the catheter is connected to the pump head assembly 231. The pump head assembly 231 includes a pump housing, a bracket, and an impeller, and the impeller is located inside the pump housing. The above pump housing can be formed by a film, serving as a flexible pipeline assembly to provide a blood flow path.

[0158] A bracket is provided inside the pump housing. The bracket can be a metal lattice made of alloys such as nickel and titanium. The metal lattice has a mesh design to facilitate the expansion and folding of the bracket in the radial direction. The pump housing also includes a film, and the film is mounted on the bracket. The middle part of the bracket is covered by the film to form a fluid channel, and the area of the distal end of the bracket not covered by the film forms a blood inlet. The area of the proximal end of the bracket not covered by the film forms a section of the blood outlet. The bracket also has distal legs extending distally from the blood inlet. The bracket also has proximal legs extending proximally from the blood outlet. The distal legs are fixedly connected to the flexible support member and the distal bearing chamber, and the proximal legs are fixedly connected to components such as the proximal bearing chamber.

[0159] Specifically, the impeller is supported inside the bracket. The impeller includes a hub, and the hub is fixed on the rigid shaft of the drive shaft. Both ends of the rigid shaft are supported inside the pump housing by a proximal bearing and a distal bearing.

[0160] The rigid shaft at the distal end of the drive shaft is connected to the hub of the impeller. Specifically, the drive shaft includes a flexible shaft and a rigid shaft. The proximal end of the flexible shaft is connected to the driven rotor, and the driven rotor is magnetically coupled to the driving rotor connected to the power output shaft in the driving assembly 220. The distal end of the flexible shaft is fixedly connected to the rigid shaft. The flexible shaft is usually disposed inside the catheter, which can prevent the drive shaft from contacting the outside world. On the one hand, it ensures the normal operation of the drive shaft, and on the other hand, it avoids the drive shaft directly contacting the blood vessels of the subject during operation, causing harm to the subject. The hub is connected to the rigid shaft. The two ends of the rigid shaft are rotatably supported at the two ends of the pump housing. The two ends of the pump housing are connected with a proximal bearing chamber and a distal bearing chamber. A proximal bearing is installed inside the proximal bearing chamber, and a distal bearing is installed inside the distal bearing chamber. The rigid shaft is supported by the proximal bearing and the distal bearing. Both the proximal bearing and the distal bearing are made of hard materials, such as ceramics and other materials.

[0161] In the operating state of the catheter pump, the drive shaft passes through the blood vessel from outside the body into the heart, the impeller is inserted into the heart, and the drive shaft is used to drive the impeller to rotate, so as to pump blood from the heart to the blood vessel. Specifically, the motor in the driving assembly 220 rotates, which can drive the drive shaft to rotate. The drive shaft drives the impeller to rotate. The impeller is driven to rotate to suck blood from the blood inlet of the pump housing into the inside of the pump housing, and then pump it out from the blood outlet of the pump housing, thereby realizing the pumping and suction of blood by the pump head assembly 231. The above impeller assembly can be a power assembly in a mechanical circulatory assist device.

[0162] The above mechanical circulatory assist devices are increasingly widely used in heart failure patients, and even gradually change from the last means of saving lives to a conventional treatment option, covering fields such as acute cardiogenic shock, severe left ventricular dysfunction and long-term cardiac support. Mechanical circulatory assist devices can significantly improve the survival rate of patients, but there is still significant room for technological progress in terms of durability, blood and biocompatibility, continuous hemodynamics and cardiac status monitoring. In addition, due to the design differences of mechanical circulatory assist devices, there is still a lack of standardized parameter settings (such as speed and flow rate) for mechanical circulatory assist devices, which limits the optimization effect of individualized treatment. Research points out that through individualized parameter setting adjustment, the risks of LVAD-related thrombosis and hemolysis complications can be reduced. And continuous hemodynamic monitoring helps to customize patient management and can significantly reduce mortality.

[0163] Hemodynamic parameters can include cardiac index (CI), pressure-volume loop (P-V loop) and ventricular elasticity, etc. Monitoring hemodynamic parameters helps to adjust the parameter settings of mechanical circulatory assist devices in real time and optimize the treatment strategy. Hemodynamic parameters can be measured by methods such as thermodilution, bioimpedance, PiCCO monitoring system and echocardiography, but still face challenges such as high invasiveness or insufficient accuracy.

[0164] Hemolysis index is also one of the common complications of mechanical circulatory support devices. Among them, the monitoring of serum lactate dehydrogenase (LDH) is an important basis for clinical diagnosis. However, there is currently a lack of a unified and standardized monitoring protocol and warning data.

[0165] During the operation of mechanical circulatory support devices such as LVADs, in order to achieve better clinical outcomes of mechanical circulatory support devices, standardized monitoring of cardiovascular parameters can be performed on the target subject, and the operating setting parameter data of the mechanical circulatory support device can be adjusted based on the monitoring results. The cardiovascular parameters can include at least one of hemodynamic parameters and blood injury parameters. For example, the cardiovascular parameters monitored during LVAD support can include but are not limited to at least one of the following: blood flow, LVAD blood pump speed, power, pulsatile pressure change, hemodynamic parameters (such as mean arterial pressure (MAP), cardiac output, central venous pressure, etc.) and / or biochemical markers (coagulation function, inflammation index). By detecting cardiovascular parameters and adjusting the operating setting parameter data of the mechanical circulatory support device, the incidence of major complications such as thrombosis, right heart failure, severe bleeding, infection, and cerebrovascular events (such as stroke) can be significantly reduced, thereby reducing the total medical cost. Among the cardiovascular parameters, hemodynamic parameters play a crucial role. The hemodynamic parameters can include at least one of the following: ventricular elastance change, pressure-volume (P-V) loop, and cardiac index (CI). Hemodynamic parameters provide real-time insights into cardiac function, enabling timely adjustment of the operating setting parameter data of MCS to optimize treatment strategies and improve patient prognosis.

[0166] The cardiac index can be obtained using thermodilution, Doppler echocardiography, or impedance cardiogram (ICG), while the P-V loop and ventricular elastance can be obtained through invasive catheterization or estimated by combining echocardiogram measurement with pressure recording. Therefore, the above monitoring has the disadvantages of high invasiveness or suboptimal accuracy.

[0167] The impellers of mechanical circulatory support devices and the components in contact with blood will damage red blood cells in the blood, that is, hemolysis occurs. Cardiovascular parameters also include hemolysis index, etc. Mechanical circulatory support devices such as LVADs will have an impact on blood during operation, such as causing hemolysis. Therefore, continuous hemolysis index monitoring during the use of mechanical circulatory support devices also has clinical benefits. The hemolysis index can also be used to adjust the operating setting parameter data of the mechanical circulatory support device to reduce the incidence of complications. Similarly, there is also a lack of low-invasive or highly accurate detection methods for the hemolysis index.

[0168] Here, before describing the embodiments of the present application, the research content, definitions, etc. that may be involved in the embodiments of the present application are explained.

[0169] To achieve low-invasive, accurate, and real-time hemodynamic assessment, the technical solution provided by the present application simulates the complete human circulatory interaction by establishing a digital twin model. Zero-dimensional lumped parameter models (LPMs), three-dimensional computational fluid dynamics (CFD) models, and neural network methods can be used to simulate human blood circulation. The above models can be developed for different clinical needs respectively.

[0170] With the increase in the amount of available clinical data and the progress of modeling techniques, integrating multiple complex factors into cardiovascular models is also a very promising and worthy of in-depth exploration direction. LPM has the potential to be applied clinically. For example, LPM can simulate heart failure and Fontan circulation with an error of less than 5%. LPM can be used to explore the hemodynamic effects of different ventricular assist device (VAD) and veno-arterial extracorporeal membrane oxygenation (VA-ECMO) connection methods; the cardiopulmonary simulator integrating ECMO simulation is verified by experimental data with an error control within 17.6%. The above data confirm the practical potential and development prospects of LPM as a clinical decision-making tool.

[0171] Nevertheless, LPM still faces several challenges. The parameters of LPM mostly rely on experimental or clinical data for calibration, and there are differences in the parameters used in different studies, which affects the comparability of results; and when clinical data is limited, the difficulty of parameter fitting also increases. In addition, due to the lack of spatial analysis ability of LPM and the inability to simulate the shear stress field, its application in predicting phenomena such as hemolysis is limited. To overcome this limitation, a zero-dimensional LPM can be combined with a three-dimensional model (such as a CFD model or a finite element method (FEM) model), and the internal flow field information of the mechanical circulatory assist device can be introduced to construct a more complete and accurate simulation framework.

[0172] Traditional CFD calculations often take 12 to 24 hours, making it difficult to support real-time clinical decision-making. The embodiments of the present disclosure adopt the Reduced Order Model (ROM) technology to shorten the simulation time, such as reducing it to within 15 minutes. If further integrated with LPM, it can achieve a second-level response and control the accuracy within 1-10%, while retaining the internal flow field information of the mechanical circulatory assist device and the shear stress analysis results. Individualized simulation can accurately fit multiple physiological parameters (such as ventricular elasticity, vascular resistance, and compliance, etc.). Multiple algorithms can be used for parameter fitting optimization, including: Markov Chain Monte Carlo method (MCMC); Genetic Algorithm (GA); Simulated Annealing (SA); and Bayesian Optimization (BO), etc. The above algorithms help to automate the fitting. In addition, sensitivity analysis and parameter subset reduction can also improve the model identifiability and reduce the computational burden. Currently, there is still a lack of a set of tools with low invasiveness that can continuously monitor hemodynamics and hemolysis risk in clinical practice.

[0173] During the operation of mechanical circulatory assist devices such as LVAD, they operate according to the set operating parameters. For example, the blood pump works based on the set rotational speed data. Different operating parameters will affect the cardiovascular parameters of the target object. Such as affecting the blood flow situation and causing damage to the blood, etc. Therefore, the cardiovascular parameters can include at least one of hemodynamic parameter data and blood damage data. Therefore, how to determine the cardiovascular parameter data corresponding to each operating parameter before determining the operating parameters to be adopted for the target object, so as to be able to select reasonable operating parameters, is an urgent problem to be solved.

[0174] In view of this, the present application proposes a digital model framework integrating a lumped parameter model and a reduced order model, which can be individually configured according to different patient states and mechanical circulatory assist device support modes, and combines an automated calibration algorithm to reduce the complexity of parameter setting, realizing fast and accurate hemodynamic simulation, pressure-volume reconstruction, and hemolysis risk prediction. It is expected to optimize the clinical management strategy of MCS through standardized and real-time monitoring, thereby improving the prognosis of patients.

[0175] Example 1

[0176] Please refer to Figure 4 , Figure 4 which shows a method for monitoring the performance of the cardiovascular system provided by the embodiments of the present application. As Figure 4 shown, the method for monitoring the performance of the cardiovascular system may include:

[0177] Step 401: Obtain the cardiovascular model corresponding to the cardiovascular system in the target object and the mechanical circulatory assist model corresponding to the mechanical circulatory assist device.

[0178] The mechanical circulatory assistance model includes a reduced-order model obtained by reducing the order of a computational fluid dynamics model corresponding to a mechanical circulatory assistance device.

[0179] Here, the method for monitoring the cardiovascular system performance can be executed by the control device of the mechanical circulatory assistance device.

[0180] The target object can be a patient with a cardiovascular defect or a cardiovascular disease who needs circulatory assistance through a mechanical circulatory assistance device. The target object can also be an experimental animal or a dummy, etc. For example, the target object can be a patient with a cardiovascular defect, or an experimental animal or a dummy with a cardiovascular defect, etc.

[0181] The cardiovascular model can include an equivalent model of the cardiovascular system of the target object. The model structure of the cardiovascular model matches the cardiovascular structure of the target object. The cardiovascular model can be equivalent to the complete cardiovascular system of the target object, or the cardiovascular model can also be equivalent to a part of the cardiovascular system of the target object.

[0182] In a possible implementation, the cardiovascular model can include multiple model units. Each model unit can be respectively used to simulate one or more organs / tissues in the cardiovascular system of the target object. For example, the cardiovascular model can include a model unit for simulating pulmonary circulation, a model unit for simulating systemic circulation, a model unit for simulating left coronary circulation, a model unit for simulating right coronary circulation, a model unit for simulating the left heart, and a model unit for simulating the right heart.

[0183] In a possible implementation, the cardiovascular model can adopt a lumped parameter model to simulate the interaction between organs, the vascular system, and blood flow.

[0184] Here, the mechanical circulatory assistance device can include an in vivo mechanical circulatory assistance device and an extracorporeal mechanical circulatory assistance device.

[0185] The mechanical circulatory assistance model can be used to simulate the mechanical circulatory assistance device. For example, the mechanical circulatory assistance model can be used to simulate a left ventricular assist device and / or a right ventricular assist device.

[0186] Here, the mechanical circulatory assistance device can include an active part and a passive part. The active part can include components that generate power, such as a blood pump. The passive part can include parts through which blood flows, such as cannulas and tubing. In a possible implementation, the mechanical circulatory assistance model can be used to simulate at least one component in the mechanical circulatory assistance device.

[0187] The mechanical circulatory assist model can be implemented using a reduced - order model (ROM). The reduced - order model is obtained by simplifying a high - dimensional model of a complex system (such as partial differential equations, high - degree - of - freedom systems) through mathematical or physical methods. The reduced - order model can retain the key dynamic characteristics of the high - dimensional model and significantly reduce the computational cost. The reduced - order model can achieve fast simulation, real - time control, and / or parameter optimization while ensuring sufficient accuracy.

[0188] Specifically, based on computational fluid dynamics (CFD), the mechanical circulatory assist device can be analyzed to determine the high - dimensional computational fluid dynamics model of the mechanical circulatory assist device, and then the computational fluid dynamics model can be simplified and reduced in order to obtain a reduced - order model for simulating the mechanical circulatory assist device based on computational fluid dynamics.

[0189] The mechanical circulatory assist device can include components such as a blood pump, cannulas, and / or catheters. All components of the mechanical circulatory assist device can be simulated by one reduced - order model. Multiple components can also be simulated by multiple reduced - order models.

[0190] In a possible implementation, the mechanical circulatory assist model for simulating the mechanical circulatory assist device can include predicting the operating results of the mechanical circulatory assist device by the mechanical circulatory assist model. For example, the reduced - order model can predict the hemolysis index, and / or output flow data, etc. of the mechanical circulatory assist device.

[0191] The reduced - order model can shorten the simulation time of the computational fluid dynamics model from 12 hours to 15 minutes. Therefore, the reduced - order model is more suitable for real - time clinical prediction. The ROM can capture the details of the flow field of the mechanical circulatory assist device. The automatically generated 0D / 1D model can keep the calculation error within 1 - 10%, significantly accelerating the simulation process. At the same time, the reduced - order model also provides analysis data on shear stress and hemolysis risk. Compared with the computational fluid dynamics model, the core of the reduced - order model is to intelligently compress the high - dimensional system through mathematical methods while retaining the key physical characteristics related to the mechanical circulatory assist device. Compared with the computational fluid dynamics model, the computational amount of the reduced - order model can be compressed by 4 - 6 orders of magnitude, only calculating 5% - 10% of the modes that have the greatest impact on the system behavior, thereby improving the computational efficiency of the reduced - order model and shortening the calculation time.

[0192] Step 402: Obtain the drainage position and return position of the mechanical circulatory assist device in the cardiovascular system.

[0193] The mechanical circulatory assist device can be an intracorporeal left ventricular assist device with a working component (such as a blood pump impeller) located inside the target body, or an extracorporeal left ventricular assist device with a working component (such as a blood pump) located outside the target body. The right ventricular assist device can be an intracorporeal right ventricular assist device with a working component (such as a blood pump impeller) located inside the target body, or an extracorporeal right ventricular assist device with a working component (such as a blood pump) located outside the target body.

[0194] The mechanical circulatory assist device introduces blood flow from the drainage position of the target object, pressurizes the blood by the blood pump impeller, and then realizes blood reflux through the reflux position, thereby playing a role in circulatory assistance.

[0195] Exemplarily, for an intracorporeal ventricular assist device, the drainage position can be a ventricle (such as the right ventricle or the left ventricle), and the reflux position can be an artery (such as the pulmonary artery or the aorta). For an extracorporeal ventricular assist device, the drainage position can be an atrium (such as the right atrium or the left atrium), and the reflux position can be an artery (such as the pulmonary artery or the aorta).

[0196] The drainage position and the reflux position can be determined based on the actual connection of the mechanical circulatory assist device to the target object. When in use, the user can input the above-mentioned drainage position and reflux position into the mechanical circulatory assist device.

[0197] Step 403: Couple the cardiovascular model and the mechanical circulatory assist model according to the drainage position and the reflux position to obtain a blood circulation model.

[0198] The blood circulation model is used to simulate the blood flow situation after the mechanical circulatory assist device is connected to the cardiovascular system.

[0199] The cardiovascular model is used to simulate the cardiovascular system. Therefore, the drainage position and the reflux position in the cardiovascular system have corresponding positions in the cardiovascular model.

[0200] The mechanical circulatory assist model is used to simulate the mechanical circulatory assist device, such as a ventricular assist device. Therefore, the reduced model also has a blood input end and a blood output end. The blood input end of the reduced model can be connected to the position corresponding to the drainage position in the cardiovascular model, and the blood output end of the reduced model can be connected to the position corresponding to the reflux position in the cardiovascular model. Thus, the simulation of the connection between the ventricular assist device and the target object is realized.

[0201] Figure 5As shown, after determining the mechanical circulatory assistance model and the cardiovascular model corresponding to the mechanical circulatory assistance device, a blood circulation model for simulating the mechanical circulatory assistance of the target object using the mechanical circulatory assistance device can be formed. Specifically, according to the connection positions of the mechanical circulatory assistance device set on the target object during the actual operation, the coupling positions on the corresponding cardiovascular model can be determined, and the mechanical circulatory assistance model can be connected to the coupling positions on the cardiovascular model.

[0202] The blood circulation model can simulate the blood circulation of the target object assisted by the mechanical circulatory assistance device.

[0203] When the mechanical circulatory assistance model interacts with the cardiovascular model, the prediction data between the models can be directly transmitted. For example, the voltage of the cardiovascular model can be directly output to the mechanical circulatory assistance model as the input of the mechanical circulatory assistance model; the current of the mechanical circulatory assistance model can be directly output to the cardiovascular model. The prediction data between the models can also be transmitted after conversion. For example, the voltage of the cardiovascular model can be converted through a first predetermined conversion relationship to obtain a first conversion value, and the first conversion value can be output to the mechanical circulatory assistance model; the current of the mechanical circulatory assistance model coupled to the second connection point can be converted through a second predetermined conversion relationship to obtain a second conversion value, and the second conversion value can be output to the cardiovascular model.

[0204] Step 404: Obtain the physiological parameter data associated with the cardiovascular system and the operation setting parameter data of the mechanical circulatory assistance device.

[0205] In a possible implementation, the physiological parameter data of the target object can be obtained by monitoring the physiological state of the target object. The physiological parameter data can be the specific values of the physiological parameters. For example, the physiological parameter includes heart rate. The physiological parameter data includes the value of the heart rate: such as 70.

[0206] The physiological parameter data can include: mean arterial pressure data, cardiac output data, blood flow data, atrial pressure data.

[0207] The mechanical circulatory assistance model is used to simulate the mechanical circulatory assistance device. The mechanical circulatory assistance device has its own operation setting parameter data during operation. The mechanical circulatory assistance model can load the operation setting parameter data of the mechanical circulatory assistance device to simulate the mechanical circulatory assistance device, so as to simulate the actual operation state of the mechanical circulatory assistance device. The operation setting parameter data includes the values set for the operation setting parameters.

[0208] In some embodiments, the operation setting parameter data includes at least one of the following: flow rate setting data, rotational speed setting data.

[0209] The user can adjust the mechanical circulatory assistance by setting the flow rate data or rotational speed setting data of the mechanical circulatory assistance device.

[0210] In a possible implementation, the rotational speed setting data includes the target rotational speed set by the user for the circulatory assistance device. It can be understood that the rotational speed setting data can be the target rotational speed of the impeller or the target rotational speed of the motor.

[0211] The flow rate setting data includes the target flow rate set by the user for the circulatory assistance device. It can be understood that the flow rate setting data can be the target support flow rate of the mechanical circulatory assistance device or the total target flow rate for maintaining the whole-body blood circulation of the target object.

[0212] The operating setting parameter data can be input by the user (such as medical staff), for example, selected from multiple operating setting parameter data supported by the mechanical circulatory assistance device. The operating setting parameter data can also adopt the default operating setting parameter data.

[0213] Step 405: Drive the blood circulation model to run based on the physiological parameter data and the operating setting parameter data, and output at least one of the hemodynamic parameter data and blood injury data corresponding to the cardiovascular system.

[0214] The cardiovascular model can be fitted depending on the physiological parameter data so that the cardiovascular model can simulate the hemodynamics of the target object, such as the blood circulation of the target object.

[0215] After obtaining the cardiovascular model and the mechanical circulatory assistance model loaded with the operating setting parameter data, the blood circulation assistance of the mechanical circulatory assistance device to the target object can be simulated according to the blood circulation model combined by the cardiovascular model and the mechanical circulatory assistance model. The intervention of the mechanical circulatory assistance device will affect the hemodynamic parameter data of the target object, and the mechanical circulatory assistance device may cause damage to the blood. In the blood circulation model, the cardiovascular model simulates the cardiovascular system, while the mechanical circulatory assistance model retains the internal flow field information of the mechanical circulatory assistance device. Therefore, the blood circulation assistance of the mechanical circulatory assistance device to the target object is simulated to obtain the hemodynamic parameter data.

[0216] The cardiovascular model can be used for the simulation of the cardiovascular system. For example, the lumped parameter model provides high computational efficiency and practical value in the simulation of the cardiovascular system. However, the lumped parameter model lacks spatial resolution ability and cannot simulate the shear stress field, so its application in the prediction of phenomena such as hemolysis is limited. The mechanical circulatory assistance model can simulate the internal flow field information and shear stress field of the mechanical circulatory assistance device, so as to realize the simulation of the shear stress on the blood red blood cells, realize the prediction of blood damage, and obtain the blood injury data.

[0217] The hemodynamic parameter data can be data values for hemodynamic parameters.

[0218] In a possible implementation, the blood damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombus index data.

[0219] In some embodiments, the hemodynamic parameter data includes cardiac performance parameter data, and the cardiac performance parameter data includes at least one of the following: native cardiac index data, total cardiac index data, pressure-volume loop data, and ventricular elastance data.

[0220] The hemodynamic parameter data can be specific numerical values of hemodynamic parameters. The cardiac performance parameter data can be specific numerical values characterizing cardiac performance parameters.

[0221] In a possible implementation, the native cardiac index can be determined based on the cardiac output of the native heart. Specifically, the native cardiac index can include the quotient of the native cardiac output divided by the body surface area.

[0222] In a possible implementation, the total cardiac index can be determined based on the total cardiac output data. Specifically, the total cardiac index can be the quotient of the total cardiac output data divided by the body surface area. The cardiac output of the native heart combined with the assisted blood output (flow data) of the mechanical circulatory assist device can obtain the total cardiac output data when the mechanical circulatory assist device assists the target subject.

[0223] In a possible implementation, the blood circulation model can be set in a processing device outside the mechanical circulatory assist device and run in the processing device outside the mechanical circulatory assist device. It can be understood that the mechanical circulatory assist device and the processing device outside the mechanical circulatory assist device can also be collectively referred to as the mechanical circulatory assist device during the prediction and display process of at least one of the hemodynamic parameter data and the blood damage data as an interconnected system.

[0224] In the technical solution provided in this application, the cardiovascular model is used to simulate the blood circulation of the cardiovascular system in the target subject, and the mechanical circulatory assist model is used to simulate the flow field changes of the mechanical circulatory assist device. By obtaining the connection position of the target subject connected to the mechanical circulatory assist device and coupling the mechanical circulatory assist model with the cardiovascular model to obtain the blood circulation model, the blood circulation situation after the target subject is connected to the mechanical circulatory assist device can be simulated. After obtaining the physiological parameter data of the target subject and the operation setting parameter data of the mechanical circulatory assist device, the blood circulation model can be driven to run based on the physiological parameter data and the operation setting parameter data, so as to achieve individualized configuration according to different patient states and the support mode of the mechanical circulatory assist model.

[0225] Since the above mechanical circulatory assistance model includes a reduced-order model obtained by reducing the order of the computational fluid dynamics model corresponding to the mechanical circulatory assistance device, on the one hand, the reduced-order model can effectively retain the internal flow field information and shear stress analysis results of the computational fluid dynamics model. By introducing the coupling of the reduced-order model and the cardiovascular model, not only can the blood circulation of the target object be simulated, but also the blood damage caused by the operation of the mechanical circulatory assistance device can be simulated. Furthermore, the hemodynamic parameter data and / or blood damage data of the current object under the current support mode can be accurately output, so that medical staff can observe the blood circulation and / or blood damage of the target object without performing invasive examinations and reduce the harm to the target object.

[0226] The introduction of the reduced-order model also reduces the number of model parameters and computational complexity of the mechanical circulatory assistance model. Compared with the computational fluid dynamics model that requires 12 to 24 hours of computational time, the reduced-order model can shorten the simulation time to within 15 minutes. Coupling it with the cardiovascular model can achieve a second-level response and maintain high accuracy, and then can continuously and real-time output the above hemodynamic parameter data and / or blood damage data, so that medical staff can observe the blood circulation and / or blood damage of the target object in real time.

[0227] After the construction of the blood circulation model is completed, the cardiovascular model and the mechanical circulatory assistance model are integrated together, and the interaction between them can be linked through pressure-flow coupling. The specific way for the cardiovascular model and the mechanical circulatory assistance model to interact is as follows.

[0228] In some embodiments, as Figure 6 shown, step 405 may include:

[0229] Step 601: Drive the cardiovascular model to run according to the physiological parameter data, and output the pressure gradient data between the drainage position and the return position.

[0230] Step 602: Input the pressure gradient data and the operation setting parameter data into the mechanical circulatory assistance model to drive the blood circulation model to run, and output at least one of the hemodynamic parameter data and blood damage data corresponding to the cardiovascular system.

[0231] The cardiovascular model can run based on the physiological parameter data to simulate the real hemodynamics of the target object, and then the pressure gradient data between the drainage position and the return position can be obtained.

[0232] Here, after the cardiovascular model is connected to the mechanical circulatory assistance model, the interaction between the cardiovascular model and the mechanical circulatory assistance model can include the coupling of pressure gradient data. The blood pressure difference between the drainage position and the return position of the target object will affect the flow field in the mechanical circulatory assistance device. The mechanical circulatory assistance model can simulate the operation of the mechanical circulatory assistance device based on the operating setting parameter data and the pressure gradient data output by the cardiovascular model. The mechanical circulatory assistance model can simulate the assistance process of the mechanical circulatory assistance device and output the blood flow rate data to the cardiovascular model, so as to simulate the mechanical circulatory assistance device assisting the target object in blood circulation, and further predict at least one of the hemodynamic parameter data and the blood injury data.

[0233] The cardiovascular model simulates the target object through physiological parameter data. The mechanical circulatory assistance model simulates the actual working conditions of the mechanical circulatory assistance device based on the pressure gradient data determined by the cardiovascular model and the operating setting parameter data of the mechanical circulatory assistance device. The obtained blood circulation model can accurately simulate the situation of the mechanical circulatory assistance device assisting the target object in blood circulation, realize individualized configuration, and further improve the accuracy of predicting the hemodynamic parameter data and / or blood injury data of the target object under the mechanical circulatory assistance device. At the same time, it can reduce the situation of determining the hemolysis index and the auxiliary output blood flow rate through invasive detection methods, and reduce the harm to the target object.

[0234] After the cardiovascular model outputs the pressure gradient data, the specific working process of the blood circulation model is as follows. In some embodiments, as Figure 7 shown, step 602 may include:

[0235] Step 701: Input the pressure gradient data and the operating setting parameter data into the mechanical circulatory assistance model to drive the blood circulation model to run and output the flow rate data and blood injury data of the mechanical circulatory assistance model.

[0236] Step 702: Input the flow rate data into the cardiovascular model to update the operating state of the cardiovascular model and output the hemodynamic parameter data.

[0237] The pressure gradient data can be the blood pressure difference between the drainage position and the return position. The pressure gradient data can be obtained by a cardiovascular model simulating the target object. The mechanical circulatory assistance model simulates the mechanical circulatory assistance device based on the pressure gradient data and the operation setting parameter data (such as the blood pump speed) of the mechanical circulatory assistance model. The mechanical circulatory assistance device has two important indicators in the process of blood circulation assistance: flow rate data and blood damage data. The flow rate data is used to indicate the blood output of the simulated mechanical circulatory assistance device. Therefore, the mechanical circulatory assistance model can simulate the mechanical circulatory assistance device and output the flow rate data and blood damage data. The flow rate data is the blood output of the mechanical circulatory assistance device, that is, the blood return flow rate returning to the return position. Therefore, the flow rate data can be input into the cardiovascular model, and the cardiovascular model can simulate the blood flow rate input by the mechanical circulatory assistance device at the return position based on the flow rate data, thus realizing the coupling between the cardiovascular model and the mechanical circulatory assistance model.

[0238] Based on the updated flow rate data, the cardiovascular model can simulate the hemodynamics of the cardiovascular system, thereby realizing the update of the hemodynamic parameter data.

[0239] Connecting the cardiovascular model and the mechanical circulatory assistance model according to the actual connection method between the mechanical circulatory assistance device and the target object can accurately simulate the condition of the mechanical circulatory assistance device assisting the target object in blood circulation. The mechanical circulatory assistance model realizes the accurate simulation of the actual work of the mechanical circulatory assistance device based on the pressure gradient data between the drainage position and the return position and the operation setting parameter data, thereby improving the accuracy of predicting the hemodynamic parameter data and blood damage data of the target object under the mechanical circulatory assistance device. The mechanical circulatory assistance model simulates the mechanical circulatory assistance device and can predict the flow rate data and blood damage data of the mechanical circulatory assistance device, which can reduce the situation of determining the hemolysis index and the auxiliary output blood flow rate through invasive detection methods and reduce the harm to the target object.

[0240] The mechanical circulatory assistance device can be composed of multiple components, and its internal working process is as follows.

[0241] In some embodiments, the mechanical circulatory assistance device includes a power component and a first pipeline component, and the mechanical circulatory assistance model includes a blood pump reduced-order model corresponding to the power component and a pipeline reduced-order model corresponding to the first pipeline component; the pipeline reduced-order model is used to determine the first pressure loss data at both ends of the first pipeline component based on the flow rate data.

[0242] Step 702 may include:

[0243] Determine the pump head pressure difference data corresponding to the power component based on the pressure gradient data and the first pressure loss data.

[0244] Input the pump head pressure difference data and the operating setting parameter data into the blood pump reduced-order model to drive the operation of the blood circulation model, and output the flow rate data and the first blood damage data corresponding to the blood pump reduced-order model. The first blood damage data is used to characterize the blood damage caused by the power component.

[0245] Here, the power component can be a component that pressurizes the blood flow introduced from the drainage position. For example, the power component can include a blood pump.

[0246] During the operation of the mechanical circulatory assist device, blood is introduced from the drainage position of the target object, pressurized by the power component, and then the blood flows back from the return position. Therefore, the mechanical circulatory assist device can include a power component and a first pipeline component for introducing blood and / or returning blood.

[0247] Here, a blood pump reduced-order model and a pipeline reduced-order model can be respectively set for the components involved in blood circulation in the mechanical circulatory assist device that may cause hemolysis, such as the power component and the first pipeline component. This can improve the authenticity of simulating the mechanical circulatory assist device. The power component is used to pressurize the blood, so the blood pump generates changes in blood flow rate. At the same time, the mechanical movement of the blood pump will also cause damage to the blood. The blood flowing through the first pipeline component will cause pressure loss.

[0248] Exemplarily, a computational fluid dynamics numerical simulation study can be carried out on the blood pump and the first pipeline component. Based on the k-ω SST turbulence model, a steady-state flow analysis is carried out, and a non-Newtonian fluid model is used to characterize the blood rheological properties, and then the blood pump reduced-order model and the pipeline reduced-order model are determined.

[0249] The blood pump reduced-order model is obtained by reducing the order of the model obtained from the computational fluid dynamics analysis of the blood pump. The pipeline reduced-order model is obtained by reducing the order of the model obtained from the computational fluid dynamics analysis of the first pipeline component.

[0250] The blood pump reduced-order model simulates the flow of all the blood in the mechanical circulatory assist device driven by the blood pump. The pump head pressure difference data borne by the blood pump can be determined based on the pressure gradient data and the first pressure loss data.

[0251] Specifically, the pump head pressure difference data can be the sum of the pressure gradient data and the first pressure loss data. The blood pump reduced-order model uses the pump head pressure difference data as an input quantity and the operating setting parameter data (such as the blood pump speed) as the second input quantity to simulate the blood pump. The blood pump reduced-order model can calculate the flow rate data and the first blood damage data. Among them, the flow rate data of the blood pump is also the flow rate data of the mechanical circulatory assist device. By determining the first pressure loss data, the true pump head pressure difference data at both ends of the power component can be determined more accurately, and the prediction accuracy of the blood pump reduced-order model can be improved.

[0252] The pipeline reduced-order model is used to simulate the first pressure loss data generated when blood flows through the first pipeline component.

[0253] In this application, in order to effectively predict the hemolysis risk under the cardiovascular model, a reduced-order model based on computational fluid dynamics (CFD) simulation is established and imported. The analysis objects of the simulation include the pump body (such as the power component) and the pipeline (such as the first pipeline component). The CFD simulation adopts the steady-state condition and the k–ω SST turbulence model, and the blood rheological characteristics can be constructed according to the non-Newtonian model. The geometry of the pump body of the blood pump is established from CAD data. For example, an extracorporeal left ventricular assist device (LVAD) can be used as the blood pump used in the research, and spatial discretization is performed with unstructured tetrahedral meshes and up to 12 layers of wall-adjacent prism elements. The total number of elements is approximately 17.5 million, and the average y + value of all surfaces is less than 1. To simulate the interaction between the rotor and the stator, the moving reference frame is adopted. A total of 30 operating conditions are simulated, covering the flow rate setting data Q = [0.5–5] L / min and the rotational speed setting data n = [1500–3000] rpm, using the Latin hypercube sampling method.

[0254] The pipeline simulation covers four sizes (19, 23, 28, 32 Fr), and the flow rate range is also 0.5 L / min to 5 L / min. The pipeline geometry adopts a simplified blood vessel structure, including catheter tip and side hole configurations. The spatial discretization adopts unstructured tetrahedral meshes combined with near-wall prism refinement. The total number of elements is approximately 1.18 million to 2.2 million depending on the catheter size.

[0255] The hemolysis index (Modified Index of Hemolysis, MIH) is numerically predicted using the Euler method. This method calculates the blood injury source term through a linearized transport equation and integrates the entire computational domain. The hemolysis model corresponds the exposure time and shear stress to the experimental data in a power-law relationship, using the parameters: C = 1.745×10 -6 , α = 1.963, β = 0.7762.

[0256] The hemolysis reduction model of the pump body part (blood pump reduction model) is established by using the non-invasive Polynomial Chaos Expansion (PCE) method, which can provide continuous prediction within the range of training data. The prediction of the pressure drop and hemolysis index of the pipeline is constructed through a combined polynomial method, which can effectively describe the discrete changes brought by different catheter sizes. The ROM output includes MIH (hemolysis index), PD (pressure drop), Q (flow data), and D (pipeline size, Fr), and describes the dependence of flow and size on the prediction index through fitting parameters. The blood pump reduction model and the pipeline reduction model that make up the mechanical circulatory assistance model simulate the power component and the first pipeline component in the mechanical circulatory assistance device respectively, so as to realize the simulation of the output parameters of each component in the mechanical circulatory assistance device. On the one hand, it improves the authenticity of the mechanical circulatory assistance model in simulating the mechanical circulatory assistance device, and further improves the accuracy of each parameter output by the reduction model. On the other hand, the use of the blood pump reduction model and the pipeline reduction model can improve the efficiency of the model and shorten the prediction time while ensuring the prediction accuracy.

[0257] In some embodiments, the method for monitoring the performance of the cardiovascular system further includes: obtaining first size information corresponding to the first pipeline component; fitting the pipeline reduction model based on the first size information.

[0258] Specifically, the size of the first pipeline component will affect the pressure drop of the blood flowing through the first pipeline component. The size of the first pipeline component is also related to the damage caused by the first pipeline component to the blood.

[0259] Here, the first size information can be determined based on the first pipeline component actually used in the mechanical circulatory assistance device.

[0260] In a possible implementation, the first size information is used to indicate at least one of the following of the first pipeline component: the pipe diameter of the first pipeline component, the length of the first pipeline component, and the cross-sectional shape of the first pipeline component.

[0261] The pipeline reduction model can determine the first pressure loss data and the damage to the blood in combination with the first size information.

[0262] By introducing the first size information, the pipeline reduction model can more accurately simulate the first pipeline component and improve the accuracy of the prediction of the pipeline reduction model.

[0263] In some embodiments, step 701 may include:

[0264] Input the flow data into the pipeline reduction model for processing, and output the first pressure loss data and the second blood damage data corresponding to the first pipeline component. The second blood damage data is used to characterize the blood damage caused by the first pipeline component.

[0265] Specifically, the size of the first pipeline component affects the pressure drop of the blood flowing through the first pipeline component. The size of the first pipeline component is also related to the damage to the blood caused by the first pipeline component. Therefore, a pipeline degradation model can be used to predict the first pressure loss data and the second blood damage data.

[0266] Specifically, based on the flow rate data and the first size information, the pipeline degradation model can simulate the first pressure loss data generated when the blood flows through the first pipeline component and the second blood damage data of the first pipeline component.

[0267] By introducing the first size information, the pipeline degradation model can more accurately simulate the first pipeline component, improving the accuracy of the pipeline degradation model in predicting the first pressure loss data and the second blood damage data.

[0268] The damage to the blood by the mechanical circulatory assist device includes the damage to the blood of all components within the mechanical circulatory assist device. Therefore, the following method is used to determine the total blood damage data caused by the mechanical circulatory assist device to the blood.

[0269] In some embodiments, step 701 may include:

[0270] Performing a fusion process on the first blood damage data and the second blood damage data to obtain the total blood damage data.

[0271] Multiple components in the mechanical circulatory assist device may cause blood damage. For example, the power component and the first pipeline component may cause hemolysis. Therefore, the total blood damage data of the mechanical circulatory assist device can be obtained based on the blood damage data of multiple components in the mechanical circulatory assist device. By combining the first blood damage data and the second blood damage data, the total number of blood damages of each component of the mechanical circulatory assist device can be truly simulated, thereby improving the accuracy of the determined total blood damage data.

[0272] In some embodiments, the mechanical circulatory assist device further includes a second pipeline component, and the mechanical circulatory assist model further includes a linear pipeline model corresponding to the second pipeline component; the method further includes: obtaining second size information corresponding to the second pipeline component; fitting the linear pipeline model based on the second size information; inputting the flow rate data into the linear pipeline model for processing, and outputting the second pressure loss data at both ends of the second pipeline component; determining the pump head pressure difference data corresponding to the power component based on the pressure gradient data, the first pressure loss data, and the second pressure loss data.

[0273] Here, a second pipeline component may be further provided between the power component and the first pipeline component to facilitate adjusting the distance between the power component and the target object.

[0274] During the operation of the mechanical circulatory assist device, blood needs to flow between the second pipeline components. Therefore, pressure loss will also occur.

[0275] Here, a linear pipeline model can be set for the second pipeline component respectively, so as to improve the authenticity of simulating the mechanical circulatory assist device. The linear pipeline model can be used to simulate the pressure loss generated by the second pipeline component.

[0276] Exemplarily, the linear pipeline model can be modeled based on the Darcy - Weisbach formula, and the Darcy - Weisbach formula describes the quadratic relationship between the flow pressure drop, the flow velocity (i.e., the assisted output blood flow), and the pipeline geometric dimensions.

[0277] Specifically, the size of the second pipeline component will affect the pressure drop of the blood flowing through the second pipeline component. Here, the second size information can be determined based on the second pipeline component actually adopted by the mechanical circulatory assist device.

[0278] In a possible implementation manner, the second size information is used to indicate at least one of the following items of the second pipeline component: the pipe diameter of the second pipeline component, the length of the second pipeline component, and the cross - sectional shape of the second pipeline component.

[0279] Specifically, the size of the second pipeline component will affect the pressure drop of the blood flowing through the second pipeline component. Therefore, the linear pipeline model can be used to predict the second pressure loss data.

[0280] Specifically, the linear pipeline model can simulate the second pressure loss data generated when the blood flow passes through the second pipeline component based on the flow data and the second size information.

[0281] By introducing the second size information, the linear pipeline model can more accurately simulate the second pipeline component, improving the accuracy of the pipeline reduced - order model in predicting the second pressure loss data.

[0282] Specifically, the pump head differential pressure data can be the sum of the pressure gradient data, the first pressure loss data, and the second pressure loss data. The blood pump reduced-order model uses the pump head differential pressure data as an input quantity and the operating setting parameter data (such as the blood pump rotation speed) as the second input quantity to simulate the blood pump. The blood pump reduced-order model can calculate the flow rate data and the first blood damage data. Among them, the flow rate data of the blood pump is also the flow rate data of the mechanical circulatory assist device. The pipeline reduced-order model is used to simulate the first pressure loss data generated when the blood flow passes through the first pipeline component, and the linear pipeline model is used to simulate the first pressure loss data generated when the blood flow passes through the second pipeline component. By determining the second pressure loss data and combining the first pressure loss data, the pressure drop loss caused by the pipeline component in the mechanical circulatory assist device can be determined, so as to more accurately determine the true pump head differential pressure data at both ends of the power component in combination with the pressure gradient data, and improve the prediction accuracy of the blood pump reduced-order model.

[0283] The blood pump reduced-order model and the pipeline reduced-order model that make up the mechanical circulatory assist model respectively simulate the power component, the first pipeline component, and the second pipeline component in the mechanical circulatory assist device to achieve the simulation of the output parameters of each component in the mechanical circulatory assist device. Improve the authenticity of the mechanical circulatory assist model in simulating the mechanical circulatory assist device, and then improve the accuracy of each parameter output by the reduced-order model.

[0284] In addition, for the second pipeline component, the hemolytic damage it causes to the blood in the blood circulation is relatively small, but the pressure drop loss at both ends needs to be considered. Therefore, using a linear pipeline model instead of a reduced-order model can predict the pressure drop loss, effectively reduce the model calculation complexity, and improve the real-time performance of data prediction.

[0285] In some embodiments, the above-mentioned mechanical circulatory assist device includes an extracorporeal ventricular assist device. The first pipeline component in the extracorporeal ventricular assist device includes: a drainage cannula and a return cannula; the second pipeline component includes a first pipeline and a second pipeline; wherein, the input end of the drainage cannula is used to connect to the drainage position, the output end of the drainage cannula is used to connect to the input end of the first pipeline, the output end of the first pipeline is used to connect to the input end of the power component, the output end of the power component is used to connect to the input end of the second pipeline, the output end of the second pipeline is used to connect to the input end of the return cannula, and the output end of the return cannula is used to connect to the return position.

[0286] Such as Figure 8As shown, the pipeline reduced-order model includes: a reduced-order model of the drainage cannula corresponding to the drainage cannula, and a reduced-order model of the reflux cannula corresponding to the reflux cannula; the reduced-order model of the drainage cannula is used to determine the pressure loss data at both ends of the drainage cannula based on the flow rate data; the reduced-order model of the reflux cannula is used to determine the pressure loss data at both ends of the reflux cannula based on the flow rate data; the first pressure loss data is determined based on the pressure loss data at both ends of the drainage cannula and the pressure loss data at both ends of the reflux cannula.

[0287] The linear pipeline model includes: a first linear pipeline model corresponding to the first pipeline, and a second linear pipeline model corresponding to the second pipeline; the first linear pipeline model is used to determine the pressure loss data at both ends of the first pipeline based on the flow rate data; the second linear pipeline model is used to determine the pressure loss data at both ends of the second pipeline based on the flow rate data; the second pressure loss data is determined based on the pressure loss data at both ends of the first pipeline and the pressure loss data at both ends of the second pipeline.

[0288] In some embodiments, the reduced-order model of the drainage cannula is used to determine the blood injury data of the drainage cannula based on the flow rate data; the reduced-order model of the reflux cannula is used to determine the blood injury data of the reflux cannula based on the flow rate data; the blood injury data of the drainage cannula and the reflux cannula are used to determine the second blood injury data corresponding to the first pipeline component.

[0289] The reduced-order model of the drainage cannula is obtained by reducing the order of the model obtained by performing computational fluid dynamics analysis on the drainage cannula. The reduced-order model of the drainage cannula can simulate the pressure loss data generated when blood flows through the drainage cannula and the blood injury data generated by the drainage cannula based on the flow rate data flowing through the drainage cannula and the size information of the drainage cannula.

[0290] The reduced-order model of the reflux cannula is obtained by reducing the order of the model obtained by performing computational fluid dynamics analysis on the reflux cannula. The reduced-order model of the reflux cannula can simulate the pressure loss data generated when blood flows through the reflux cannula and the blood injury data generated by the reflux cannula based on the flow rate data flowing through the reflux cannula and the size information of the reflux cannula.

[0291] The sum of the pressure loss data generated by the drainage cannula and the pressure loss data generated by the reflux cannula is the first pressure loss data. The sum of the blood injury data generated by the drainage cannula and the blood injury data generated by the reflux cannula is the second pressure loss data.

[0292] The first linear pipeline model and the second linear pipeline model can be obtained by modeling respectively based on the hydrodynamics of the first pipeline and the second pipeline. The first linear pipeline model can simulate the pressure loss data generated by blood flow through the first pipeline based on the flow rate data of the first pipeline and the dimension information of the first pipeline. Similarly, the second linear pipeline model can simulate the pressure loss data generated by blood flow through the second pipeline based on the flow rate data of the second pipeline and the dimension information of the second pipeline.

[0293] The sum of the pressure loss data generated by the first pipeline and the pressure loss data generated by the second pipeline is the second pressure loss data.

[0294] In a possible implementation, the first linear pipeline model and the second linear pipeline model can also predict the blood damage data of the first pipeline and the blood damage data of the second pipeline respectively.

[0295] Here, a specific example is proposed in combination with the above embodiments:

[0296] In the embodiments of the present application, the cardiovascular model and the mechanical circulatory assist model are integrated together, and the interaction between them is linked through pressure-flow coupling. The total effective pressure difference required for the mechanical circulatory assist device (i.e., the pump head pressure difference data) is defined as the sum of the following items: the pressure gradient data between the drainage position and the return position in the native cardiovascular system (e.g., from the left atrium / left ventricle to the aorta); the pressure loss caused by the cannula, including: ΔPdrain (the pressure loss data generated by the drainage cannula) and ΔPreturn (the pressure loss data generated by the return cannula); and the pressure loss ΔPtubing (the second pressure loss data) of the pipeline in the mechanical circulatory assist device loop. The mechanical circulatory assist model of the mechanical circulatory assist device takes the total effective pressure difference and the blood pump speed (RPM) as inputs, calculates the corresponding pump flow rate (Q_pump) (i.e., the flow rate data) and the mechanical hemolysis index (MIH), that is, the total blood damage data, and feeds the results back to the cardiovascular model to update the systemic hemodynamic state. The models of the cannula and the pipeline (i.e., the pipeline reduced-order model and the linear pipeline model) are also integrated into the overall model to consider their influence on the hemodynamics.

[0297] The drainage cannula (inflow) and the return cannula (outflow) are modeled with the CFD-derived reduced-order model (the drainage cannula reduced-order model and the return cannula reduced-order model are obtained), and the pump flow (Q_pump) and the catheter diameter are mapped to the pressure loss (ΔP_drain, ΔP_return). The pipeline part connecting the mechanical circulation auxiliary assembly (the second pipeline assembly) is modeled with the Darcy-Weisbach equation to describe the quadratic relationship between the pressure loss (ΔP_tubing), the flow (Q) and the pipeline geometry. The quadratic relationship can be expressed as follows:

[0298]

[0299] Where f is the friction factor, D is the fixed inner diameter of the tube (such as 3 / 8 inch), L is the user-defined tube length, ρ is the blood density, and Q represents the flow rate. This analytical method takes into account both computational efficiency and physical accuracy without the need for high-dimensional simulation, and can effectively capture the nonlinear resistance effect. Here, combined with Figure 8 , the specific workflows of the blood pump reduced-order model, the drainage cannula reduced-order model, the reflux cannula reduced-order model, the first linear pipeline model and the second linear pipeline model are explained.

[0300] During the initial period of the cardiovascular model and the mechanical circulatory assist model, the blood pump was not started, and the pressure drop losses ΔPdrain (pressure loss data generated by the drainage cannula), ΔPreturn (pressure loss data generated by the reflux cannula), and ΔPtubing (second pressure loss data) on the drainage cannula, reflux cannula, the first linear pipeline model, and the second linear pipeline model can be regarded as zero.

[0301] The blood pump reduced-order model can use the pressure gradient data between the drainage position and the reflux position as the initial pressure difference input (i.e., the pump head pressure difference data), and when the blood pump is started, obtain the current rotation speed, output the flow data based on the rotation speed and the pump head pressure difference data, and calculate the hemolytic index of the blood pump reduced-order model (blood pump).

[0302] After the blood pump reduction model outputs the flow data, the drainage cannula reduction model, the reflux cannula reduction model, the first linear pipeline model and the second linear pipeline model output their corresponding pressure drops according to the flow data: ΔPdrain (pressure loss data generated by the drainage cannula), ΔPreturn (pressure loss data generated by the reflux cannula), ΔPtubing (second pressure loss data) and hemolysis index.

[0303] By combining the pressure gradient data between the drainage position and the return position with ΔPdrain, ΔPreturn, and ΔPtubing, the true pressure difference (i.e., the pump head pressure difference data) at both ends of the blood pump reduced-order model (i.e., the blood pump inlet and the blood pump outlet) can be obtained. Then, using the true pressure difference (i.e., the pump head pressure difference data) as the input of the blood pump reduced-order model, the real-time flow rate data is output in combination with the current rotational speed, and the hemolysis index of the blood pump reduced-order model is calculated.

[0304] The blood pump reduced-order model, the drainage cannula reduced-order model, the return cannula reduced-order model, the first linear pipeline model, and the second linear pipeline model that make up the mechanical circulatory assistance model respectively simulate each component in the mechanical circulatory assistance device: the blood pump, the drainage cannula, the return cannula, the first pipeline, and the second pipeline, so as to realize the simulation of the output parameters of each component in the mechanical circulatory assistance device. The authenticity of the mechanical circulatory assistance model in simulating the mechanical circulatory assistance device is improved, and further the accuracy of each parameter output by the reduced-order model is improved.

[0305] The cardiovascular model can be implemented using various models, such as machine learning models, etc. The cardiovascular model provided in this embodiment is as follows.

[0306] In some embodiments, such as Figure 9 and Figure 10 shown, the cardiovascular model is a lumped parameter model. The circuit structure in the lumped parameter model is used to characterize the cardiovascular system. The lumped parameter model includes a charge-discharge circuit corresponding to the heart in the cardiovascular system. The lumped parameter model and the mechanical circulatory assistance model are connected through pressure-flow coupling.

[0307] The flow rate data is input into the cardiovascular model to update the operating state of the cardiovascular model and output hemodynamic parameter data, including: inputting the flow rate data into the lumped parameter model to update the current change in the circuit structure; obtaining the electrical signal of the charge-discharge circuit; and determining the heart parameter data based on the electrical signal of the charge-discharge circuit.

[0308] The lumped parameter model (Lumped Parameter Model, LPM) is a simplified mathematical model. The lumped parameter model can describe the overall characteristics of the blood circulation of the target object through lumped parameters (such as equivalent resistance, capacitance, inductance, etc.).

[0309] The lumped parameter model can simulate the blood circulation of a target object (such as a target human body). The lumped parameter model can represent the cardiovascular system of the target object as a simplified equivalent circuit. The equivalent circuit can include resistors, capacitors, inductors, etc. Voltage can correspond to the blood pressure of the target object, current can correspond to the blood flow rate of the target object (blood flow volume per unit time), resistance can correspond to the blood flow resistance in the blood vessels of the target object, capacitance can correspond to the vascular compliance of the target object (the ability of blood vessels to dilate and store blood) and / or cardiac compliance (the ability of the heart to store blood), etc., and inductance can correspond to the blood flow inertia of the target object.

[0310] In one possible implementation, a 0-dimensional lumped parameter model can be used to simulate the blood circulation of the target object. The 0-dimensional lumped parameter model can ignore the spatial distribution characteristics of blood circulation and only consider the dynamic behavior of blood circulation changing over time.

[0311] In this embodiment, the lumped parameter model includes a simplified zero-dimensional (0D) mathematical model, and the lumped parameter model is established based on the principle of the Windkessel theory. This theory uses circuit analysis methods to describe hemodynamic behavior through a set of partial differential equations. The lumped parameter model can be implemented through simulation software to provide a dynamic simulation operating environment. As Figure 9 and Figure 10 shown, the equivalent circuit of the lumped parameter model can include six interconnected regions: pulmonary circulation, systemic circulation, left coronary circulation, right coronary circulation, left heart, and right heart. The lumped parameter model and the mechanical circulatory assist model are connected through pressure-flow coupling, that is, the lumped parameter model provides pressure gradient data to the mechanical circulatory assist model, and the mechanical circulatory assist model predicts flow rate data based on the pressure gradient data and feeds it back to the lumped parameter model to simulate the hemodynamics of the cardiovascular system of the target object under the assistance of the mechanical circulatory assist device.

[0312] Exemplarily, as Figure 9 and Figure 10 exemplarily shows the equivalent circuit of the lumped parameter model of blood circulation. The heart can be equivalent to a charging and discharging circuit. The current and voltage changes in the charging and discharging circuit are used to simulate the hemodynamics of the heart. For example, in the equivalent circuit, the variable capacitor ELA is used to simulate the left atrium, the diode MV is used to simulate the mitral valve between the left atrium and the left ventricle (used to maintain unidirectional blood flow), the variable capacitor ELV is used to simulate the left ventricle, and the diode MV is used to simulate the aortic valve between the left ventricle and the aorta (used to maintain unidirectional blood flow). The lumped parameter model can determine cardiac parameter data based on the electrical signals in the equivalent circuit of the lumped parameter model.

[0313] In some embodiments, the charging and discharging circuit includes a variable capacitor for simulating the ventricle;

[0314] Based on the electrical signals of the charge and discharge circuit, determine cardiac parameter data, including at least one of the following: determine native cardiac index data based on the current data of the variable capacitor; determine the pressure change data of the ventricle based on the voltage change data of the variable capacitor; determine the volume change data of the ventricle based on the charge change data of the variable capacitor; determine the total cardiac index data according to the native cardiac output data and the flow data; determine at least one of the pressure-volume loop data and the ventricular elastance data based on the pressure change data of the ventricle and the volume change data of the ventricle.

[0315] Specifically, the mechanical circulatory assist model can feedback the auxiliary output flow data obtained by simulating the mechanical circulatory assist device to the equivalent circuit of the lumped parameter model, which is equivalent to the mechanical circulatory assist device performing blood circulation assistance on the target object. The prediction result obtained by the lumped parameter model simulating the target object is equivalent to the cardiac parameter data when the target object undergoes blood circulation assistance.

[0316] As Figure 9 and Figure 10 shown, the equivalent circuit model can include multiple model units. Each model unit can be respectively used to simulate one or more organs / tissues in the cardiovascular system of the target object. Each model unit can be composed of at least one electronic component.

[0317] As Figure 9 and Figure 10 shown, the equivalent circuit model can include a model unit for simulating pulmonary circulation, a model unit for simulating systemic circulation, a model unit for simulating left coronary artery circulation, a model unit for simulating right coronary artery circulation, a model unit for simulating the left heart, and a model unit for simulating the right heart.

[0318] As Figure 9 and Figure 10 In [reference], the variable capacitor ELA, diode MV, variable capacitor ELV, and diode AoV are combined to simulate the left heart. Among them, the variable capacitor ELA is used to simulate the left atrium, the diode MV is used to simulate the mitral valve, the variable capacitor ELV is used to simulate the left ventricle, the diode AoV is used to simulate the aortic valve between the left ventricle and the aorta, and the cathode of the diode AoV is connected to the aorta.

[0319] The variable capacitor ERA, diode TriV, variable capacitor ERV, and diode PV are combined to simulate the right heart. Among them, the variable capacitor ERA is used to simulate the right atrium, the diode TriV is used to simulate the tricuspid valve, the variable capacitor ERV is used to simulate the right ventricle, and the diode PV is used to simulate the pulmonary valve between the right ventricle and the pulmonary artery.

[0320] Here, the cardiac output of the ventricle can be characterized based on the current output by the variable capacitors (variable capacitor ELV and / or variable capacitor ERV) that simulate the ventricle. For example, based on the current output by the variable capacitor ELV, the cardiac output of the left ventricle, i.e., the native cardiac output data of the target object, can be determined.

[0321] In a possible implementation, the native cardiac index data can be determined based on the cardiac output of the native heart. Specifically, the native cardiac index data includes the quotient of the native cardiac output divided by the body surface area.

[0322] The cardiac output of the native heart combined with the assisted blood output (flow data) of the mechanical circulatory assist device can yield the total cardiac output data when the mechanical circulatory assist device assists the target object.

[0323] In a possible implementation, the total cardiac index data can be determined based on the total cardiac output data. Specifically, the total cardiac index data can be the quotient of the total cardiac output data divided by the body surface area.

[0324] The voltage of the variable capacitor used to simulate the ventricle can characterize the pressure of the ventricle. Therefore, the pressure change data of the ventricle can be determined based on the voltage change of the variable capacitor that simulates the ventricle. The pressure change data of the ventricle can include the pressure change data of the ventricle during a complete cardiac cycle.

[0325] The electric charge quantity of the variable capacitor used to simulate the ventricle can characterize the volume of the ventricle. Therefore, the volume change data of the ventricle can be determined based on the change in the electric charge quantity of the variable capacitor that simulates the ventricle. The volume change data of the ventricle can include the volume change data of the ventricle during a complete cardiac cycle.

[0326] Combining the pressure change data of the ventricle during a complete cardiac cycle and the pressure change data of the ventricle during a complete cardiac cycle can determine the pressure-volume loop data of the ventricle. The pressure-volume loop data of the ventricle can be a graphical tool used to describe the dynamic relationship between the ventricular pressure and the ventricular volume during a complete cardiac cycle, and can intuitively reflect the systolic and diastolic functions, work efficiency, and pathological states of the heart.

[0327] Combining the pressure change data of the ventricle and the volume change data of the ventricle can determine the elasticity of the ventricle. Specifically, the elasticity (E) of the ventricle can be the derivative of the ventricular pressure with respect to the change in the ventricular volume.

[0328] In this way, the auxiliary output blood flow obtained by simulating the mechanical circulatory assist device through the mechanical circulatory assist model is fed back to the equivalent circuit model, and the equivalent circuit simulates cardiac parameter data such as cardiac output data, pressure-volume loop data, and ventricular elastance data of the target object during ventricular assist. On the one hand, the situation of determining cardiac parameter data through invasive detection methods can be reduced, thereby reducing the harm to the target object. On the other hand, the equivalent circuit model can accurately simulate the cardiac parameter data of the target object during ventricular assist, improving the accuracy of the predicted vascular correlation parameters.

[0329] Embodiment 2

[0330] In the present specification, the various embodiments or implementation manners are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts among the various embodiments can be referred to each other. For example, in Embodiment 1, the blood circulation model can be formed by coupling a cardiovascular model and a mechanical circulatory assist model. The implementation manner of the blood circulation model is as any one of the implementation manners in Embodiment 1, which will not be elaborated here.

[0331] In some embodiments, the blood circulation model can also be implemented by a machine learning model, an artificial neural network, etc., for simulating the assistance of a mechanical circulatory assist device to a target object.

[0332] The blood circulation model is used to simulate the assistance of a mechanical circulatory assist device to a target object. The individual characteristics of the target object, that is, the physiological parameter data of each target object (such as each patient) may be different, and the physiological parameter data of the same target object at different times may also be different. Therefore, in order to improve the accuracy of simulating the target object, it is necessary to fit the blood circulation model so that the hemodynamics of the cardiovascular system of the target object simulated by the blood circulation model is more consistent with the actual situation of the cardiovascular system of the target object. The specific fitting method is as follows:

[0333] In some embodiments, a method for monitoring the performance of the cardiovascular system, such as Figure 11 shown in the figure, the method for monitoring the performance of the cardiovascular system includes:

[0334] Step 1101: Obtain a blood circulation model of the connection between the mechanical circulatory assist device and the cardiovascular system in the target object's body.

[0335] The blood circulation model is used to simulate the blood flow situation after the connection between the mechanical circulatory assist device and the cardiovascular system.

[0336] The implementation manner of obtaining the blood circulation model is as any one of the implementation manners in Embodiment 1, which will not be elaborated here. The blood circulation model can also be implemented by a machine learning model, an artificial neural network, etc., for simulating the assistance of a mechanical circulatory assist device to a target object.

[0337] In a possible implementation, a machine learning model, an artificial neural network, etc. can be trained based on operation setting parameter training data and cardiovascular parameter training data. The trained machine learning model or artificial neural network can predict corresponding cardiovascular parameter data based on operation setting parameter data.

[0338] Step 1102: Obtain physiological parameter data associated with the cardiovascular system and obtain operation setting parameter data of the mechanical circulatory support device.

[0339] The specific implementation manner of obtaining the physiological parameter data is as any one of the implementation manners in Embodiment 1, which will not be elaborated herein.

[0340] The operation setting parameter data may also include the values of the operation setting parameters of the mechanical circulatory support device when simulating the mechanical circulatory support device by the blood circulation model. For example, the operation setting parameter data may include the operation setting parameter data of the mechanical circulatory support model in Embodiment 1.

[0341] Step 1103: After the mechanical circulatory support device is connected to the patient, periodically fit the model parameters of the blood circulation model according to the physiological parameter data to obtain model parameter data matching the target object.

[0342] Among them, the model parameters fitted within at least two cycles are different.

[0343] When fitting the blood circulation model, the physiological parameter data can be used as a reference standard for fitting. For example, the model parameter data when the difference between the physiological parameter data output by the blood circulation model and the physiological parameter data of the cardiovascular system is less than a predetermined difference threshold can be used as the model parameter data matching the target object. The model parameters can be the internal parameters of the blood circulation model and can be obtained through fitting or training.

[0344] In a possible implementation, the model parameters for fitting can be the model parameters with a strong correlation with the physiological parameter data.

[0345] After the mechanical circulatory support device is connected to the patient, since the clinical state of the target object will change, the physiological parameter data of the target object will also change over time. Therefore, fitting can be performed periodically to reduce the difference between the blood model simulating the cardiovascular system and the cardiovascular system of the target object.

[0346] Step 1104: Drive the blood circulation model to run based on the model parameter data and the operation setting parameter data, and output the cardiovascular parameter data corresponding to the cardiovascular system.

[0347] The cardiovascular parameter data is used to characterize the physiological state of the cardiovascular system.

[0348] The operating setting parameters can be external parameters of the blood circulation model, which can be set by the user or automatically optimized. The operating setting parameters can be parameters for simulating the operating state of the mechanical circulatory assist device in the mechanical circulatory assist model, such as flow rate or rotational speed.

[0349] In a possible implementation, in the blood circulation model composed of the cardiovascular model and the mechanical circulatory assist model, the model parameters can be the configuration parameters of the cardiovascular model, and the operating setting parameters can be the configuration parameters for the operation of the mechanical circulatory assist model. The model parameter data is obtained by fitting the blood circulation model and the physiological parameter data of the target object. Therefore, driving the blood circulation model with the model parameter data can improve the matching degree between the blood circulation model and the cardiovascular system of the target object. The fitted model parameter data matches the physiological parameter data of the target object for each cycle.

[0350] The operating setting parameters can enable the blood circulation model to accurately simulate the operating state of the mechanical circulatory assist device. Therefore, using the model parameter data and the operating setting parameter data to drive the operation of the blood circulation model can improve the accuracy of the blood circulation model simulation and the accuracy of the predicted cardiovascular parameter data of the target object in the assisted state of the mechanical circulatory assist device.

[0351] For the blood circulation model composed of the cardiovascular model and the mechanical circulatory assist model, the cardiovascular system of the target object is simulated by the cardiovascular model. Therefore, it is necessary to fit the cardiovascular system of the target object with the cardiovascular model. The cardiovascular model outputs pressure gradient data. As can be seen from Embodiment 1, the pressure gradient data is directly related to the accuracy of the blood circulation model prediction. Accurate pressure gradient data can be obtained through the following implementation.

[0352] In some embodiments, such as Figure 12 shown, step 1103 or step 601 in Embodiment 1 may include:

[0353] Step 1201: Fit the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data that matches the target object.

[0354] Step 1202: Drive the operation of the cardiovascular model based on the model parameter data, and determine the first pressure data corresponding to the drainage position and the second pressure data corresponding to the return position in the cardiovascular model.

[0355] Step 1203: Output pressure gradient data based on the first pressure data and the second pressure data.

[0356] The cardiovascular model can operate based on model parameter data to simulate the hemodynamics of the cardiovascular system of the target object. Here, the physiological parameter data can be used as a basis for judging whether the cardiovascular model fits the cardiovascular system of the target object. The smaller the difference between the physiological parameter data simulated by the cardiovascular model and the physiological parameter data of the cardiovascular system, the higher the fitting degree of the cardiovascular model to the cardiovascular system.

[0357] In some embodiments, the physiological parameter data includes at least one of the following: mean arterial pressure data, cardiac output data, blood flow data, atrial pressure data. The mean arterial pressure data, cardiac output data, blood flow data, and atrial pressure data have a relatively high weight in hemodynamics. On the one hand, the accuracy of the fitting judgment can be improved through the above four data. On the other hand, the fitting judgment can be achieved with fewer data, which can improve the fitting efficiency.

[0358] In a possible implementation, the model parameter data when the difference between the physiological parameter data simulated by the vascular model and the physiological parameter data of the cardiovascular system is less than a predetermined difference threshold can be used as the model parameter data matching the target object.

[0359] The cardiovascular model can operate based on the physiological parameter data to simulate the real hemodynamics of the target object, simulate the hemodynamics between the drainage position and the return position, and then obtain the first pressure data at the drainage position and the second pressure data at the return position. The pressure gradient data can be obtained based on the first pressure data and the second pressure data. For example, the pressure gradient data can be the difference between the first pressure data and the second pressure data.

[0360] In a possible implementation, the Bayesian optimization algorithm can be used to achieve the automatic fitting of the model parameters. Bayesian optimization can find a solution close to the global optimum with very few evaluation times.

[0361] The fitting of the cardiovascular model to the target object through the physiological parameter data improves the accuracy of the cardiovascular model in simulating the target object. Based on the fitted cardiovascular model, accurate pressure gradient data can be obtained, which further improves the accuracy of the predicted hemodynamic parameter data and / or blood damage data of the target object under the mechanical circulatory assist device. At the same time, the situation of determining the hemolysis index and the auxiliary output blood flow through invasive detection methods can be reduced, reducing the harm to the target object.

[0362] The model parameters of the blood circulation model can be periodically fitted according to the physiological parameter data, and the following implementation methods can be adopted.

[0363] In the first period after the mechanical circulatory assist device is connected to the patient, according to the physiological parameter data corresponding to the target object in the first period, fit the first number of model parameters in the blood circulation model to obtain the first fitting data of the first number of model parameters matching the target object in the first period;

[0364] In the second period after the first period, according to the physiological parameter data corresponding to the target object in the second period, fit the second number of model parameters in the blood circulation model to obtain the second fitting data of the second number of model parameters matching the target object in the second period. The first number and the second number are different, and at least two cycles include the first period and the second period.

[0365] Here, the first fitting data can be the values of the model parameters obtained by fitting in the first period. Similarly, the second fitting data can be the values of the model parameters obtained by fitting in the second period. The first fitting data and the second fitting data can be the same or different.

[0366] The first fitting data is used to drive the blood circulation model in the first period, and the second fitting data is used to drive the blood circulation model in the second period.

[0367] Here, the fitting in the first period can be the first fitting between the blood circulation model and the target object. The first period can be the period when the blood circulation model first fits with the target object. For example, the first day when the target object uses the mechanical circulatory assist device. In the first period, the first number of model parameters can be used to adjust the blood circulation model. By adjusting the model parameters, the hemodynamics simulated by the blood circulation model can be affected, and further the physiological parameter data predicted by the blood circulation model can be affected.

[0368] In a possible implementation, the first number of model parameters can be selected based on the degree of association between the candidate model parameters and the physiological parameter data, that is, the first number of model parameters can be selected based on the degree of association between the candidate model parameters and the hemodynamics. Specifically, the candidate model parameters associated with the physiological parameter data of the target object can be determined, and then the model parameters for fitting can be selected from the candidate model parameters. Here, the candidate model parameters associated with the physiological parameter data can include: the model parameters whose physiological parameter data can be changed by adjusting the parameter values. The higher the change in the physiological parameter data under the same adjustment amount, the higher the degree of association between the model parameter and the physiological parameter data. The candidate model parameters associated with the physiological parameter data can be sorted, and the first number of candidate model parameters with a higher degree of association can be selected as the model parameters.

[0369] In a possible implementation, the Generalized Simulated Annealing (GSA) algorithm can be used to determine the degree of association between each model parameter and the physiological parameter data. Model parameters with a higher degree of association are selected.

[0370] The physiological parameter data of the target object in the first period can be detected before the first fitting. By comparing the physiological parameter data predicted by the blood circulation model under the current first number of model parameters with the physiological parameter data of the target object in the first period, until the difference between the predicted physiological parameter data and the physiological parameter data of the target object in the first period is within the first predetermined range, it is determined that the blood circulation model fits the target object. The specific data value corresponding to the current model parameter can be used as the first fitting data.

[0371] After determining the first fitting data, the first fitting data can be used to drive the blood circulation model.

[0372] The blood circulation of the target object changes over time, and the blood circulation model may not be able to accurately simulate the hemodynamics of the target object. Therefore, after the first fitting is completed and the mechanical circulatory assist device has been running for the first duration, the hemodynamic fitting between the blood circulation model and the target object can be performed again. Here, the first duration can be one day or multiple days.

[0373] In the second period, since the blood circulation of the target object may change, such as on the third day when the target object uses the mechanical circulatory assist device, etc., the physiological parameter data of the target object in the second period can be detected before the fitting, so that the fitted blood circulation model can match the hemodynamics of the target object in the second period.

[0374] For the blood circulation model composed of a cardiovascular model and a mechanical circulatory assist model, the periodic fitting of the cardiovascular model can be implemented as in the above embodiments.

[0375] Here, the fitting in the first period can be the first fitting between the cardiovascular model and the target object. The first period can be the period when the cardiovascular model first fits with the target object. In the first period, the cardiovascular model can be adjusted with the first number of model parameters. By adjusting the model parameters, the hemodynamics simulated by the cardiovascular model can be affected, and thus the physiological parameter data determined by the cardiovascular model can be affected.

[0376] In a possible implementation, the first quantity of model parameters can be selected based on the degree of association between the candidate model parameters and the physiological parameter data, that is, the first quantity of model parameters can be selected based on the degree of association between the candidate model parameters and hemodynamics. Specifically, the candidate model parameters associated with the physiological parameter data of the target object can be determined, and then the model parameters for fitting can be selected from the candidate model parameters. Here, the candidate model parameters associated with the physiological parameter data can include: the model parameters whose physiological parameter data can be changed by adjusting the parameter values. The greater the change in the physiological parameter data under the same adjustment amount, the higher the degree of association between the model parameter and the physiological parameter data. The candidate model parameters associated with the physiological parameter data can be sorted, and the first quantity of candidate model parameters with a higher degree of association can be selected as the model parameters.

[0377] The physiological parameter data of the target object in the first time period can be detected before the first fitting. By comparing the physiological parameter data predicted by the cardiovascular model under the current first quantity of model parameters with the physiological parameter data of the target object in the first time period, until the difference between the predicted physiological parameter data and the physiological parameter data of the target object in the first time period is within the first predetermined range, it is determined that the cardiovascular model fits the target object. The specific data value corresponding to the current model parameters can be used as the first fitting data.

[0378] After determining the first fitting data, the cardiovascular model can be driven by the first fitting data.

[0379] The blood circulation of the target object changes over time, and the cardiovascular model may not be able to accurately simulate the hemodynamics of the target object. Therefore, after the first fitting is completed and the mechanical circulatory assist device has been operating for the first duration, the hemodynamic fitting between the cardiovascular model and the target object can be performed again. Here, the first duration can be one day or multiple days.

[0380] During the second time period, since the blood circulation of the target object may change, the physiological parameter data of the target object in the second time period can be detected before the fitting, so that the fitted cardiovascular model can match the hemodynamics of the target object in the second time period.

[0381] Here, the first quantity can be different from the second quantity. Similar methods can be used to determine the first quantity and the second quantity for the blood circulation model and the cardiovascular model. Taking the cardiovascular model as an example for illustration here, there is no need to distinguish between the blood circulation model and the cardiovascular model and explain them separately.

[0382] In some embodiments, the second quantity is greater than the first quantity. Specifically, the second quantity can be determined based on the accuracy of fitting. If it is determined that the prediction accuracy of the blood circulation model fitted in the first time period is less than the accuracy threshold, then more model parameters can be used for fitting. For example, the model parameters used in the first time period and the model parameters other than those used in the first time period are used to fit the blood circulation model. Thereby increasing the control dimension of the blood circulation model and improving the accuracy of the blood circulation model.

[0383] In some embodiments, the second quantity is less than the first quantity. Using a smaller number of model parameters can reduce the complexity of the model parameters, thereby improving the fitting efficiency and shortening the fitting time.

[0384] Specifically, the second quantity of model parameters can be selected from the first quantity of model parameters. The second quantity of model parameters with a relatively high degree of association with the physiological parameter data can be selected from the first quantity of model parameters as the model parameters. The physiological parameter data predicted by the cardiovascular model under the current second quantity of model parameters can be compared with the physiological parameter data of the target object in the second time period until the difference between the predicted physiological parameter data and the physiological parameter data of the target object in the second time period is within the second predetermined range, then it is determined that the cardiovascular model in the second time period fits the target object. The specific data value corresponding to the current model parameters can be used as the second fitting data.

[0385] In a possible implementation, the first quantity can be 5 and the second quantity can be 2.

[0386] In some embodiments, the first quantity of model parameters includes: left ventricular elasticity, systemic vascular resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elasticity;

[0387] The second quantity of model parameters includes: left ventricular elasticity and systemic vascular resistance.

[0388] Fitting needs to consider the computational cost of fitting and the fitting accuracy. The computational cost of fitting increases exponentially with the increase in the number of model parameters. Here, left ventricular elasticity, systemic vascular resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elasticity represent the determinants with relatively high influence on hemodynamic performance; they are the five model parameters with a relatively high degree of association with predicting physiological data. Using the above five model parameters can ensure the basic dynamics of the interaction between the cardiovascular model and the mechanical circulatory assist model while maintaining physiological rationality. At the same time, using five model parameters can reduce the computational cost of fitting and achieve a balance between fitting complexity and fitting accuracy. In the actual fitting process, the five model parameters of left ventricular elasticity, systemic vascular resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elasticity can balance computational efficiency and physiological fidelity. By adjusting the cardiovascular model with left ventricular elasticity, systemic vascular resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elasticity in the first period, while reducing the computational cost of fitting, it still meets the requirements of fitting accuracy, which can improve the efficiency of cardiovascular model adjustment and reduce the fitting time of the cardiovascular model. By adjusting the cardiovascular model with two model parameters of left ventricular elasticity and systemic vascular resistance in the second period, the computational cost of fitting can be further reduced, the efficiency of fitting the cardiovascular model after the first fitting can be improved, the subsequent fitting time can be shortened, and the real-time performance of data monitoring can be enhanced.

[0389] In a possible implementation, Figure 9 For the equivalent circuit model of the cardiovascular model, the adjustment of model parameters such as left ventricular elasticity, systemic vascular resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elasticity can be achieved by adjusting at least one electronic component parameter in the equivalent circuit model.

[0390] During the use of the cardiovascular model, multiple fittings can be performed based on the state changes of the target object, so that the hemodynamics of the cardiovascular model conforms to the human hemodynamics of the target object, thereby improving the accuracy of the cardiovascular model in simulating the target object. In the first-period fitting, using fewer model parameters as fitting parameters can reduce the complexity of fitting and improve the fitting efficiency. In the second-period fitting, the cardiovascular model obtained from the first-period fitting can be used as the basis for fitting, and the number of model parameters used in the second-period fitting is less than the number of model parameters used in the first-time fitting in the first period. Using a smaller number of model parameters can reduce the complexity of the model parameters, thereby improving the fitting efficiency and shortening the fitting time.

[0391] Here, a specific example is provided in combination with the above embodiments to illustrate the fitting of the cardiovascular model.

[0392] This example provides 11 target objects from Pt1 to Pt11. The specific information is shown in Table 1.

[0393] Table 1

[0394]

[0395]

[0396]

[0397] Table 2 lists 37 model parameters associated with physiological parameter data. Among the 37 model parameters, the model parameters that have the greatest impact on 3 physiological parameters, namely aortic pressure (mean arterial pressure), cardiac output, and central venous pressure (atrial pressure), are shown in Table 2 as the results of the GSA operation.

[0398] Table 2

[0399]

[0400]

[0401] Since there are too many 37 model parameters, Table 2 only lists the operation results of some of the 37 model parameters, but this does not affect drawing conclusions. According to the results in Table 2, the top parameters with the highest influence on each physiological parameter are arranged from high to low as shown in Table 3. Based on Table 3, the top five with the highest influence are arranged from high to low as shown in Table 4. EmaxLV represents left ventricular elasticity, V0vn represents blood volume, Rsar represents systemic vascular resistance, RV_Pd_a represents right ventricular end-diastolic stiffness, and EmaxRV represents right ventricular elasticity.

[0402] Table 3

[0403]

[0404]

[0405] Table 4

[0406]

[0407] Table 3 lists the top 10 model parameters for each physiological parameter: MAP, CO, and CVP, arranged from 1 to 10 in descending order of influence. Table 4 shows the top five model parameters with the highest influence based on Table 3, arranged from 1 to 5 in descending order of influence. From the above analysis results, it can be seen that this application uses 5 model parameters as the fitting parameters for the first point (the first time period) to achieve a satisfactory fitting result (average error of 1.5% and average time consumption of about 4 hours). In order to further increase the efficiency and reduce the possibility of the model falling into a local solution, it is found after experimentation that for subsequent points (such as the second time period), the number of parameters can be further reduced to as few as only two of the most important model parameters (left ventricular elastance, systemic vascular resistance), further reducing the operation time to an average of 2 hours per point and the average error to 8%.

[0408] The model parameters are selected based on the degree of association between the candidate model parameters and the physiological parameter data. During the fitting process of the cardiovascular model, the adjustment of the model parameters can more effectively achieve the adjustment of the physiological parameter data, thereby improving the fitting efficiency and shortening the fitting time. At the same time, by fitting the cardiovascular model in the above-mentioned fitting method and using fewer model parameters as the fitting parameters, the complexity of the fitting can be reduced and the fitting efficiency can be improved.

[0409] Such as Figure 13 is a comparison between the clinical physiological parameters of a target subject in Table 1 and the physiological parameters predicted by the fitted cardiovascular model. Such as Figure 13 shown, the curve pointed to by arrow A1 is the clinical MAP data curve, and the curve pointed to by arrow B1 is the fitted MAP data curve. In this way, after fitting the data of all patients, a comprehensive regression line can be obtained as Figures 14 to 16 shown. Figure 14 is the comprehensive regression line of MAP, where the average accuracy AP of the mean arterial pressure reaches 0.79. Figure 15 is the comprehensive regression line of blood flow, where the blood flow AP reaches 0.55. Figure 15 is the comprehensive regression line of cardiac output, where the correlation degree of the cardiac output CO reaches 0.75. As described above, the automated fitting ability of the model is verified.

[0410] In a possible implementation, the selection of model parameters can be carried out within a reasonable range, as shown in Table 5.

[0411] Table 5

[0412] Model parameters Range Blood volume 4000-8000ml Systemic vascular resistance 0.2 - 2.8 mmHg / ml Systemic vascular resistance 0.0 - 1.5 mmHg.s / ml Right ventricular elastance 0.5 - 1.5 mmHg / ml Right ventricular end - diastolic stiffness 0.1 - 0.8 mmHg

[0413] Example 3

[0414] The various embodiments or implementation manners in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other.

[0415] The operating setting parameter data of the mechanical circulatory assist device directly affects the hemodynamic parameter data and / or blood injury data. During the process of connecting the mechanical circulatory assist device, medical staff need to set or adjust the operating setting parameter data of the mechanical circulatory assist device, such as the target rotation speed of the blood pump in the mechanical circulatory assist device. The following method can be used to assist medical staff in selecting operating setting parameter data that is more suitable for the current target object.

[0416] In some embodiments, as Figure 17 shown, the method for monitoring the performance of the cardiovascular system further includes:

[0417] Step 1701: Start traversing multiple sets of operating setting parameter data.

[0418] Step 1702: When traversing to the current set of operating setting parameter data, input the pressure gradient data and parameter setting data into the mechanical circulatory assist model to drive the operation of the blood circulation model, output the flow data and blood injury data of the mechanical circulatory assist model, input the flow data into the cardiovascular model to update the operating state of the cardiovascular model, and output the hemodynamic parameter data.

[0419] Step 1703: After traversing all multiple sets of operating setting parameter data, obtain the hemodynamic parameter data corresponding to the multiple sets of operating setting parameter data, and / or the blood injury data corresponding to the multiple sets of operating setting parameter data;

[0420] Step 1704: Determine the first change relationship information between the hemodynamic parameters and the operating setting parameters based on the hemodynamic parameter data corresponding to the multiple sets of operating setting parameter data and the multiple sets of operating setting parameter data; and / or determine the second change relationship information between the blood injury parameters and the operating setting parameters based on the blood injury data corresponding to the multiple sets of operating setting parameter data and the multiple sets of operating setting parameter data.

[0421] Here, the mechanical circulatory assist model and the cardiovascular model can be used for simulation prediction to obtain the changes in the hemodynamic parameter data, and / or the changes in the blood injury data, of the cardiovascular system of the target object when the mechanical circulatory assist device operates with different operating setting parameter data.

[0422] In a possible implementation, the blood injury data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

[0423] The hemodynamic parameter data includes cardiac performance parameter data, and the cardiac performance parameter data includes at least one of the following: native cardiac index data, total cardiac index data, pressure-volume loop data, and ventricular elastance data.

[0424] In a possible implementation, the multiple sets of operating setting parameter data may include all the operating setting parameter data that the mechanical circulatory assist device can support. For example, all the rotational speeds that the blood pump can support. Specifically, the mechanical circulatory assist model can traverse each set of operating setting parameter data and couple with the cardiovascular model under each set of operating setting parameter data, so as to predict the hemodynamic parameter data and / or blood damage data at each rotational speed.

[0425] In a possible implementation, the multiple sets of operating setting parameter data can be selected by medical staff and input into the control device. In another possible implementation, the multiple sets of operating setting parameter data are sampled and traversed by the control device itself.

[0426] The specific implementation manner of constructing a blood circulation model through the mechanical circulatory assist model and the cardiovascular model and predicting the hemodynamic parameter data and / or blood damage data based on the operating setting parameter data can be as in any of the above embodiments, and will not be elaborated here.

[0427] By running the blood circulation model on multiple sets of operating setting parameter data, the hemodynamic parameter data corresponding to each set of operating setting parameter data can be obtained. Therefore, the first change relationship information between the hemodynamic parameters and the operating setting parameters can be determined.

[0428] By running the blood circulation model on multiple sets of operating setting parameter data, the blood damage data corresponding to each set of operating setting parameter data can be obtained. Therefore, the second change relationship information between the blood damage parameters and the operating setting parameters can be determined.

[0429] Through the first change relationship information and / or the first change relationship information, it is convenient for medical staff to evaluate the blood damage data and / or hemodynamic parameter data under different operating setting parameter data, so as to select the operating setting parameter data that is safer for the target object.

[0430] By predicting the blood damage data and / or hemodynamic parameter data under each operating setting parameter data through the mechanical circulatory assist model and the cardiovascular model, medical staff can determine the blood damage data and / or hemodynamic parameter data of the mechanical circulatory assist device under different operating setting parameter data. Thus, appropriate operating setting parameter data can be selected from them for the mechanical circulatory assist device. Reducing or avoiding the safety risks brought to the target object by unreasonable rotational speeds or flow rates.

[0431] In some embodiments, as Figure 18 shown, the method for monitoring the performance of the cardiovascular system further includes:

[0432] Step 1801: Output parameter setting suggestion information corresponding to the operation setting parameters according to the first variation relationship information and / or the second variation information.

[0433] In a possible implementation, outputting parameter setting suggestion information corresponding to the operation setting parameters includes displaying the parameter setting suggestion information on the display screen of the control device and sending the parameter setting suggestion information to the user's terminal.

[0434] The control device may be set with selection conditions for blood injury parameters and / or hemodynamic parameters. The control device may select the operation setting parameter data corresponding to the cardiovascular parameters that meet the selection conditions from the first variation relationship information and / or the second variation information as the recommended parameter settings, such as the recommended rotational speed range and flow rate range.

[0435] Through the parameter setting suggestion information, medical staff can assist medical staff in making a preliminary judgment on the operation setting parameter data, reducing the burden on medical staff to evaluate the operation setting parameter data, and can also be used by medical staff to verify the selected operation setting parameter data, further improving the safety of the mechanical circulatory assist device for assistance.

[0436] In some embodiments, step 1101 includes:

[0437] Obtain a cardiovascular model corresponding to the cardiovascular system in the target object and a mechanical circulatory assist model corresponding to the mechanical circulatory assist device, where the mechanical circulatory assist model includes a reduced-order model obtained by reducing the order of the computational fluid dynamics model corresponding to the mechanical circulatory assist device;

[0438] Obtain the drainage position and the return position of the mechanical circulatory assist device in the cardiovascular system;

[0439] Couple the cardiovascular model and the mechanical circulatory assist model according to the drainage position and the return position to obtain a blood circulation model;

[0440] Step 1104 includes: Fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0441] Step 1104 further includes:

[0442] Based on the model parameter data, drive the cardiovascular model to run and output the pressure gradient data between the drainage position and the return position;

[0443] Input the pressure gradient data and the parameter setting data into the mechanical circulatory assist model to drive the blood circulation model to run and output the cardiovascular parameter data.

[0444] Here, the cardiovascular model and the mechanical circulatory assistance model are obtained, the cardiovascular model is fitted to determine the pressure gradient data, and then the pressure gradient data and the parameter setting data are input into the mechanical circulatory assistance model to determine the cardiovascular parameter data. The specific implementation manners are as described in any one of Embodiments 1 to 4, and will not be elaborated herein.

[0445] In some embodiments, inputting the pressure gradient data and the parameter setting data into the mechanical circulatory assistance model to drive the blood circulation model to operate and output the cardiovascular parameter data includes:

[0446] Inputting the pressure gradient data and the parameter setting data into the mechanical circulatory assistance model to drive the blood circulation model to operate and output the flow rate data and the blood damage data of the mechanical circulatory assistance model;

[0447] Inputting the flow rate data into the cardiovascular model to update the operating state of the cardiovascular model and output the hemodynamic parameter data.

[0448] Here, the specific implementation manners of inputting the pressure gradient data and the parameter setting data into the mechanical circulatory assistance model to determine the flow rate data and the blood damage data of the mechanical circulatory assistance model, and outputting the hemodynamic parameter data by the cardiovascular model based on the flow rate data are as described in any one of Embodiments 1 to 4, and will not be elaborated herein.

[0449] In some embodiments, the mechanical circulatory assistance device includes a power component, a first pipeline component, and a second pipeline component, and the mechanical circulatory assistance model includes a blood pump reduced-order model corresponding to the power component, a pipeline reduced-order model corresponding to the first pipeline component, and a linear pipeline model corresponding to the second pipeline component;

[0450] The method further includes: obtaining first dimension information corresponding to the first pipeline component and second dimension information corresponding to the second pipeline component;

[0451] Inputting the pressure gradient data and the parameter setting data into the mechanical circulatory assistance model to drive the blood circulation model to operate and output the flow rate data and the blood damage data of the mechanical circulatory assistance model includes:

[0452] Processing the flow rate data and the first dimension information through the pipeline reduced-order model to output first pressure loss data at both ends of the first pipeline component and second blood damage data corresponding to the first pipeline component, where the second blood damage data is used to characterize the blood damage situation caused by the first pipeline component;

[0453] Input the flow rate data and the second dimension information into the linear pipeline model for processing, and output the second pressure loss data at both ends of the second pipeline component;

[0454] Based on the pressure gradient data, the first pressure loss data, and the second pressure loss data, determine the pump head pressure difference data corresponding to the power component;

[0455] Input the pump head pressure difference data and the parameter setting data into the blood pump reduced-order model to drive the operation of the blood circulation model, and update and output the flow rate data and the first blood damage data corresponding to the blood pump reduced-order model. The first blood damage data is used to characterize the blood damage caused by the power component;

[0456] Perform fusion processing on the first blood damage data and the second blood damage data to obtain the total blood damage data.

[0457] Here, based on the specific composition of the mechanical circulatory assist device, the specific implementation manner of determining the blood flow damage data through the blood pump reduced-order model, the pipeline reduced-order model, and the linear pipeline model is as described in any one of Embodiments 1 to 4, and will not be elaborated here.

[0458] In some embodiments, the cardiovascular model is a lumped parameter model. The circuit structure in the lumped parameter model is used to characterize the cardiovascular system. The lumped parameter model includes a variable capacitor for simulating the ventricle, and the lumped parameter model is connected to the mechanical circulatory assist model through pressure-flow coupling;

[0459] The step of inputting the flow rate data into the cardiovascular model to update the operating state of the cardiovascular model and output hemodynamic parameter data includes:

[0460] Input the flow rate data into the lumped parameter model to update the current change in the circuit structure;

[0461] Determine the native cardiac output data based on the current data of the variable capacitor;

[0462] Determine the ventricular pressure change data based on the voltage change data of the variable capacitor;

[0463] Determine the ventricular volume change data based on the charge change data of the variable capacitor;

[0464] Determine the total cardiac output data according to the native cardiac output data and the flow rate data;

[0465] Determine at least one of the cardiac pressure-volume loop data and the ventricular elastance data based on the ventricular pressure change data and the ventricular volume change data.

[0466] Here, the specific implementation manner of determining cardiovascular parameter data based on the lumped parameter model is as described in any one of Embodiments 1 to 4, and will not be elaborated here.

[0467] Embodiment 4

[0468] In this specification, the embodiments or implementation manners are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. For the same or similar parts among the various embodiments, reference may be made to each other.

[0469] The operating setting parameter data of the mechanical circulatory assist device directly affects cardiovascular parameters. During the operation of the mechanical circulatory assist device, medical staff need to set or adjust the operating setting parameter data of the mechanical circulatory assist device, such as the rotation speed or flow rate of the blood pump in the mechanical circulatory assist device. The following method can be used to assist medical staff in selecting more suitable operating setting parameter data for the current target object.

[0470] In view of this, the present application also provides a mechanical circulatory assist device, as Figure 19 shown, when in use, the mechanical circulatory assist device is connected and coupled to the cardiovascular system in the target object, and there are a drainage position and a return position between the mechanical circulatory assist device and the cardiovascular system.

[0471] The mechanical circulatory assist device includes a control device, a drive assembly, a power assembly, and a pipeline assembly. One end of the pipeline assembly is located at the drainage position, and the other end of the pipeline assembly is located at the return position.

[0472] The control device is configured to: obtain first parameter setting data corresponding to the operating setting parameters, and control the drive assembly to operate based on the first parameter setting data to drive the power assembly to pump blood, wherein the power assembly drives the blood to flow into the pipeline assembly from the drainage position and flow out from the return position.

[0473] The control device is associated with a user interface, and the user interface is configured to:

[0474] display the cardiovascular parameter monitoring data corresponding to the cardiovascular parameters, and / or display the data change relationship prediction information between the cardiovascular parameters and the operating setting parameters.

[0475] Wherein, the cardiovascular parameters include at least one of cardiac index, blood injury parameter, pressure-volume loop, and ventricular elasticity; the data change relationship prediction information is used to characterize the data change situation of the cardiovascular parameters when the mechanical circulatory assist device is under different operating setting parameters.

[0476] The operating setting parameter data can be input to the control device by the user through the user interface of the mechanical circulatory assist device or through a network connection. The operating setting parameter data can also be the default operating setting parameter data set by the mechanical circulatory assist device.

[0477] In a possible implementation, the operating setting parameter data includes the rotational speed of the power component, the output flow rate of the blood pump, etc. The operating setting parameter data can be the value of the operating setting parameter. For example, if the operating setting parameter is rotational speed, the operating setting parameter data is a specific rotational speed value, such as 1800, etc. The control device can control the drive component based on the operating setting parameter data to drive the power component.

[0478] The cardiovascular parameter data can include data related to the cardiovascular system of the target object assisted by the mechanical circulatory assist device. The cardiovascular parameters can include hemodynamic parameters and blood injury parameters related to the cardiovascular system. Correspondingly, the cardiovascular parameter data can include hemodynamic parameter data and blood injury parameter data related to the cardiovascular system. The cardiovascular parameter data can be the value corresponding to the cardiovascular parameter. For example, if the cardiovascular parameter is cardiac index, the cardiovascular parameter data can be the specific value of the cardiac index, such as 2.2, etc. The blood injury parameter data can be the value corresponding to the blood injury parameter. For example, if the cardiovascular parameter is hemolysis index, the hemolysis index data can be the specific value of the hemolysis index.

[0479] In a possible implementation, the blood injury parameters include but are not limited to at least one of the following: hemolysis index, coagulation index, and thrombosis index.

[0480] In a possible implementation, the hemodynamic parameters include but are not limited to at least one of the following: cardiac index, pressure-volume loop, and ventricular elastance.

[0481] The first parameter setting data can be the operating setting parameter data currently set by the mechanical circulatory assist device. The control device can control the drive component based on the first parameter setting data, and when the drive component works, it drives the power component to pump blood to achieve circulatory assistance for the target object.

[0482] The control device can predict the cardiovascular parameter data when the mechanical circulatory assist device assists the target object with different operating setting parameter data. Specifically, the control device can use a blood circulation model to predict the cardiovascular parameter data based on the operating setting parameter data. The blood circulation model can include a machine learning model, etc. The machine learning model can be trained based on the operating setting parameter training data and the cardiovascular parameter training data, and the trained machine learning model can predict the corresponding cardiovascular parameter data based on the operating setting parameter data.

[0483] In a possible implementation, cardiovascular parameter data under different operating setting parameter data can be predicted by a processing device other than the mechanical circulatory assist device. The external processing device can send the prediction result to the user interface for display by the user interface. It can be understood that during the process of predicting and displaying cardiovascular parameter data, the mechanical circulatory assist device and the processing device outside the mechanical circulatory assist device can be collectively referred to as the mechanical circulatory assist device as an interconnected system.

[0484] The cardiovascular parameter monitoring data can be the cardiovascular parameter data predicted by the control device based on the first parameter setting data. The control device can also predict the cardiovascular parameter data corresponding to at least one operating setting parameter data other than the first parameter setting data. Thus, prediction information on the data change relationship characterizing the data change of cardiovascular parameters of the mechanical circulatory assist device under different operating setting parameters is obtained.

[0485] The operating setting parameter data used to predict cardiovascular parameter data can include all the operating setting parameter data supported by the mechanical circulatory assist device, or can be part of the operating setting parameter data selected from all the operating setting parameter data.

[0486] The user interface can display the prediction results, such as cardiovascular parameter monitoring data and / or prediction information on the data change relationship. The user interface can interact with the user by displaying data, charts, etc. The user interface associated with the control device can include a display screen set on the control device; the user interface can also include a display screen separated from the control device, such as the display screen of an external monitor, the display screen of an external display, and / or the display screen of a user terminal. The control device can send information to the user interface through a data connection for display by the user interface.

[0487] The prediction information on the data change relationship can characterize the data change of cardiovascular parameters of the mechanical circulatory assist device under different operating setting parameters by listing multiple operating setting parameter data and the corresponding cardiovascular parameter data; the prediction information on the data change relationship can also characterize the data change of cardiovascular parameters of the mechanical circulatory assist device under different operating setting parameters by means of a change relationship curve of the operating setting parameter data and the cardiovascular parameter data.

[0488] By displaying the monitoring data of cardiovascular parameters, a mechanical circulatory assist device enables medical staff to judge the effect of the mechanical circulatory assist device on assisting the cardiovascular system when the mechanical circulatory assist device operates with the first parameter setting data. This facilitates medical staff to promptly detect problems and adjust the operating setting parameters of the mechanical circulatory assist device in a timely manner, reducing or avoiding adverse effects on the target object and improving the safety and effectiveness of the mechanical circulatory assist device. By displaying the prediction information on the data change relationship between the operating setting parameters and the cardiovascular parameters, medical staff can, without actually adjusting the operating setting parameters of the mechanical circulatory assist device, view the changes in the cardiovascular parameters when the mechanical circulatory assist device operates with different operating setting parameter data. This helps medical staff select appropriate operating setting parameter data for the mechanical circulatory assist device from among them and determine whether the assisting effect of the current operating setting parameter data is reasonable, reducing the safety risks brought by unreasonably set operating setting parameter data to the target object. Combining the cardiovascular parameter monitoring data and the data change relationship prediction information, the user can evaluate the rationality of the currently adopted first parameter setting data, thereby improving the accuracy of setting the operating setting parameters.

[0489] In some embodiments, the user interface is further configured to: based on the data change relationship prediction information, display parameter setting suggestion information corresponding to the operating setting parameters.

[0490] The control device can be set with selection conditions for cardiovascular parameters. The control device can select the operating setting parameter data corresponding to the cardiovascular parameter data that meets the selection conditions based on the data change relationship prediction information as the recommended operating setting parameter data. The control device can also select the data range of the operating setting parameter data corresponding to the cardiovascular parameter data that meets the selection conditions based on the data change relationship prediction information as the recommended operating setting parameter data range.

[0491] The user interface can display the recommended operating setting parameter data and / or the operating setting parameter data range to the user through the parameter setting suggestion information.

[0492] In a possible implementation manner, the parameter setting suggestion information can be displayed by showing the recommended operating setting parameter data, and / or by a display method of marking the recommended operating setting parameter data with graphics, colors, etc.

[0493] After the user obtains the parameter setting suggestion information, the user can select the operating setting parameter data from it based on the parameter setting suggestion information, and the control device uses the suggested operating setting parameter data selected by the user from the parameter setting suggestion information to control the drive component. Through the parameter setting suggestion information, the user can be assisted in making a preliminary judgment on the operating setting parameter data, reducing the burden on the user to evaluate the rationality of the operating setting parameter data, and can also be used by the user to verify the currently selected first parameter setting data, further improving the safety of the mechanical circulatory assist device for assisting the target object.

[0494] In some embodiments, the data change relationship prediction information is presented as a prediction data statistical chart, and the prediction data statistical chart includes cardiovascular parameter data corresponding to each of a plurality of operating setting parameter data.

[0495] Among them, the plurality of operating setting parameter data includes first parameter setting data and a plurality of second parameter setting data. The first parameter setting data is the setting data currently corresponding to the operating setting parameter, and the second parameter setting data is different from the first parameter setting data;

[0496] The plurality of cardiovascular parameter data includes cardiovascular parameter monitoring data and a plurality of cardiovascular parameter prediction data, and the cardiovascular parameter prediction data corresponds one-to-one with the second parameter setting data.

[0497] Here, both the cardiovascular parameter monitoring data and the cardiovascular parameter prediction data are cardiovascular parameter data predicted by the control device. The operating setting parameter data includes first parameter setting data and second parameter setting data.

[0498] The first parameter setting data may be the operating setting parameter data currently used by the control device to control the operation of the drive component. The second parameter setting data may be one or more operating setting parameter data other than the first parameter setting data.

[0499] The cardiovascular parameter monitoring data may be data obtained by the control device predicting the cardiovascular parameters of the target object when the drive component operates at the first parameter setting data.

[0500] The cardiovascular parameter prediction data may be data obtained by the control device predicting the cardiovascular parameters of the target object when the drive component operates at the second parameter setting data. The control device may predict the cardiovascular parameter prediction data corresponding to each of one or more second parameter setting data.

[0501] Each operating setting parameter data in the prediction data statistical chart corresponds one-to-one with the predicted cardiovascular parameter data. For example, the first parameter setting data corresponds to the cardiovascular parameter monitoring data, and each second parameter setting data corresponds to a cardiovascular parameter prediction data respectively.

[0502] The prediction data statistical chart can display the cardiovascular parameter monitoring data corresponding to the first parameter setting data and the cardiovascular parameter prediction data corresponding to each second parameter setting data by listing the data.

[0503] The prediction data statistical chart can also characterize the cardiovascular parameter monitoring data corresponding to the first parameter setting data and the cardiovascular parameter prediction data corresponding to each second parameter setting data in the form of a change curve. For example, the prediction data statistical chart can use a coordinate system with two coordinate axes. The first coordinate axis represents the operating setting parameters, and the second coordinate axis is used to represent the cardiovascular parameters. A point in the coordinate system represents the cardiovascular parameter data corresponding to an operating setting parameter data. For example, the first coordinate axis coordinate of the point in the coordinate system is the operating setting parameter data, and the second coordinate axis coordinate is the predicted cardiovascular parameter data. The change correspondence between the operating setting parameter data and the predicted cardiovascular parameter data is characterized by connecting the points in the coordinate system.

[0504] Through the prediction data statistical chart, the effect of the mechanical circulatory assist device on assisting the cardiovascular system under the first parameter setting data and the second parameter setting data can be determined, so that appropriate operating setting parameter data can be selected from them for the mechanical circulatory assist device. The user can evaluate the rationality of the currently adopted first parameter setting data, thereby improving the accuracy of the operating setting parameter setting. At the same time, displaying the cardiovascular parameter data corresponding to the operating setting parameter data in the form of a chart is more intuitive and convenient for the user to observe.

[0505] In some embodiments, the parameter setting suggestion information includes the recommended setting data corresponding to the operating setting parameters.

[0506] The recommended setting data is associated with the operating setting parameter data corresponding to the target data, and the target data is the data that meets the conditions among multiple cardiovascular parameter prediction data.

[0507] The control device can be set with selection conditions for cardiovascular parameters. The control device can select the operating setting parameter data corresponding to the cardiovascular parameter data that meets the selection conditions from the cardiovascular parameter monitoring data and multiple cardiovascular parameter prediction data as the recommended operating setting parameter data. The control device can also select the data range of the operating setting parameter data corresponding to the cardiovascular parameter data that meets the selection conditions from the data change relationship prediction information as the recommended setting data.

[0508] The user interface can display the recommended setting data to the user.

[0509] In a possible implementation manner, it can be displayed by displaying the recommended setting data and / or marking the recommended setting data.

[0510] After the user obtains the recommended setting data, on the one hand, the user can check the current first parameter setting data to determine whether the first parameter setting data is correct or reasonable. On the other hand, the user can select the target operating setting parameter data from the recommended setting data, and the control device uses the target operating setting parameter data to control the driving component. Through the recommended setting data, it can assist the user in making a preliminary judgment on the operating setting parameter data, reducing the burden on the user to evaluate the operating setting parameter data, and can also be used by the user to verify the currently selected first parameter setting data, further improving the safety of the mechanical circulatory assist device in providing assistance.

[0511] In some embodiments, the parameter setting recommendation information includes the first identification information corresponding to the recommended setting data, and the user interface is further configured to:

[0512] display the second identification information corresponding to the first parameter setting data;

[0513] In the case where the first identification information and the second identification information indicate that the first parameter setting data does not match the recommended setting data, issue a parameter adjustment prompt information and / or a risk prompt information.

[0514] In a possible implementation, the identification information is used to highlight the first parameter setting data and the recommended setting data on the user interface. For example, the identification information can use at least one of a specific font, background color, and mark to identify the operating setting parameter data.

[0515] In a possible implementation, the first identification information can be used to highlight the recommended setting data. The first identification information can also be used to highlight the recommended setting data and the cardiovascular parameter prediction data corresponding to the recommended setting data. The second identification information can be used to highlight the first parameter setting data. The second identification information can also be used to highlight the first parameter setting data and the cardiovascular parameter monitoring data corresponding to the first parameter setting data.

[0516] In a possible implementation, the first identification information and the second identification information can be different. For example, in the coordinate system, the first identification information corresponding to the recommended setting data is a green background within the range of the first parameter setting data in the coordinate system. The first parameter setting data corresponding to the first identification information is a red background within the range of the first parameter setting data in the coordinate system.

[0517] Exemplarily, such as Figure 20As shown, the abscissa in the coordinate system represents the operation setting parameters, and the ordinate represents the cardiovascular parameter monitoring data (total cardiac index monitoring data). The curve pointed by arrow X1 in the figure includes the predicted cardiovascular parameter monitoring data corresponding to the operation setting parameters. Among them, the first set of parameter data is identified by the second identification information, and the second identification information can be the first background color within the range of arrow B2. The recommended setting data is identified by the first identification information, and the first identification information can be the second background color within the range of arrow A2. The first background color is different from the second background color. In this way, it can be clearly determined that the first set of parameter data is within the recommended setting data.

[0518] In this way, the first set of parameter data and the recommended setting data are respectively identified by the first identification information and the second identification information. The distinguishability of the first set of parameter data and the recommended setting data can be improved. Users can intuitively determine the relationship between the recommended setting data and the first set of parameter data, determine whether the first set of parameter data is within the range of the recommended operation setting parameter data, and improve the visibility of data display.

[0519] In some embodiments, the operation setting parameters include rotational speed, and the data change relationship prediction information includes a first data statistical chart, and the first data statistical chart is used to characterize the data change trend between the cardiac index and the rotational speed;

[0520] The first data statistical chart includes cardiac index data corresponding to multiple rotational speed setting data respectively;

[0521] Among them, the multiple rotational speed setting data includes a first rotational speed setting data and multiple second rotational speed setting data. The first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data;

[0522] The multiple cardiac index data includes the cardiac index monitoring data corresponding to the first rotational speed setting data and multiple cardiac index prediction data, and the multiple cardiac index prediction data corresponds one-to-one to the multiple second rotational speed setting data.

[0523] The cardiac index is an important indicator to measure heart function. The cardiac index can be obtained by dividing the blood flow data pumped by the heart by the body surface area.

[0524] Here, the first rotational speed setting data can be the target rotational speed used by the current control device to control the operation of the drive component. The second rotational speed setting data can include one or more rotational speed setting data other than the first rotational speed setting data.

[0525] The cardiac index monitoring data can be the data predicted by the control device for the cardiac index of the target object when the drive component operates at the first rotational speed setting data.

[0526] The cardiac index prediction data can be the data obtained by the control device predicting the cardiac index of the target object when the driving component operates at the second rotational speed setting data. The control device can predict the cardiac index prediction data corresponding to one or more second rotational speed setting data respectively.

[0527] Each rotational speed setting data in the first data statistical chart corresponds one-to-one with the predicted cardiac index. For example, the first rotational speed setting data corresponds to the cardiac index monitoring data, and each second rotational speed setting data corresponds to a cardiac index prediction data respectively.

[0528] The first data statistical chart can display the cardiac index monitoring data corresponding to the first rotational speed setting data and the cardiac index prediction data corresponding to each second rotational speed setting data respectively in a way of listing data.

[0529] The first data statistical chart can also characterize the cardiac index monitoring data corresponding to the first rotational speed setting data and the cardiac index prediction data corresponding to each second rotational speed setting data respectively in a way of a change curve. For example, the first data statistical chart can adopt a coordinate system with two coordinate axes. The first coordinate axis represents the rotational speed, and the second coordinate axis is used to represent the cardiac index. A point in the coordinate system represents the cardiac index data corresponding to a rotational speed setting data. The connection of each point in the coordinate system characterizes the change correspondence relationship between the operating set rotational speed setting data and the predicted cardiac index data.

[0530] Through the first data statistical chart, the effect of the mechanical circulatory assist device on assisting the cardiovascular system at the first rotational speed setting data and the recommended second rotational speed setting data can be determined, so that a suitable rotational speed setting data can be selected from them for the mechanical circulatory assist device. The user can evaluate the rationality of the currently adopted first rotational speed setting data, thereby improving the accuracy of the rotational speed setting.

[0531] In some embodiments, the cardiac index includes the native cardiac index and the total cardiac index. The first data statistical chart includes a first trend line and / or a second trend line. The first trend line is used to characterize the data change trend between the native cardiac index and the rotational speed, and the second trend line is used to characterize the data change trend between the total cardiac index and the rotational speed.

[0532] Here, when the mechanical circulatory assist device assists the target object, the heart of the target object will pump blood, and the mechanical circulatory assist device will also pump blood. Therefore, the control device can predict the rotational speed setting data, the native cardiac index, and the total cardiac index. The native cardiac index data can be obtained by dividing the blood flow rate data pumped by the heart of the target object by the body surface area. The total cardiac index data can be obtained by dividing the total blood flow rate data (the sum of the blood flow rate data pumped by the heart and the blood flow rate data pumped by the mechanical circulatory assist device) by the body surface area.

[0533] The first data statistical chart can represent the native cardiac index monitoring data and the total cardiac index monitoring data corresponding to the first rotational speed setting data, as well as the native cardiac index prediction data and the total cardiac index prediction data corresponding to each second rotational speed setting data in the form of a trend line.

[0534] Exemplarily, as Figure 21 shown, in the 21 coordinate system, the abscissa represents the rotational speed and the ordinate represents the cardiac index. The black dot symbols in the figure include the total cardiac index monitoring data corresponding to the first rotational speed setting data (2000 RPM) and the total cardiac index prediction data corresponding to the second rotational speed setting data. Connecting the points results in the second trend line indicated by the X2 arrow. In a possible implementation, adjacent black dot symbols can be directly connected, or adjacent black dot symbols can be connected by interpolation.

[0535] Figure 21 The "x" symbols in include the native cardiac index monitoring data corresponding to the first rotational speed setting data (2000 RPM) and the native cardiac index prediction data corresponding to the second rotational speed setting data. Connecting the points results in the first trend line indicated by the Y2 arrow. In a possible implementation, adjacent "x" symbols can be directly connected, or adjacent "x" symbols can be connected by interpolation.

[0536] By presenting the first trend line and the second trend line, the user can respectively and intuitively observe the native cardiac index data and the total cardiac index data corresponding to multiple rotational speed setting data, thereby judging the physiological state of the native heart and the blood perfusion condition of the whole body of the target object, which is convenient for the user to select the rotational speed setting data that meets the conditions for both the native cardiac index data and the total cardiac index data based on the trend line.

[0537] In some embodiments, the blood damage parameter includes the hemolysis index, and the first data statistical chart further includes a third trend line for representing the data change trend between the hemolysis index and the rotational speed.

[0538] The first data statistical chart can include trend lines (such as the first trend line and / or the second trend line) representing the data change trend between the cardiac index and the rotational speed, and can simultaneously display the third trend line representing the data change trend between the hemolysis index and the rotational speed.

[0539] Exemplarily, as Figure 22 shown, Figure 22 can include a trend line (such as the one indicated by the arrow X3) representing the data change trend between the cardiac index and the rotational speed, Figure 22 and further includes a trend line (such as the one indicated by the arrow Y3) representing the data change trend between the hemolysis index and the rotational speed.

[0540] In some scenarios, the user can select the rotation speed by combining multiple cardiovascular parameter data. Therefore, the trend lines of the data changes between multiple cardiovascular parameters and the rotation speed can be intuitively displayed in the same data statistical chart, so as to facilitate the user to select the rotation speed by combining multiple cardiovascular parameter data and improve the accuracy of rotation speed selection. For example, the user can combine the trend line of the cardiac index indicated by arrow X3 and the trend line of the hemolysis index indicated by arrow Y3, and select the rotation speed setting data that can meet the conditions for both the cardiac index data and the hemolysis index data. Thereby improving the selection efficiency of the rotation speed setting data and reducing the situation of misselection. In this application, digital twin models were established for 11 patients in Table 1 at the first postoperative time point (T1), and multi-pump speed (rotation speed) simulations were performed at the T1 time point. Some of the measurement data are as described in Table 6 and Table 9. By combining the patient-specific body surface area (BSA) data, the native cardiac index (Cl heart) and hemolysis index (MlH) corresponding to each pump speed were calculated, as Figure 26 and Figure 27 shown. Among them, Figure 26 are the native cardiac index data of 11 patients at different pump speeds. Figure 27 (For the convenience of illustration, only some curves are marked) are the hemolysis index data of 11 patients at different pump speeds. The results show that there are individual differences in the rotation speed setting data of the Cl heart zeroing threshold for different patients. When the rotation speed setting data exceeds this threshold, the slope of the mean arterial pressure (MAP) in the simulation data changes significantly, indicating that the mechanical circulatory assist device almost completely replaces the patient's own cardiac output. Although there are differences among patients in the MlH value, the overall change trend is similar.

[0541] Table 6

[0542]

[0543]

[0544] Table 7

[0545] Position RPM Cl_total (L / min / m^2) Pt1 WH 1780 2.3 Pt2 SX 2050 1.6 Pt3 CQ 1940 2.12 Pt4 WH 2270 2.15 Pt5 SX 2110 1.8 Pt6 WH 2090 1.75 Pt7 WH 1940 2.46 Pt8 NJ 2001 3.4

[0546] In some embodiments, the first data statistical chart further includes a recommended rotation speed range, and the rotation speed setting data within the recommended rotation speed range satisfies at least one of the following conditions:

[0547] The native cardiac index data corresponding to the maximum rotation speed setting data within the recommended rotation speed range is greater than the first threshold;

[0548] The total cardiac index data corresponding to the minimum rotation speed setting data within the recommended rotation speed range is greater than or equal to the second threshold;

[0549] The maximum rotational speed setting data within the recommended rotational speed range is less than the first rotational speed threshold. The first rotational speed threshold is the rotational speed setting data corresponding to the native cardiac index equal to the first threshold in the first trend line. There is a first difference between the first rotational speed threshold and the maximum rotational speed setting data. Optionally, the first threshold is 0;

[0550] The hemolysis index data corresponding to the maximum rotational speed setting data within the recommended rotational speed range is less than the third threshold.

[0551] When the mechanical circulatory assist device assists a target subject, the native heart of the target subject needs to maintain at least a minimum blood output. Therefore, when the mechanical circulatory assist device operates at the maximum rotational speed setting data, the native heart still needs to maintain a minimum blood output to prevent the risk of ventricular aspiration caused by excessive rotational speed, that is, the native cardiac index data corresponding to the maximum rotational speed setting data needs to be greater than the first threshold. When the mechanical circulatory assist device operates at the minimum rotational speed setting data, the blood output of the native heart and the blood output of the mechanical circulatory assist device also need to meet the minimum blood circulation requirements of the target subject, that is, the total cardiac index data corresponding to the minimum rotational speed setting data is greater than the second threshold.

[0552] Therefore, the recommended rotational speed setting data can be selected based on the first threshold and the second threshold. The recommended rotational speed range includes the rotational speed range of the recommended rotational speed setting data.

[0553] Further, the maximum rotational speed setting data within the recommended rotational speed range can be less than the rotational speed setting data corresponding to the first threshold. The first difference can be set based on the safety margin of the native cardiac index, so that at the maximum rotational speed setting data, the native heart of the target subject still maintains a cardiac output to prevent ventricular aspiration. Thereby improving the safety of the target subject when the mechanical circulatory assist device operates. An exemplary first difference can be 500 RPM.

[0554] The rotational speed setting data is positively correlated with the hemolysis index data. The third threshold can be set based on the safety of the hemolysis index. Setting the hemolysis index data corresponding to the maximum rotational speed setting data within the recommended rotational speed range to be less than the third threshold can reduce the rotational speed range in which the mechanical circulatory assist device operates with an unsafe hemolysis index data.

[0555] In a possible implementation, the second threshold is 2.2.

[0556] After obtaining the relationship curve between the rotational speed setting data and the hemolysis index data or the relationship curve between the rotational speed setting data and the cardiac index data, a recommended rotational speed range that is blood-friendly to the patient can be determined according to the corresponding conditions for the performance of the current machine.

[0557] Exemplarily, as Figure 22 shown, Figure 23In the coordinate system, the abscissa represents the speed and the ordinate represents the native cardiac index. For example, Figure 22 It may include a trend line (indicated by arrow X3) characterizing the data change trend between the cardiac index and the rotational speed. For example, Figure 22 It also includes a trend line (indicated by arrow Y3) characterizing the data change trend between the hemolysis index and the rotational speed.

[0558] For example, Figure 23 As shown, Figure 23 In the coordinate system, the abscissa represents the speed and the ordinate represents the native cardiac index. For example, Figure 23 It may include a trend line (indicated by arrow X4) characterizing the data change trend between the cardiac index and the rotational speed. For example, Figure 22 and Figure 23 It also includes a trend line (indicated by arrow Y4) characterizing the data change trend between the hemolysis index and the rotational speed.

[0559] Exemplarily, as Figure 21 shown, after obtaining the relationship curve between the rotational speed setting data and the total cardiac index data, according to the preset threshold of the cardiac index data, the recommended rotational speed range for the performance of the current machine is determined and displayed to the doctor through the screen to prevent aspiration risk. Specifically, the recommended rotational speed range includes a safe rotational speed rpm range, such as Figure 21 the range indicated by arrow A3 in Figure 21 where the total cardiac index (CI_total) exceeds 2.2, the native cardiac index (CI_heart) of this machine remains greater than zero, and a conservative margin of 500 rpm is subtracted from the lower boundary (as

[0560] shown by arrow Y1, the abscissa starts from 500 rpm). In the range indicated by arrow A3, the total cardiac index (CI_total) exceeds 2.2, meeting the condition of the total cardiac index, and the rotational speed data is less than 2500, which can prevent aspiration. Therefore, the range of 500 - 2500 rpm is set as the recommended rotational speed range. Figure 22 As

[0561] Figure 22 shown, the range indicated by arrow A4 in the figure is the recommended rotational speed range. The range indicated by arrow B4 is the first set rotational speed setting data. As shown in the figure, the range indicated by arrow A5 in the figure is the recommended rotational speed range. The range indicated by arrow B5 is the first set rotational speed setting data.

[0562] Figure 23 As shown, the first rotational speed setting data is outside the recommended rotational speed range, and the cardiac index data corresponding to the first rotational speed setting data cannot meet the requirements. The first rotational speed setting data is less than the minimum value of the recommended rotational speed range. In this case, the mechanical circulatory assist device may have insufficient support, and medical staff can thus conduct further examinations to confirm whether there is an actual situation of insufficient support.

[0563] By setting the recommended rotational speed range, users can immediately determine whether the current rotational speed setting data is reasonable through the recommended rotational speed range and can make adjustments in a timely manner, thereby improving the safety level of the target object when using the mechanical circulatory assist device.

[0564] Here, this embodiment will be described with reference to 11 target objects in Table 2. Specifically, the safe range of rpm (i.e., the recommended rotational speed range) can be determined, such as Figure 22 and Figure 23 the ranges respectively pointed to by arrow A4 and arrow A5 in, which is composed of the rpm with CI_total > 2.2 and the rpm with CI_heart > 0 minus 500 rpm, and compare it with the actual clinical data (i.e., the recommended rotational speed range): Figure 22 and Figure 23 the ranges respectively pointed to by arrow B4 and arrow B5 in. It is composed of the historical adjusted rotational speed range from the first day of clinically using the mechanical circulatory assist device until the final end of use, and the two areas are compared. Among the 11 patients, the settings of the mechanical circulatory assist device for 5 patients failed to provide sufficient cardiac support, such as Figure 23 is one of them, Figure 23 in, the first rotational speed setting data is outside the recommended rotational speed range. Therefore, there is a risk that the cardiovascular parameter data does not meet the conditions. Therefore, selecting other settings (rotational speed setting data) may make the overall application safer. And Figure 22 is one of the overlaps of the range pointed to by arrow A4 and the range pointed to by arrow B4. The range pointed to by arrow B4 is included in the range pointed to by arrow A4, that is, the first rotational speed setting data is within the recommended rotational speed range. In addition, the appropriate support range of the mechanical circulatory assist device varies greatly among different patients (1780 rpm - 2900 rpm, CI_total 1.6 - 2.9), and there are also certain differences in the use in different medical centers (1780 rpm - 2900 rpm, CI_total 1.7 - 2.9).

[0565] In some embodiments, the first parameter setting data includes the first rotational speed setting data, and the user interface is further configured to perform at least one of the following operations:

[0566] When the first rotational speed setting data does not fall within the recommended rotational speed range, display a rotational speed adjustment prompt message;

[0567] When the first rotational speed setting data is greater than the maximum rotational speed setting data, display at least one of an over-support risk prompt message, a suction risk message, and a blood damage risk message;

[0568] When the first rotational speed setting data is less than the minimum rotational speed setting data, display an under-support risk prompt message.

[0569] The speed range of the recommended rotational speed range is defined by the maximum rotational speed setting data and the minimum rotational speed setting data. When the first rotational speed setting data does not fall within the recommended rotational speed range, it may cause abnormalities in the cardiovascular parameter data of the target object, such as too high hemolysis index, under-support, over-support, etc. Therefore, when the first rotational speed setting data does not fall within the recommended rotational speed range, the user interface can display a rotational speed adjustment prompt message to the user to prompt the user to adjust the rotational speed, thereby improving the safety of the target object when the mechanical circulatory assist device is working.

[0570] When the first rotational speed setting data exceeds the maximum rotational speed setting data, certain risks may occur, such as the ventricles or atria being over-aspirated and collapsing, and the hemolysis index exceeding the safe range. Therefore, the user interface can display an over-support risk prompt message when the first rotational speed setting data exceeds the maximum rotational speed setting data to remind the user to adjust the speed.

[0571] When determining the specific safety risks based on the cardiovascular parameter data, the specific risk types can be displayed. For example, if it is determined based on the cardiovascular parameter data that the suction risk is high, the suction risk message can be displayed. If it is determined based on the cardiovascular parameter data that the blood damage is high, the blood damage risk message can be displayed. By displaying the specific risk types, the user can directly determine the risk types, improve the efficiency of the user's risk judgment, reduce the user's response time, and improve the safety of the target object.

[0572] When the first rotational speed setting data is less than the minimum rotational speed setting data, the cardiovascular system may not receive sufficient support, resulting in blood circulation risks. The user interface can promptly display an under-support risk prompt message to remind the user to adjust the speed. Thus, the user can respond in a timely manner and improve the safety of the target object.

[0573] In some embodiments, the operation setting parameters include rotational speed, and the data change relationship prediction information includes a second data statistical chart, and the second data statistical chart is used to characterize the data change trend between the pressure-volume loop and the rotational speed;

[0574] The second data statistical chart includes pressure-volume loop curves corresponding to multiple rotational speed setting data respectively, and the pressure-volume loop curves are used to characterize the variation relationship between the ventricular pressure and the ventricular volume corresponding to the ventricle;

[0575] Among them, the multiple rotational speed setting data include a first rotational speed setting data and multiple second rotational speed setting data. The first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data;

[0576] The multiple pressure-volume loop curves include a pressure-volume loop monitoring curve corresponding to the first rotational speed setting data and multiple pressure-volume loop prediction curves, and the multiple pressure-volume loop prediction curves correspond one-to-one with the multiple second rotational speed setting data.

[0577] The pressure-volume loop describes the variation relationship between the ventricular pressure and the ventricular volume of the heart (usually referring to the left ventricle). The pressure-volume loop curve uses a curve in the coordinate system to characterize the variation relationship between the ventricular pressure and the ventricular volume.

[0578] The control device can determine the pressure-volume loop curve by predicting the ventricular pressure data and the ventricular volume data. The process by which the control device determines the pressure-volume loop curve by predicting the ventricular pressure data and the ventricular volume data is simply referred to as predicting the pressure-volume loop curve in this embodiment without special instructions.

[0579] Here, the first rotational speed setting data can be the target rotational speed adopted by the current control device to control the operation of the driving component. The second rotational speed setting data can include one or more rotational speed setting data other than the first rotational speed setting data.

[0580] The pressure-volume loop monitoring curve can be obtained by the control device predicting the pressure-volume loop curve of the target object when the driving component operates at the first rotational speed setting data.

[0581] The pressure-volume loop prediction curve can be obtained by the control device predicting the pressure-volume loop of the target object when the driving component operates at the second rotational speed setting data. The control device can predict the pressure-volume loop prediction curves corresponding to one or more second rotational speed setting data respectively.

[0582] Each rotational speed setting data in the second data statistical chart corresponds one-to-one with the predicted pressure-volume loop curve. For example, the first rotational speed setting data corresponds to the pressure-volume loop monitoring curve, and each second rotational speed setting data corresponds to a pressure-volume loop prediction curve respectively.

[0583] Exemplarily, as Figure 24 shown, Figure 24 has three pressure-volume loop curves ( Figure 24The ones indicated by the arrows O, P, and Q are obtained by the control device predicting three rotational speed setting data.

[0584] Through the second data statistical chart, the pressure-volume loop curve of the mechanical circulatory assist device assisting the cardiovascular system under the first rotational speed setting data and the second rotational speed setting data can be determined, so that appropriate rotational speed setting data can be selected therefrom for the mechanical circulatory assist device. The user can evaluate the rationality of the currently adopted first rotational speed setting data, thereby improving the accuracy of rotational speed setting.

[0585] In some embodiments, the cardiovascular parameter further includes ventricular elastance, and the second data statistical chart is further used to characterize the data change trend between ventricular elastance and rotational speed;

[0586] The second data statistical chart includes ventricular elastance lines respectively corresponding to multiple rotational speed setting data, and the slope of the ventricular elastance line is related to ventricular elastance; the slope of the ventricular elastance line can characterize the left ventricular elastance or represent ventricular contractility.

[0587] Among them, the multiple rotational speed setting data include the first rotational speed setting data and multiple second rotational speed setting data. The first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data;

[0588] The multiple ventricular elastance lines include the ventricular elastance monitoring line corresponding to the first rotational speed setting data and multiple ventricular elastance prediction lines, and the multiple ventricular elastance prediction lines correspond one by one to the multiple second rotational speed setting data.

[0589] Ventricular elastance generally refers to the elastic properties of the ventricular wall, describes the resistance of the ventricular wall to volume changes, and is a key mechanical property for understanding ventricular filling (diastolic function) and ejection (systolic function).

[0590] In a possible implementation manner, the ventricular elastance line can be determined based on the pressure-volume loop. The ventricular elastance line can be the end-systolic pressure-volume relationship curve of the pressure-volume loop curve, and the slope of the end-systolic pressure-volume relationship curve is used to characterize ventricular elastance.

[0591] The control device can determine the pressure-volume loop curve by predicting ventricular pressure data and ventricular volume data, and then determine the ventricular elastance line. The control device determines the pressure-volume loop curve by predicting ventricular pressure data and ventricular volume data. In the process of determining the ventricular elastance line, unless otherwise specified, it is simply referred to as predicting the ventricular elastance line in this embodiment.

[0592] Here, the first rotational speed setting data can be the target rotational speed currently used by the control device to control the operation of the drive component. The second rotational speed setting data can include one or more rotational speed setting data other than the first rotational speed setting data.

[0593] The pressure ventricular elastance monitoring line can be obtained by the control device predicting the ventricular elastance of the target object when the driving component operates at the first rotational speed setting data.

[0594] The ventricular elastance prediction line can be obtained by the control device predicting the ventricular elastance of the target object when the driving component operates at the second rotational speed setting data. The control device can predict the ventricular elastance prediction lines corresponding to one or more second rotational speed setting data respectively.

[0595] Each rotational speed setting data in the second data statistical chart corresponds one-to-one with the predicted pressure ventricular elastance line. For example, the first rotational speed setting data corresponds to the pressure ventricular elastance monitoring line, and each second rotational speed setting data corresponds to a ventricular elastance prediction line respectively.

[0596] Exemplarily, as Figure 24 shown, Figure 24 there are three ventricular elastance prediction lines in Figure 24 (indicated by arrows a, b, and c in

[0597] ), which are obtained by the control device predicting three rotational speed data. Through the second data statistical chart, the ventricular elastance lines for the mechanical circulatory assist device to assist the cardiovascular system at the first rotational speed setting data and the second rotational speed setting data can be determined, so that a suitable rotational speed setting data can be selected therefrom for the mechanical circulatory assist device. The user can evaluate the rationality of the currently adopted first rotational speed setting data, thereby improving the accuracy of the rotational speed setting.

[0598] In some embodiments, the second data statistical chart further includes recommended rotational speed information, and the rotational speed setting data in the recommended rotational speed information satisfies at least one of the following;

[0599] The pressure-volume loop curve corresponding to the rotational speed setting data in the recommended rotational speed information satisfies the pressure-volume loop condition;

[0600] The ventricular elastance line corresponding to the rotational speed setting data in the recommended rotational speed information satisfies the ventricular elastance condition;

[0601] The user interface is further configured to:

[0602] In the case where the first rotational speed setting data does not match the recommended rotational speed information, display a rotational speed adjustment prompt message and / or a risk prompt message for indicating ventricular systolic performance.

[0603] The control device may be set with selection conditions for the pressure-volume loop curve and / or the ventricular elastance line. The control device may select the rotational speed setting data corresponding to the pressure-volume loop curve and / or the ventricular elastance line that meet the selection conditions from the pressure-volume loop curve and / or the ventricular elastance line in the second data statistical chart as the recommended rotational speed setting data. The user interface may display the recommended rotational speed setting data to the user through the recommended rotational speed information.

[0604] In a possible implementation manner, the user interface may display the recommended rotational speed setting data and / or mark the recommended rotational speed setting data for display.

[0605] After the user obtains the rotational speed setting data, on the one hand, the user may determine whether the pressure-volume loop curve meets the pressure-volume loop condition and whether the ventricular elastance line meets the ventricular elastance condition to judge whether the first rotational speed setting data is correct or reasonable. On the other hand, the user may select the target rotational speed setting data based on the recommended rotational speed information according to whether the pressure-volume loop curve meets the pressure-volume loop condition and whether the ventricular elastance line meets the ventricular elastance condition, and the control device uses the rotational speed setting data to control the driving component. Through the rotational speed setting data, it can assist the user in pre-judging the rotational speed setting data, reducing the burden on the user to evaluate the rotational speed setting data, and can also be used by the user to verify the currently selected first rotational speed setting data, further improving the safety of the mechanical circulatory assist device during assistance.

[0606] If the first rotational speed setting data does not match the recommended rotational speed information, that is, the first rotational speed setting data exceeds the maximum rotational speed setting data in the recommended rotational speed setting data, or the first rotational speed setting data is lower than the minimum rotational speed setting data in the recommended rotational speed setting data, certain safety risks may occur, such as the pressure-volume loop curve and / or the ventricular elastance line not meeting the selection conditions. Therefore, the user interface may display a rotational speed adjustment prompt message when the first rotational speed setting data does not match the recommended rotational speed information to remind the user to adjust the speed, thereby improving the safety of the target object.

[0607] Based on the pressure-volume loop curve and / or the ventricular elastance line, specific safety risks are determined. When it is determined that there is a risk of ventricular systolic performance, for example, the specific risk type may be displayed. For example, a risk prompt message for ventricular systolic performance may be displayed. By displaying the risk prompt message for ventricular systolic performance, the user can determine the risk type, which can improve the user's efficiency in judging risks, reduce the user's response time, and improve the safety of the target object.

[0608] In some embodiments, the operating setting parameter includes the rotational speed, the cardiovascular parameter includes the ventricular elastance, and the data change relationship prediction information includes a third data statistical chart, and the third data statistical chart is used to characterize the data change trend between the ventricular elastance and the rotational speed;

[0609] The third data statistical chart includes ventricular elasticity data corresponding to multiple rotational speed setting data respectively;

[0610] Among them, the multiple rotational speed setting data includes a first rotational speed setting data and multiple second rotational speed setting data. The first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data;

[0611] The multiple ventricular elasticity data includes ventricular elasticity monitoring data corresponding to the first rotational speed setting data and multiple ventricular elasticity prediction data. The multiple ventricular elasticity prediction data corresponds one-to-one with the multiple second rotational speed setting data.

[0612] Different from the second data statistical chart, the above third data statistical chart can display ventricular elasticity data alone. Of course, the above third data statistical chart can also display other cardiovascular parameters, which are not limited in the embodiments of the present application.

[0613] In a possible implementation manner, the control device can predict ventricular elasticity data.

[0614] In a possible implementation manner, the ventricular elasticity data can include specific ventricular elasticity values or can include curves characterizing ventricular elasticity.

[0615] Here, the first rotational speed setting data can be the target rotational speed adopted by the current control device to control the operation of the driving component. The second rotational speed setting data can include one or more rotational speed setting data other than the first rotational speed setting data.

[0616] The pressure ventricular elasticity monitoring data can be obtained by the control device predicting the ventricular elasticity of the target object when the driving component operates at the first rotational speed setting data.

[0617] The ventricular elasticity prediction data can be obtained by the control device predicting the ventricular elasticity of the target object when the driving component operates at the second rotational speed setting data. The control device can predict ventricular elasticity prediction data corresponding to one or more second rotational speed setting data respectively.

[0618] Each rotational speed setting data in the third data statistical chart corresponds one-to-one with the predicted pressure ventricular elasticity data. For example, the first rotational speed setting data corresponds to the pressure ventricular elasticity monitoring data, and each second rotational speed setting data corresponds to a ventricular elasticity prediction data respectively.

[0619] The ventricular elastic data of the mechanical circulatory assist device for assisting the cardiovascular system under the first rotational speed setting data and the second rotational speed setting data can be determined through the third data statistical chart, so that appropriate rotational speed setting data can be selected therefrom for the mechanical circulatory assist device. The user can evaluate the rationality of the currently adopted first rotational speed setting data, thereby improving the accuracy of rotational speed setting.

[0620] In some embodiments, the third data statistical chart further includes recommended rotational speed information, and the rotational speed setting data in the recommended rotational speed information corresponds to ventricular elastic data that satisfies the ventricular elasticity condition;

[0621] The user interface is further configured to:

[0622] In the case where the first rotational speed setting data does not match the recommended rotational speed information, display a rotational speed adjustment prompt information and / or a risk prompt information for indicating ventricular systolic performance.

[0623] The control device can be set with a ventricular elasticity condition for the ventricular elastic data. The control device can select the rotational speed setting data corresponding to the ventricular elastic data that satisfies the ventricular elasticity condition from the ventricular elastic data in the third data statistical chart as the recommended rotational speed setting data. The user interface can display the recommended rotational speed setting data to the user through the recommended rotational speed information.

[0624] In a possible implementation manner, the user interface can be displayed by displaying the recommended rotational speed setting data and / or marking the recommended rotational speed setting data.

[0625] After the user obtains the rotational speed setting data, on the one hand, the user can check the current first rotational speed setting data, and judge whether the first rotational speed setting data is correct or reasonable by whether the ventricular elastic data satisfies the ventricular elasticity condition. On the other hand, the user can select the target rotational speed setting data therefrom based on the recommended rotational speed information, and the control device uses the rotational speed setting data to control the driving component. Through the rotational speed setting data, it can assist the user to pre-judge the rotational speed setting data, reduce the burden of the user evaluating the rotational speed setting data, and can also be used by the user to verify the currently selected first rotational speed setting data, further improving the safety of the mechanical circulatory assist device for assistance.

[0626] If the first rotational speed setting data does not match the recommended rotational speed information, that is, the first rotational speed setting data exceeds the maximum rotational speed setting data in the recommended rotational speed setting data, or the first rotational speed setting data is lower than the minimum rotational speed setting data in the recommended rotational speed setting data, certain safety risks may occur, such as the ventricular elastic data not satisfying the ventricular elasticity condition. Therefore, the user interface can display a rotational speed adjustment prompt information when the first rotational speed setting data does not match the recommended rotational speed information to remind the user to adjust the speed, thereby improving the safety of the target object.

[0627] Based on ventricular elasticity data, specific safety risks are determined. For example, when determining the risk of ventricular systolic performance, specific risk types can be presented. For example, risk prompt information regarding ventricular systolic performance can be shown. By presenting the risk prompt information regarding ventricular systolic performance, users can determine the risk types, which can improve the efficiency of users' risk judgment, reduce the response time of users, and enhance the safety of the target object.

[0628] In some embodiments, the operating setting parameters include rotational speed, the blood damage parameter includes the hemolysis index, and the data change relationship prediction information includes a fourth data statistical chart. The fourth data statistical chart is used to characterize the data change trend between the hemolysis index and the rotational speed, and the fourth data statistical chart includes hemolysis index data corresponding to multiple rotational speed setting data respectively.

[0629] Among them, the multiple rotational speed setting data include a first rotational speed setting data and multiple second rotational speed setting data. The first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data.

[0630] The multiple hemolysis index data include the hemolysis index monitoring data corresponding to the first rotational speed setting data and multiple hemolysis index prediction data. The multiple hemolysis index prediction data are in one-to-one correspondence with the multiple second rotational speed setting data.

[0631] When the blood pump in the mechanical circulatory assist device operates, it can cause damage to the blood, such as hemolysis. The hemolysis index can indicate the degree of hemolysis damage caused by the mechanical circulatory assist device.

[0632] In a possible implementation, the control device can predict the hemolysis index data.

[0633] In a possible implementation, the hemolysis index data can include specific hemolysis index values or can also include a curve characterizing the hemolysis index.

[0634] Here, the first rotational speed setting data can be the target rotational speed used by the current control device to control the operation of the drive component. The second rotational speed setting data can include one or more rotational speed setting data other than the first rotational speed setting data.

[0635] The hemolysis index monitoring data can be predicted by the control device for the hemolysis index of the target object when the drive component operates at the first rotational speed setting data.

[0636] The hemolysis index prediction data can be predicted by the control device for the hemolysis index of the target object when the drive component operates at the second rotational speed setting data. The control device can predict the hemolysis index prediction data corresponding to one or more second rotational speed setting data respectively.

[0637] In the fourth data statistical chart, each rotational speed setting data corresponds one-to-one with the predicted hemolysis index data. For example, the first rotational speed setting data corresponds to the hemolysis index monitoring data, and each second rotational speed setting data corresponds to a hemolysis index prediction data respectively.

[0638] Through the fourth data statistical chart, the hemolysis index data of the mechanical circulatory assist device assisting the cardiovascular system at the first rotational speed setting data and the second rotational speed setting data can be determined, so that appropriate rotational speed setting data can be selected therefrom for the mechanical circulatory assist device. The user can evaluate the rationality of the currently adopted first rotational speed setting data, thereby improving the accuracy of the rotational speed setting and reducing the damage to the blood caused by the mechanical circulatory assist device.

[0639] In some embodiments, the fourth data statistical chart further includes a recommended rotational speed range, and the hemolysis index data corresponding to the rotational speed setting data within the recommended rotational speed range is lower than the third threshold;

[0640] The user interface is further configured to perform at least one of the following operations:

[0641] When the first rotational speed setting data does not fall within the recommended rotational speed range, display a rotational speed adjustment prompt message;

[0642] When the first rotational speed setting data is greater than the maximum rotational speed setting data of the recommended rotational speed range, display a hemolysis risk prompt message.

[0643] The control device can be set with a hemolysis index condition for the hemolysis index data, that is, the third threshold. If the hemolysis index data is lower than the third threshold, it indicates that the mechanical circulatory assist device has less damage to the blood. The control device can select the rotational speed setting data corresponding to the hemolysis index data less than the third threshold from the ventricular elasticity data in the fourth data statistical chart as the recommended rotational speed setting data, and determine the speed range of the recommended rotational speed setting data, that is, the recommended rotational speed range. The user interface can display the recommended rotational speed setting data to the user through the recommended rotational speed range.

[0644] In a possible implementation, the recommended rotational speed range can be determined by the control device in combination with the hemolysis index data and at least one other cardiovascular parameter data.

[0645] In a possible implementation, the user interface can be displayed by displaying the recommended rotational speed setting data and / or marking the recommended rotational speed setting data.

[0646] After the user obtains the rotation speed setting data, on the one hand, the user can check the current first rotation speed setting data, and determine whether the first rotation speed setting data is correct or reasonable by determining whether the hemolysis index data is lower than the third threshold. On the other hand, the user can select the target rotation speed setting data from the recommended rotation speed information, and the control device uses the rotation speed setting data to control the drive component. Through the rotation speed setting data, it can assist the user to pre-judge the rotation speed setting data, reduce the burden on the user to evaluate the rotation speed setting data, and can also be used by the user to verify the currently selected first rotation speed setting data, further improving the safety of the mechanical circulatory assist device for assistance.

[0647] If the first rotation speed setting data does not match the recommended rotation speed range, it may pose a safety risk to the target object. The user interface can display a rotation speed adjustment prompt message when the first rotation speed setting data does not match the recommended rotation speed information to remind the user to adjust the speed, thereby improving the safety of the target object.

[0648] If the first rotation speed setting data exceeds the maximum rotation speed setting data in the recommended rotation speed setting data, it may cause the hemolysis index data to be greater than the third threshold, thereby generating a certain safety risk. Therefore, a hemolysis risk prompt message can be displayed. By displaying the hemolysis risk prompt message, the user can determine the type of risk, improve the efficiency of the user's risk judgment, reduce the user's response time, reduce the damage to the blood caused by the mechanical circulatory assist device, and improve the safety of the target object.

[0649] In some embodiments, the cardiovascular parameter monitoring data is presented as a monitoring data statistical chart, and the monitoring data statistical chart includes the monitoring data corresponding to the cardiovascular parameters at multiple time nodes. The monitoring data statistical chart is used to display the changes in the physiological state of the cardiovascular system during the support process of the mechanical circulatory assist device.

[0650] Here, the multiple time nodes may include multiple historical time nodes.

[0651] The control device recording the monitoring data corresponding to multiple time nodes may include: the control device predicting the cardiovascular parameters corresponding to the first parameter setting data at multiple time nodes. The first parameter setting data for each time node may be the same or different.

[0652] In a possible implementation, the control device can record each time node and the corresponding cardiovascular parameter monitoring data.

[0653] The monitoring data statistical chart can first display each time node and the corresponding cardiovascular parameter monitoring data for each time node.

[0654] The monitoring data statistical chart can represent the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by listing multiple time nodes and the corresponding cardiovascular parameter monitoring data; or can also represent the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by means of the change relationship curve of multiple time nodes and the cardiovascular parameter monitoring data. Through the monitoring data statistical chart, the user can intuitively observe the cardiovascular parameter monitoring data of the target object's heart at multiple time nodes, so as to judge the recovery status of the heart function.

[0655] In this way, through the monitoring data corresponding to multiple time nodes, the user can determine the cardiovascular parameter monitoring data corresponding to different operating setting parameter data at historical time nodes. Thus, the evolution status of the cardiovascular parameter data of the target object can be determined, and the status of the heart function of the target object can be determined more accurately.

[0656] In some embodiments, the monitoring data statistical chart includes at least one of the following:

[0657] Cardiac systolic performance statistical chart, the cardiac systolic performance statistical chart includes ventricular elasticity monitoring data corresponding to multiple time nodes, and / or, pressure-volume loop monitoring curves corresponding to multiple time nodes, and the cardiac systolic performance statistical chart is used to characterize the change of the cardiac systolic performance during the support process of the mechanical circulatory assist device;

[0658] Cardiac index monitoring data chart, the cardiac index monitoring data chart includes cardiac index monitoring data corresponding to multiple time nodes, and the cardiac index monitoring data chart is used to characterize the change of the cardiac output during the support process of the mechanical circulatory assist device;

[0659] Blood injury monitoring data chart, the blood injury monitoring data chart includes blood injury parameter monitoring data corresponding to multiple time nodes, and the blood injury monitoring data chart is used to characterize the blood injury during the support process of the mechanical circulatory assist device.

[0660] The monitoring data statistical chart can be used to display cardiovascular parameter data related to the mechanical circulatory assist device.

[0661] The control device predicts at least one of the pressure-volume loop curve, cardiac index monitoring data, and blood injury parameter monitoring data corresponding to multiple time nodes. The monitoring of the pressure-volume loop curve, cardiac index monitoring data, and blood injury parameter monitoring data can be respectively displayed through the cardiac systolic performance statistical chart, cardiac index monitoring data chart, and blood injury monitoring data chart.

[0662] The cardiac systolic performance statistical chart can represent the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by listing multiple time nodes and the corresponding pressure-volume loop monitoring curves. Exemplarily, such asFigure 25 As shown, the pressure-volume loop monitoring curves indicated by arrows J, K, and L can be monitored at three time nodes respectively. Among them, J is the pressure-volume loop monitoring curve data monitored on the last day, K is the pressure-volume loop monitoring curve monitored on the middle day, and L is the pressure-volume loop monitoring curve monitored on the first day. From this, it can be seen that the pressure-volume loop data of the heart is gradually improving.

[0663] Exemplarily, as Figure 25 shown, Figure 25 has three ventricular elastance prediction lines ( Figure 25 indicated by arrows j, k, and l in

[0664] The cardiac index monitoring data chart can characterize the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by listing multiple time nodes and the corresponding cardiac index monitoring data; it can also characterize the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by means of the change relationship curve of multiple time nodes and the cardiac index monitoring data.

[0665] The blood injury monitoring data chart can characterize the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by listing multiple time nodes and the corresponding blood injury parameter monitoring data; it can also characterize the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by means of the change relationship curve of multiple time nodes and the blood injury parameter monitoring data.

[0666] In this way, through the monitoring data corresponding to multiple time nodes, the user can determine the cardiovascular parameter monitoring data corresponding to different operating setting parameter data at historical time nodes. Thus, the development status of the cardiovascular parameter data of the target object can be determined, and an accurate assessment of the status of the target object can be achieved. For example, the cardiac systolic performance, cardiac index, and blood injury of the target object can be evaluated respectively through the cardiac systolic performance statistical chart, cardiac index monitoring data chart, and blood injury monitoring data chart, so as to be able to judge the recovery status of cardiac function.

[0667] In some embodiments, the user interface is further configured to:

[0668] display weaning suggestion information when the monitoring data statistical chart indicates the performance recovery of the cardiovascular system;

[0669] and / or display risk warning information when the monitoring data statistical chart indicates the performance deterioration of the cardiovascular system.

[0670] The monitoring data statistical chart can be used as the basis for the user to evaluate the condition of the target object. If the monitoring data statistical chart indicates that the performance of the cardiovascular system of the target object has recovered, then the user interface can display weaning advice information for the user to refer to. The user can comprehensively evaluate the performance of the cardiovascular system based on the displayed weaning advice information to determine the subsequent treatment plan. Among them, the weaning advice information can be used to indicate that the condition of the target object can perform blood circulation by itself without relying on a mechanical circulatory assist device.

[0671] If the monitoring data statistical chart indicates that the performance of the cardiovascular system has deteriorated, such as at least one of the cardiac index data, pressure-volume loop data, and ventricular elastance data being lower than the corresponding threshold or not meeting the corresponding conditions, then the user interface can display risk warning information.

[0672] By displaying the weaning advice information and / or risk warning information, automated monitoring of the cardiovascular parameters of the mechanical circulatory assist device can be achieved, which can reduce the requirement for the user to continuously observe the monitoring data manually, as well as reduce the burden of manually continuously judging the cardiac index data, pressure-volume loop data, and ventricular elastance data.

[0673] In a possible implementation, multiple cardiovascular models corresponding to multiple target users can be obtained through fitting, such as multiple LPM models, and integrated into the software platform. The user (usually a medical device company) can upload the relevant data of the mechanical circulatory assist device to be tested, and the software platform can fit the mechanical circulatory assist model of the mechanical circulatory assist device to be tested, such as a reduced-order model, and integrate the reduced-order model with the hemodynamic models of multiple target users to obtain the trend lines of the cardiovascular parameter data corresponding to multiple target users respectively, so as to evaluate the performance of the mechanical circulatory assist device.

[0674] In a possible implementation, mechanical circulatory assist models corresponding to multiple mechanical circulatory assist devices can be made, such as multiple reduced-order models, and integrated into the software platform. The user (such as a doctor) can upload the relevant data of the target user to be treated, and the software platform can fit the cardiovascular model of the target user to be treated, such as an LPM model, and couple the LPM model with multiple reduced-order models respectively to obtain the trend lines of the cardiovascular parameter data corresponding to each coupled reduced-order model respectively, so as to find a ventricular assist device suitable for the target user before the device is put into use.

[0675] Exemplarily, based on the above settings, a set of digital twin models can be generated at each recorded time point. These models support retrospective simulation to evaluate the potential impact of different speed setting data on patient hemodynamics at a specific time point. The model integrates the complete fluid dynamics information of the mechanical circulatory assist device, and can separate the contribution of the mechanical circulatory assist device from the measured total cardiac output (CO), thereby inferring the changes in the patient's own intrinsic cardiac output at different pump speeds.

[0676] In some embodiments, the control device is further configured to perform:

[0677] Acquiring a blood circulation model, wherein the blood circulation model is used to simulate blood flow conditions after the mechanical circulatory assist device is connected to the cardiovascular system;

[0678] Acquiring physiological parameter data associated with the cardiovascular system, and acquiring operating setting parameter data of the mechanical circulatory assist device;

[0679] The blood circulation model is driven to operate based on the physiological parameter data and the operation setting parameter data, and the cardiovascular parameter monitoring data and / or the data change relationship prediction information are output.

[0680] Here, the specific implementation method of using the blood circulation model to perform cardiovascular parameter monitoring data and data change relationship prediction information prediction based on the physiological parameter data and the operation setting parameter data is as described in any one of embodiments 1 to 4, and will not be repeated here.

[0681] In some embodiments, the control device is specifically configured to perform:

[0682] Acquire a cardiovascular model corresponding to the cardiovascular system and a mechanical circulatory assistance model corresponding to the mechanical circulatory assistance device, wherein the mechanical circulatory assistance model comprises a reduced-order model obtained by reducing the order of a computational fluid dynamics model corresponding to the mechanical circulatory assistance device;

[0683] The cardiovascular model and the mechanical circulation assistance model are coupled according to the drainage position and the reflux position to obtain a blood circulation model.

[0684] Here, the specific implementation of obtaining the blood circulation model by coupling the cardiovascular model and the reduced-order model is as described in any of the implementations in Examples 1 to 4, and will not be repeated here.

[0685] In some embodiments, the control device is specifically configured to perform:

[0686] The step of fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0687] Traverse multiple groups of parameter setting data corresponding to the operation setting parameters;

[0688] When traversing to the current group of parameter setting data, drive the operation of the blood circulation model based on the model parameter data and the parameter setting data, and output the cardiovascular parameter data corresponding to the cardiovascular system;

[0689] Determine the prediction information on the data change relationship according to the cardiovascular parameter data corresponding to the multiple groups of parameter setting data.

[0690] Here, the specific implementation manner of determining the cardiovascular parameter data corresponding to multiple groups of parameter setting data by traversing the operation setting parameters and determining the prediction information on the data change relationship is as described in any one of Embodiments 1 to 4, and will not be elaborated here.

[0691] In some embodiments, the cardiovascular model is a lumped parameter model, the circuit structure in the lumped parameter model is used to characterize the cardiovascular system, the lumped parameter model includes a variable capacitor for simulating the ventricle, and the lumped parameter model and the mechanical circulatory assist model are connected through pressure-flow coupling;

[0692] The control device is specifically configured to execute:

[0693] Fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0694] Drive the operation of the cardiovascular model based on the model parameter data, and output the pressure gradient data between the drainage position and the return position;

[0695] Input the pressure gradient data and the parameter setting data into the mechanical circulatory assist model to drive the operation of the blood circulation model, and output the flow data and blood damage data of the mechanical circulatory assist model;

[0696] Input the flow data into the lumped parameter model to update the current change in the circuit structure;

[0697] Determine the native cardiac output data based on the current data of the variable capacitor;

[0698] Determine the ventricular pressure change data based on the voltage change data of the variable capacitor;

[0699] Determine the ventricular volume change data based on the charge change data of the variable capacitor;

[0700] Determine the total cardiac output data according to the native cardiac output data and the flow data;

[0701] Determine at least one of pressure-volume loop data and ventricular elastance data based on the pressure change data and the volume change data of the ventricle.

[0702] Determine native cardiac index data based on native cardiac output data;

[0703] Determine total cardiac index data based on total cardiac output data.

[0704] Here, the specific implementation manner of determining cardiovascular parameter data based on the lumped parameter model is as described in any one of Embodiments 1 to 4, and will not be elaborated here.

[0705] As Figure 26 described, the present disclosure also provides a monitoring device 10 for cardiovascular system performance. The device includes a processing module 11, and the processing module is configured to:

[0706] Obtain a cardiovascular model corresponding to the cardiovascular system in a target object and a mechanical circulatory assist model corresponding to a mechanical circulatory assist device, where the mechanical circulatory assist model includes a reduced-order model obtained by reducing the order of a computational fluid dynamics model corresponding to the mechanical circulatory assist device;

[0707] Obtain the drainage position and the return position of the mechanical circulatory assist device in the cardiovascular system;

[0708] Couple the cardiovascular model and the mechanical circulatory assist model according to the drainage position and the return position to obtain a blood circulation model, where the blood circulation model is used to simulate the blood flow condition after the mechanical circulatory assist device is connected to the cardiovascular system;

[0709] Obtain physiological parameter data associated with the cardiovascular system and obtain operation setting parameter data of the mechanical circulatory assist device;

[0710] Drive the blood circulation model to run based on the physiological parameter data and the operation setting parameter data, and output at least one of hemodynamic parameter data and blood damage data corresponding to the cardiovascular system.

[0711] In some embodiments, the processing module is specifically configured to:

[0712] Drive the cardiovascular model to run according to the physiological parameter data, and output the pressure gradient data between the drainage position and the return position;

[0713] Input the pressure gradient data and the operation setting parameter data into the mechanical circulatory assist model to drive the blood circulation model to run, and output at least one of hemodynamic parameter data and blood damage data corresponding to the cardiovascular system.

[0714] In some embodiments, the processing module is specifically configured to:

[0715] Fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0716] Based on the model parameter data, driving the operation of the cardiovascular model to determine the first pressure data corresponding to the drainage position in the cardiovascular model and the second pressure data corresponding to the return position in the cardiovascular model;

[0717] Based on the first pressure data and the second pressure data, outputting pressure gradient data.

[0718] In some embodiments, the processing module is specifically configured to:

[0719] In the first period after the mechanical circulatory assist device is put into use, fitting the first number of model parameters in the cardiovascular model according to the physiological parameter data corresponding to the first period to obtain the first fitting data corresponding to the first number of model parameters in the first period, wherein the first fitting data is used to drive the cardiovascular model;

[0720] In the second period after the first period, fitting the second number of model parameters in the cardiovascular model according to the physiological parameter data corresponding to the second period to obtain the second fitting data corresponding to the second number of model parameters in the second period, and the second fitting data is used to drive the cardiovascular model, and the first number and the second number are different.

[0721] In some embodiments, the second number is less than the first number.

[0722] In some embodiments, the first number of model parameters includes: left ventricular elasticity, systemic vascular resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elasticity;

[0723] The second number of model parameters includes: left ventricular elasticity and systemic vascular resistance.

[0724] In some embodiments, the processing module is specifically configured to:

[0725] Inputting the pressure gradient data and the operation setting parameter data into the mechanical circulatory assist model to drive the operation of the blood circulation model, and outputting the flow data and blood damage data of the mechanical circulatory assist model;

[0726] Inputting the flow data into the cardiovascular model to update the operation state of the cardiovascular model and outputting hemodynamic parameter data.

[0727] In some embodiments, the mechanical circulatory assist device includes a power component and a first pipeline component, and the mechanical circulatory assist model includes a blood pump reduced-order model corresponding to the power component and a pipeline reduced-order model corresponding to the first pipeline component; the pipeline reduced-order model is used to determine first pressure loss data at both ends of the first pipeline component based on flow rate data;

[0728] Specifically, the processing module is configured to: determine pump head pressure difference data corresponding to the power component based on pressure gradient data and the first pressure loss data;

[0729] Input the pump head pressure difference data and the operating setting parameter data into the blood pump reduced-order model to drive the operation of the blood circulation model, and output flow rate data and first blood damage data corresponding to the blood pump reduced-order model, where the first blood damage data is used to characterize the blood damage caused by the power component.

[0730] In some embodiments, the processing module is further configured to:

[0731] Obtain first dimension information corresponding to the first pipeline component;

[0732] Fit the pipeline reduced-order model based on the first dimension information.

[0733] In some embodiments, the processing module is specifically configured to:

[0734] Input the flow rate data into the pipeline reduced-order model for processing, and output the first pressure loss data and second blood damage data corresponding to the first pipeline component, where the second blood damage data is used to characterize the blood damage caused by the first pipeline component.

[0735] In some embodiments, the processing module is specifically configured to:

[0736] Perform fusion processing on the first blood damage data and the second blood damage data to obtain total blood damage data.

[0737] In some embodiments, the mechanical circulatory assist device further includes a second pipeline component, and the mechanical circulatory assist model further includes a linear pipeline model corresponding to the second pipeline component; the processing module is further configured to:

[0738] Obtain second dimension information corresponding to the second pipeline component;

[0739] Fit the linear pipeline model based on the second dimension information;

[0740] Input the flow rate data into the linear pipeline model for processing, and output second pressure loss data at both ends of the second pipeline component;

[0741] Specifically, the processing module is configured to:

[0742] Determine pump head pressure difference data corresponding to the power component based on the pressure gradient data, the first pressure loss data, and the second pressure loss data.

[0743] In some embodiments,

[0744] The first pipeline assembly includes: a drainage cannula and a return cannula; the second pipeline assembly includes a first pipeline and a second pipeline; wherein, the input end of the drainage cannula is used to connect to the drainage position, the output end of the drainage cannula is used to connect to the input end of the first pipeline, the output end of the first pipeline is used to connect to the input end of the power assembly, the output end of the power assembly is used to connect to the input end of the second pipeline, the output end of the second pipeline is used to connect to the input end of the return cannula, and the output end of the return cannula is used to connect to the return position;

[0745] The pipeline reduced-order model includes: a drainage cannula reduced-order model corresponding to the drainage cannula and a return cannula reduced-order model corresponding to the return cannula; the drainage cannula reduced-order model is used to determine the pressure loss data at both ends of the drainage cannula based on the flow rate data; the return cannula reduced-order model is used to determine the pressure loss data at both ends of the return cannula based on the flow rate data; the first pressure loss data is determined based on the pressure loss data at both ends of the drainage cannula and the pressure loss data at both ends of the return cannula;

[0746] The linear pipeline model includes: a first linear pipeline model corresponding to the first pipeline and a second linear pipeline model corresponding to the second pipeline; the first linear pipeline model is used to determine the pressure loss data at both ends of the first pipeline based on the flow rate data; the second linear pipeline model is used to determine the pressure loss data at both ends of the second pipeline based on the flow rate data; the second pressure loss data is determined based on the pressure loss data at both ends of the first pipeline and the pressure loss data at both ends of the second pipeline.

[0747] In some embodiments, the drainage cannula reduced-order model is used to determine the blood damage data of the drainage cannula based on the flow rate data; the return cannula reduced-order model is used to determine the blood damage data of the return cannula based on the flow rate data; the blood damage data of the drainage cannula and the blood damage data of the return cannula are used to determine the second blood damage data corresponding to the first pipeline assembly.

[0748] In some embodiments, the processing module is further configured to:

[0749] Start traversing multiple sets of operating setting parameter data;

[0750] When traversing to the current set of operating setting parameter data, start executing the step of inputting the pressure gradient data and the operating setting parameter data into the mechanical circulatory assist model to drive the blood circulation model to run and output the flow rate data and blood damage data of the mechanical circulatory assist model;

[0751] After traversing all the multiple sets of operating setting parameter data, obtain the hemodynamic parameter data corresponding to the multiple sets of operating setting parameter data, and / or, the blood damage data corresponding to the multiple sets of operating setting parameter data;

[0752] Determine the first variation relationship information between the hemodynamic parameters and the operating setting parameters based on the hemodynamic parameter data corresponding to multiple groups of operating setting parameter data and the multiple groups of operating setting parameter data; and / or, determine the second variation relationship information between the blood injury parameters and the operating setting parameters based on the blood injury data corresponding to multiple groups of operating setting parameter data and the multiple groups of operating setting parameter data.

[0753] In some embodiments, the processing module is further configured to:

[0754] Output parameter setting recommendation information corresponding to the operating setting parameters according to the first variation relationship information and / or the second variation information.

[0755] In some embodiments, the hemodynamic parameter data includes cardiac performance parameter data, and the cardiac performance parameter data includes at least one of the following:

[0756] Native cardiac index data, total cardiac index data, pressure-volume loop data, ventricular elastance data;

[0757] The blood injury data includes at least one of the following:

[0758] Hemolysis index data, coagulation index data, thrombus index data;

[0759] The operating setting parameter data includes at least one of the following:

[0760] Flow rate setting data, rotation speed setting data;

[0761] The physiological parameter data includes at least one of the following:

[0762] Mean arterial pressure data, cardiac output data, blood flow rate data, atrial pressure data.

[0763] In some embodiments, the cardiovascular model is a lumped parameter model, the circuit structure in the lumped parameter model is used to characterize the cardiovascular system, the lumped parameter model includes a charging and discharging circuit corresponding to the heart in the cardiovascular system, and the lumped parameter model and the mechanical circulatory assist model are connected through pressure-flow coupling;

[0764] The processing module is specifically configured to:

[0765] Input the flow rate data into the lumped parameter model to update the current change in the circuit structure;

[0766] Obtain the electrical signal of the charging and discharging circuit;

[0767] Determine the cardiac parameter data based on the electrical signal of the charging and discharging circuit.

[0768] In some embodiments, the charge and discharge circuit includes a variable capacitor for simulating the ventricle;

[0769] Based on the electrical signals of the charge and discharge circuit, cardiac parameter data is determined, including at least one of the following:

[0770] Determine native cardiac output data based on the current data of the variable capacitor;

[0771] Determine the ventricular pressure change data based on the voltage change data of the variable capacitor;

[0772] Determine the ventricular volume change data based on the charge change data of the variable capacitor;

[0773] According to the native cardiac output data and the flow data, determine the total cardiac output data;

[0774] Based on at least one of the ventricular pressure change data and the ventricular volume change data, determine at least one of the pressure-volume loop data and the ventricular elastance data.

[0775] Figure 28 Schematic flow diagram of a performance test method for a mechanical circulatory support device according to an embodiment. The method can be executed by a controller in the mechanical circulatory support device, such as Figure 28 shown, the method includes steps 2801 to step 2803.

[0776] Step 2801: Determine the mechanical circulatory support device to be tested.

[0777] Step 2802: In response to a parameter configuration operation for the mechanical circulatory support device, obtain the parameter configuration data of the mechanical circulatory support device.

[0778] Step 2803: In response to a test instruction for the mechanical circulatory support device, output the virtual clinical trial results corresponding to the mechanical circulatory support device.

[0779] Wherein, the virtual clinical trial results include the prediction results of using the mechanical circulatory support device to assist multiple sample subjects in blood circulation, and the prediction results are used to predict the physiological state changes of the cardiovascular system in the sample subjects when the mechanical circulatory support device works with the parameter configuration data.

[0780] The mechanical circulatory support device to be tested can be determined based on the clinical trial requirements of the user.

[0781] Exemplarily, the mechanical circulatory support device to be tested can be an implantable mechanical circulatory support device or an extracorporeal mechanical circulatory support device. For example, the mechanical circulatory support device to be tested is a left ventricular assist device (LVAD) or a right ventricular assist device (RVAD), or the mechanical circulatory support device to be tested is an extracorporeal ventricular assist device or an extracorporeal membrane oxygenation device.

[0782] Exemplarily, in response to the input of hydrodynamic data, the mechanical circulatory assist device to be tested can be determined, and the hydrodynamic data is used to characterize the hydrodynamic performance of the mechanical circulatory assist device.

[0783] The hydrodynamic data of the mechanical circulatory assist device may include at least one of the following: flow-related data (such as flow distribution data, flow velocity data), pressure-related data, shear force-related data, etc. The flow-related data may include flow distribution data, flow velocity data, etc. The flow distribution data is used to describe the change of blood flow at different positions of the mechanical circulatory assist device, and the flow velocity data is used to describe the flow velocity of blood in the mechanical circulatory assist device. The pressure-related data may include pressure gradient data, peak pressure data, etc. The pressure gradient data is used to describe the pressure change inside the mechanical circulatory assist device, and the peak pressure data is used to indicate the maximum pressure value that the mechanical circulatory assist device may reach during operation. The shear force-related data may include shear stress distribution data, and the shear stress distribution data is used to describe the magnitude and distribution of the shear force exerted on the blood in the mechanical circulatory assist device.

[0784] By determining the mechanical circulatory assist device to be tested in response to the input of hydrodynamic data, the physical characteristics (such as flow characteristics, pressure characteristics) exhibited by the mechanical circulatory assist device when driving blood flow can be fully considered in the virtual clinical trial environment, thereby improving the reliability of the performance test of the mechanical circulatory assist device.

[0785] Another exemplarily, in response to the input operation of the device identifier, the mechanical circulatory assist device to be tested can be determined. The device identifier may be information such as the name and model of the mechanical circulatory assist device.

[0786] Exemplarily, a sample subject can be used to test the performance of the mechanical circulatory assist device in a virtual clinical trial. For example, the sample subject may be a patient with cardiovascular dysfunction, such as a patient with heart failure, arrhythmia or vascular stenosis. The types of disorders, severity, physiological characteristics or age groups of different sample subjects may vary.

[0787] Exemplarily, the parameter configuration operation for the mechanical circulatory assist device may include: selecting or setting parameters such as the flow rate, pump speed, pipeline size and / or connection position of the mechanical circulatory assist device through a user interaction method.

[0788] The parameter configuration data of the mechanical circulatory assist device may include one or more of flow rate setting data, rotational speed setting data, pipeline size information and / or connection position information. It can be understood that for parameters not explicitly set in the parameter configuration data, the default parameter values corresponding to the mechanical circulatory assist device can be used during the prediction process.

[0789] Exemplarily, for the performance test of the same mechanical circulatory assist device, the flow rate setting data, rotational speed setting data, and / or pipeline size information corresponding to multiple sample objects can be completely the same, completely different, or partially the same. The conditions of different sample objects can vary, and the user can configure the above parameter configuration data according to the actual situation.

[0790] Exemplarily, in response to a test instruction, based on the physiological parameter data of the sample object and the parameter configuration data of the mechanical circulatory assist device, the mechanical circulatory assist model corresponding to the mechanical circulatory assist device and the cardiovascular model corresponding to the cardiovascular system can be driven to run jointly, and the prediction result can be output. The mechanical circulatory assist model can be constructed based on the hydrodynamic data of the mechanical circulatory assist device.

[0791] Also exemplarily, in response to a test instruction, the physiological parameter data of the sample object and the parameter configuration data of the mechanical circulatory assist device can be input into an artificial intelligence model to output virtual clinical trial results through the artificial intelligence model.

[0792] The virtual clinical trial results include the prediction results of using the mechanical circulatory assist device to assist multiple sample objects in blood circulation. The prediction results can be used to predict the physiological state changes of the cardiovascular system in the sample object when the mechanical circulatory assist device works with the above parameter configuration data. The physiological state changes can include, for example, but are not limited to, the change trends of one or more cardiovascular parameters such as hemodynamic parameters and / or blood injury parameters.

[0793] Exemplarily, the prediction results can include cardiovascular parameter prediction data, and the cardiovascular parameter prediction data can include the hemodynamic parameter data and / or blood injury data corresponding to each of the multiple sample objects.

[0794] Exemplarily, the virtual clinical trial results can be output in a visual or structured manner such as charts, curves, or numerical lists for reference by users (such as clinicians), so as to provide data support for the applicable object range of the mechanical circulatory assist device.

[0795] Exemplarily, the method can further include: outputting a risk prompt message when the prediction result indicates that the performance of the cardiovascular system of the sample object deteriorates.

[0796] In the above embodiments, after determining the mechanical circulatory assist device to be tested, by responding to a parameter configuration operation for the mechanical circulatory assist device, parameter configuration data of the mechanical circulatory assist device is obtained, enabling a user (such as a clinician) to flexibly configure relevant parameters of the mechanical circulatory assist device according to clinical needs, thereby enhancing the flexibility of performance testing. By responding to a test instruction for the mechanical circulatory assist device, a virtual clinical trial result corresponding to the mechanical circulatory assist device is output. The virtual clinical trial result includes a prediction result of using the mechanical circulatory assist device to assist blood circulation in multiple sample objects, and the prediction result is used to predict the physiological state changes of the cardiovascular system in the sample objects when the mechanical circulatory assist device operates with the parameter configuration data. In this way, it is possible to predict the impact of the mechanical circulatory assist device on the physiological state of the cardiovascular system of different sample objects under the set parameter configuration in a virtual clinical trial test environment.

[0797] Thus, it can be seen that the embodiments of the present application can test the performance of the mechanical circulatory assist device on different sample objects without relying on real clinical operations. This not only helps users understand in advance the performance of the mechanical circulatory assist device in blood circulation, facilitating the evaluation of the safety and effectiveness of the mechanical circulatory assist device and early detection of potential risks of the mechanical circulatory assist device, but also helps users identify in advance the applicable range of objects, thereby improving the safety of mechanical circulatory assist therapy in real clinical applications.

[0798] In some embodiments, the method may further include: outputting a performance evaluation result corresponding to the virtual clinical trial result, where the performance evaluation result is used to characterize the performance of the mechanical circulatory assist device in assisting blood circulation.

[0799] In the above embodiments, by outputting a performance evaluation result corresponding to the virtual clinical trial result, the performance evaluation result can be presented in the form of a performance score value, a performance level, or other quantitative forms, thereby intuitively reflecting the performance of the mechanical circulatory assist device in assisting blood circulation, facilitating the evaluation of the safety and effectiveness of the mechanical circulatory assist device.

[0800] In some embodiments, the method may further include: determining the performance evaluation result according to the virtual clinical trial result.

[0801] Exemplarily, the controller may evaluate the performance of the mechanical circulatory assist device according to the virtual clinical trial results and generate corresponding performance evaluation results. The performance evaluation results may include performance evaluation index data of the mechanical circulatory assist device. The performance evaluation index data may include an average performance score and / or a performance level. The average performance score is the average of the performance scores of the mechanical circulatory assist device corresponding to multiple sample objects. The performance level may be determined by comparing the average performance score with the score range corresponding to the performance level. For example, the prediction results of each sample object may be mapped to performance scores of cardiovascular parameters in multiple dimensions (such as hemodynamic parameters, blood damage) to obtain the performance scores of the mechanical circulatory assist device corresponding to each sample object, and the average of the performance scores of the mechanical circulatory assist device corresponding to each sample object may be calculated to obtain the average performance score.

[0802] In some embodiments, the method may further include: sending the virtual clinical trial results to at least one expert account; receiving the performance evaluation index data returned by the at least one expert account, where the performance evaluation results include the performance evaluation index data. In this way, by having experts evaluate the performance of the mechanical circulatory assist device based on the virtual clinical trial results, with the help of the expertise and experience of the experts, the accuracy of the performance evaluation results of the mechanical circulatory assist device can be improved, thereby providing more reliable data support for clinical decision-making.

[0803] In some embodiments, the parameter configuration data includes connection position information corresponding to each sample object, and the connection position information includes the drainage position and the return position of the mechanical circulatory assist device; the mechanical circulatory assist device includes a power component and a pipeline component, and the parameter configuration data further includes: parameter setting data corresponding to each sample object, where the parameter setting data is used to control the operation of the power component; pipeline size information corresponding to each sample object, where the pipeline size information is used to characterize the geometric size of the pipeline component.

[0804] Exemplarily, the drainage position is the position where the mechanical circulatory assist device extracts blood from the cardiovascular system, and the return position is the position where the mechanical circulatory assist device reinjects the extracted blood (such as pressurized blood) back into the cardiovascular system.

[0805] For example, for an in-vivo mechanical circulatory assist device, the drainage position may be the ventricle (such as the right ventricle or the left ventricle), and the return position may be the artery (such as the pulmonary artery or the aorta). For example, the drainage position of a left ventricular assist device (LVAD) is the left ventricle, and the return position is the aorta; the drainage position of a right ventricular assist device (RVAD) is the right ventricle, and the return position is the pulmonary artery.

[0806] Exemplarily, the power assembly can drive blood to flow into the pipeline assembly from the drainage position and flow out from the return position. The power assembly can include a blood pump or an impeller in the blood pump. The mechanical circulatory assistance device can also include a driving assembly, and the power assembly is driven by the driving assembly to pump blood. The driving assembly can include, for example, an electric motor. The pipeline assembly can be used to connect the mechanical circulatory assistance device and the cardiovascular system in the body of the sample object. The pipeline assembly provides a flow path for blood in the mechanical circulatory assistance device. Blood can flow into one end of the pipeline assembly and, under the drive of the power assembly, flow back to the cardiovascular system from the other end of the pipeline assembly.

[0807] Exemplarily, the parameter setting data can include the flow rate setting data and / or the rotational speed setting data corresponding to the power assembly. The flow rate setting data can indicate the target flow rate output by the mechanical circulatory assistance device (the unit can be liters per minute). The rotational speed setting data can be the pump speed (the unit can be revolutions per minute), such as the pumping speed is set to 1800 revolutions per minute or 2000 revolutions per minute.

[0808] The pipeline size information corresponding to the pipeline assembly can be used to characterize the geometric size of the pipeline assembly.

[0809] It can be understood that the parameter setting data and the pipeline size information corresponding to the pipeline assembly can be constrained by fluid mechanics formulas.

[0810] In the above embodiments, by including the connection position information (the drainage position and the return position of the mechanical circulatory assistance device) corresponding to each sample object, the parameter setting data of the power assembly, and the pipeline size information of the pipeline assembly in the parameter configuration data, the pipeline size information can be used for personalized parameter setting according to the actual pipeline configuration used in clinical practice. In this way, not only the connection position information corresponding to multiple sample objects and the working characteristics of the power assembly are considered, but also the influence of the pipeline size on the fluid transmission performance is fully introduced, so as to more realistically reflect the actual operation effect of the mechanical circulatory assistance device during the process of simulating assisted blood circulation. Thereby, the reliability of the virtual clinical trial results corresponding to the mechanical circulatory assistance device is further improved, and thus the reliability of the performance test is enhanced.

[0811] In order to simulate the parameter dynamic adjustment process of the mechanical circulatory assistance device in actual clinical applications, in some embodiments, the parameter setting data can include: the flow rate setting data corresponding to at least one time period, and / or, the rotational speed setting data corresponding to at least one time period; the prediction result can include the cardiovascular parameter prediction data of the sample object corresponding to the at least one time period.

[0812] Exemplarily, at least one time period may include one or more time units that simulate the operation of a mechanical circulatory assist device, such as each day of five consecutive days. For the at least one time period, the parameter setting data can be personalized for each time period according to clinical needs. For example, it may include the flow rate setting data and rotational speed setting data corresponding to each day. Specifically, different time periods can be set with different operating setting parameters based on changes in the patient's physiological state or treatment strategy. For example, in order to stabilize the blood flow state as soon as possible, the flow rate setting data or rotational speed setting data can be set to a higher value in the first time period (such as the first day); as the patient's blood flow state gradually stabilizes (such as the third day or the fifth day), the flow rate setting data or rotational speed setting data can be appropriately lowered. Correspondingly, the prediction results are also output in units of time periods, for example, including the cardiovascular parameter prediction data for each day.

[0813] Exemplarily, the cardiovascular parameter prediction data may include hemodynamic parameter data and / or blood injury data; wherein, the hemodynamic parameter data includes cardiac performance parameter data, and the cardiac performance parameter data includes at least one of the following: native cardiac index data, total cardiac index data, ventricular pressure-volume loop data, ventricular elastance data; the blood injury data includes at least one of the following: hemolysis index data, coa...

Claims

1. A mechanical circulatory assist device, characterized in that, When in use, the mechanical circulatory assist device is connected and coupled to the cardiovascular system in a target subject, and there are a drainage position and a return position between the mechanical circulatory assist device and the cardiovascular system; The mechanical circulatory assist device includes a control device, a drive assembly, a power assembly, and a pipeline assembly. One end of the pipeline assembly is located at the drainage position, and the other end of the pipeline assembly is located at the return position; The control device is configured to: obtain first parameter setting data corresponding to operating setting parameters, and control the drive assembly to operate based on the first parameter setting data to drive the power assembly to pump blood, wherein the power assembly drives the blood to flow into the pipeline assembly from the drainage position and flow out from the return position; The control device is associated with a user interface, and the user interface is configured to: display cardiovascular parameter monitoring data corresponding to cardiovascular parameters, and / or display data change relationship prediction information between the cardiovascular parameters and the operating setting parameters; wherein, the cardiovascular parameters include at least one of cardiac index, blood injury parameter, pressure-volume loop, and ventricular elasticity; the data change relationship prediction information is used to characterize the data change situation of the cardiovascular parameters when the mechanical circulatory assist device is under different operating setting parameters.

2. The mechanical circulatory assist device according to claim 1, wherein The user interface is further configured to: display parameter setting recommendation information corresponding to the operating setting parameters based on the data change relationship prediction information.

3. The mechanical circulatory assist device according to claim 2, wherein The data change relationship prediction information is presented as a prediction data statistical chart, and the prediction data statistical chart includes cardiovascular parameter data corresponding to respective operating setting parameter data; wherein, the multiple operating setting parameter data include the first parameter setting data and multiple second parameter setting data, the first parameter setting data is the setting data currently corresponding to the operating setting parameters, and the second parameter setting data is different from the first parameter setting data; The multiple cardiovascular parameter data include the cardiovascular parameter monitoring data and multiple cardiovascular parameter prediction data, and the cardiovascular parameter prediction data corresponds to the second parameter setting data one by one.

4. The mechanical circulatory assist device according to claim 3, wherein The parameter setting recommendation information includes recommended setting data corresponding to the operating setting parameters; The recommended setting data is associated with the operating setting parameter data corresponding to target data, and the target data is data that meets the conditions among the multiple cardiovascular parameter prediction data.

5. The mechanical circulatory assist device according to claim 4, wherein The parameter setting recommendation information includes first identification information corresponding to the recommended setting data, and the user interface is further configured to: display second identification information corresponding to the first parameter setting data; In the case where the first identification information and the second identification information indicate that the first parameter setting data does not match the recommended setting data, issue a parameter adjustment prompt information and / or a risk prompt information.

6. The mechanical circulatory assist device according to claim 3, wherein The operating setting parameters include rotational speed, and the data change relationship prediction information includes a first data statistical chart, and the first data statistical chart is used to characterize the data change trend between the cardiac index and the rotational speed; The first data statistical chart includes cardiac index data corresponding to multiple rotational speed setting data respectively; Among them, the multiple rotational speed setting data include a first rotational speed setting data and multiple second rotational speed setting data. The first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data; The multiple cardiac index data include cardiac index monitoring data corresponding to the first rotational speed setting data and multiple cardiac index prediction data, and the multiple cardiac index prediction data correspond to the multiple second rotational speed setting data one by one.

7. The mechanical circulatory assist device according to claim 6, characterized in that, The cardiac index includes a native cardiac index and a total cardiac index. The first data statistical chart includes a first trend line and / or a second trend line. The first trend line is used to characterize the data change trend between the native cardiac index and the rotational speed, and the second trend line is used to characterize the data change trend between the total cardiac index and the rotational speed.

8. The mechanical circulatory assist device according to claim 7, wherein, The blood injury parameter includes a hemolysis index. The first data statistical chart further includes a third trend line, and the third trend line is used to characterize the data change trend between the hemolysis index and the rotational speed.

9. The mechanical circulatory assist device according to claim 8, wherein The first data statistical chart further includes a recommended rotational speed range, and the rotational speed setting data within the recommended rotational speed range satisfies at least one of the following conditions: The native cardiac index data corresponding to the maximum rotational speed setting data within the recommended rotational speed range is greater than a first threshold; The total cardiac index data corresponding to the minimum rotational speed setting data within the recommended rotational speed range is greater than or equal to a second threshold; The maximum rotational speed setting data within the recommended rotational speed range is less than a first rotational speed threshold. The first rotational speed threshold is the rotational speed setting data corresponding to when the native cardiac index in the first trend line is equal to the first threshold, and there is a first difference between the first rotational speed threshold and the maximum rotational speed setting data; Optionally, the first threshold is 0; The hemolysis index data corresponding to the maximum rotational speed setting data within the recommended rotational speed range is less than a third threshold.

10. The mechanical circulatory assist device according to claim 9, wherein, The first parameter setting data includes the first rotational speed setting data, and the user interface is further configured to perform at least one of the following operations: When the first rotational speed setting data does not fall within the recommended rotational speed range, display a rotational speed adjustment prompt message; When the first rotational speed setting data is greater than the maximum rotational speed setting data, display at least one of an over-support risk prompt message, a suction risk message, and a blood injury risk message; When the first rotational speed setting data is less than the minimum rotational speed setting data, display a support-insufficient risk prompt message.

11. The mechanical circulatory assist device according to claim 3, wherein The operation setting parameter includes a rotational speed, and the data change relationship prediction information includes a second data statistical chart, and the second data statistical chart is used to characterize the data change trend between the pressure-volume loop and the rotational speed; The second data statistical chart includes pressure-volume loop curves corresponding to multiple rotational speed setting data respectively, and the pressure-volume loop curves are used to characterize the change relationship between the ventricular pressure corresponding to the ventricle and the ventricular volume; Among them, the multiple rotational speed setting data include first rotational speed setting data and multiple second rotational speed setting data. The first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data; The multiple pressure-volume loop curves include the pressure-volume loop monitoring curve corresponding to the first rotational speed setting data and multiple pressure-volume loop prediction curves. The multiple pressure-volume loop prediction curves correspond one-to-one with the multiple second rotational speed setting data.

12. The mechanical circulatory assist device according to claim 11, wherein, The cardiovascular parameter further includes ventricular elastance, and the second data statistical chart is further configured to characterize the data change trend between the ventricular elastance and the rotational speed; The second data statistical chart includes ventricular elastance lines corresponding to the multiple rotational speed setting data respectively, and the slope of the ventricular elastance line is related to the ventricular elastance; Among them, the multiple rotational speed setting data include first rotational speed setting data and multiple second rotational speed setting data. The first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data; The multiple ventricular elastance lines include the ventricular elastance monitoring line corresponding to the first rotational speed setting data and multiple ventricular elastance prediction lines. The multiple ventricular elastance prediction lines correspond one-to-one with the multiple second rotational speed setting data.

13. The mechanical circulatory assist device according to claim 12, wherein The second data statistical chart further includes recommended rotational speed information, and the rotational speed setting data in the recommended rotational speed information satisfies at least one of the following; The pressure-volume loop curve corresponding to the rotational speed setting data in the recommended rotational speed information satisfies the pressure-volume loop condition; The ventricular elastance line corresponding to the rotational speed setting data in the recommended rotational speed information satisfies the ventricular elastance condition; The user interface is further configured to: When the first rotational speed setting data does not match the recommended rotational speed information, display a rotational speed adjustment prompt message and / or a risk prompt message for indicating ventricular systolic performance.

14. The mechanical circulatory assist device according to claim 3, wherein The operating setting parameter includes rotational speed, the cardiovascular parameter includes ventricular elastance, and the data change relationship prediction information includes a third data statistical chart. The third data statistical chart is configured to characterize the data change trend between the ventricular elastance and the rotational speed; The third data statistical chart includes ventricular elastance data corresponding to the multiple rotational speed setting data respectively; Among them, the multiple rotational speed setting data include first rotational speed setting data and multiple second rotational speed setting data. The first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data; The multiple ventricular elastance data include the ventricular elastance monitoring data corresponding to the first rotational speed setting data and multiple ventricular elastance prediction data. The multiple ventricular elastance prediction data correspond one-to-one with the multiple second rotational speed setting data.

15. The mechanical circulatory assist device according to claim 14, wherein The third data statistical chart further includes recommended rotational speed information, and the ventricular elasticity data corresponding to the rotational speed setting data within the recommended rotational speed information satisfies the ventricular elasticity condition; The user interface is further configured to: When the first rotational speed setting data does not match the recommended rotational speed information, display a rotational speed adjustment prompt message and / or a risk prompt message for indicating ventricular systolic performance.

16. The mechanical circulatory assist device according to claim 3, wherein, The operating setting parameter includes rotational speed, the blood damage parameter includes hemolysis index, the data change relationship prediction information includes a fourth data statistical chart, and the fourth data statistical chart is used to characterize the data change trend between the hemolysis index and the rotational speed. The fourth data statistical chart includes hemolysis index data corresponding to multiple rotational speed setting data respectively; Wherein, the multiple rotational speed setting data includes a first rotational speed setting data and multiple second rotational speed setting data, the first rotational speed setting data is the target rotational speed currently set by the mechanical circulatory assist device, and the first rotational speed setting data is different from the second rotational speed setting data; The multiple hemolysis index data includes the hemolysis index monitoring data corresponding to the first rotational speed setting data and multiple hemolysis index prediction data, and the multiple hemolysis index prediction data correspond to the multiple second rotational speed setting data one by one.

17. The mechanical circulatory assist device according to claim 16, wherein The fourth data statistical chart further includes a recommended rotational speed range, and the hemolysis index data corresponding to the rotational speed setting data within the recommended rotational speed range is lower than a third threshold; The user interface is further configured to perform at least one of the following operations: When the first rotational speed setting data does not fall within the recommended rotational speed range, display a rotational speed adjustment prompt message; When the first rotational speed setting data is greater than the maximum rotational speed setting data of the recommended rotational speed range, display a hemolysis risk prompt message.

18. The mechanical circulatory assist device according to claim 1, wherein The cardiovascular parameter monitoring data is presented as a monitoring data statistical chart, and the monitoring data statistical chart includes the monitoring data corresponding to the cardiovascular parameters at multiple time nodes. The monitoring data statistical chart is used to display the change of the physiological state of the cardiovascular system during the support process of the mechanical circulatory assist device.

19. The mechanical circulatory assist device according to claim 18, wherein The monitoring data statistical chart includes at least one of the following: A cardiac systolic performance statistical chart, which includes ventricular elasticity monitoring data corresponding to multiple time nodes and / or pressure-volume loop monitoring curves corresponding to multiple time nodes. The cardiac systolic performance statistical chart is used to characterize the change of the cardiac systolic performance during the support process of the mechanical circulatory assist device; A cardiac index monitoring data chart, which includes cardiac index monitoring data corresponding to multiple time nodes. The cardiac index monitoring data chart is used to characterize the change of the cardiac output during the support process of the mechanical circulatory assist device; Blood injury monitoring data chart, the blood injury monitoring data chart includes blood injury parameter monitoring data corresponding to a plurality of the time nodes, and the blood injury monitoring data chart is used to characterize the injury condition of the blood during the support process of the mechanical circulatory assist device.

20. The mechanical circulatory assist device according to claim 19, wherein The user interface is further configured to: display a weaning recommendation message when the monitoring data statistical chart indicates the performance recovery of the cardiovascular system; and / or, display a risk prompt message when the monitoring data statistical chart indicates the performance deterioration of the cardiovascular system.

21. The mechanical circulatory assist device according to any one of claims 1 to 20, characterized in that, The control device is further configured to execute: obtain a blood circulation model, and the blood circulation model is used to simulate the blood flow condition after the mechanical circulatory assist device is connected to the cardiovascular system; obtain the physiological parameter data associated with the cardiovascular system, and obtain the operation setting parameter data of the mechanical circulatory assist device; drive the blood circulation model to run based on the physiological parameter data and the operation setting parameter data, and output the cardiovascular parameter monitoring data, and / or, the data change relationship prediction information.

22. The mechanical circulatory assist device according to claim 21, wherein The control device is specifically configured to execute: obtain the cardiovascular model corresponding to the cardiovascular system and the mechanical circulatory assist model corresponding to the mechanical circulatory assist device, and the mechanical circulatory assist model includes a reduced-order model obtained by reducing the order of the computational fluid dynamics model corresponding to the mechanical circulatory assist device; couple the cardiovascular model and the mechanical circulatory assist model according to the drainage position and the return position to obtain a blood circulation model.

23. The mechanical circulatory assist device according to claim 22, wherein The control device is specifically configured to execute: fit the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object; traverse multiple groups of parameter setting data corresponding to the operation setting parameters; when traversing to the current group of parameter setting data, drive the blood circulation model to run based on the model parameter data and the parameter setting data, and output the cardiovascular parameter data corresponding to the cardiovascular system; determine the data change relationship prediction information according to the cardiovascular parameter data corresponding to the multiple groups of parameter setting data.

24. The mechanical circulatory assist device according to claim 22, wherein the cardiovascular model is a lumped parameter model, the circuit structure in the lumped parameter model is used to characterize the cardiovascular system, the lumped parameter model includes a variable capacitor for simulating the ventricle, and the lumped parameter model and the mechanical circulatory assist model are connected through pressure-flow coupling; The control device is specifically configured to execute: fit the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object; drive the cardiovascular model to run based on the model parameter data, and output the pressure gradient data between the drainage position and the return position; input the pressure gradient data and the parameter setting data into the mechanical circulatory assist model to drive the blood circulation model to run, and output the flow data and blood injury data of the mechanical circulatory assist model. Input the flow rate data into the lumped parameter model to update the current change in the circuit structure; Determine the native cardiac output data based on the current data of the variable capacitor; Determine the ventricular pressure change data based on the voltage change data of the variable capacitor; Determine the ventricular volume change data based on the charge change data of the variable capacitor; Determine the total cardiac output data according to the native cardiac output data and the flow rate data; Determine at least one of the pressure-volume loop data and the ventricular elastance data based on the ventricular pressure change data and the ventricular volume change data; Determine the native cardiac index data based on the native cardiac output data; Determine the total cardiac index data based on the total cardiac output data.

25. A data processing method for a mechanical circulatory assist device, characterized in that, The mechanical circulatory assist device is connected and coupled to the cardiovascular system in a target subject during use, and there are a drainage position and a return position between the mechanical circulatory assist device and the cardiovascular system; the mechanical circulatory assist device includes a control device, a drive assembly, a power assembly, and a pipeline assembly, one end of the pipeline assembly is located at the drainage position, and the other end of the pipeline assembly is located at the return position; the method includes: obtaining first parameter setting data corresponding to the operating setting parameters, and controlling the drive assembly to work based on the first parameter setting data to drive the power assembly to pump blood, wherein the power assembly drives the blood to flow into the pipeline assembly from the drainage position and flow out from the return position; the method includes: Display the cardiovascular parameter monitoring data corresponding to the cardiovascular parameters and / or display the data change relationship prediction information between the cardiovascular parameters and the operating setting parameters on the user interface associated with the mechanical circulatory assist device; Wherein, the cardiovascular parameters include at least one of cardiac index, blood injury parameter, pressure-volume loop, and ventricular elastance; the data change relationship prediction information is used to characterize the data change situation of the cardiovascular parameters when the mechanical circulatory assist device is under different operating setting parameters.

26. An electronic device, comprising a processor, a memory, and an executable program stored on the memory and capable of being run by the processor, characterized in that, When the processor runs the executable program, it executes the steps of the mechanical circulatory assist device control method according to claim 25.

27. A storage medium having an executable program stored thereon, characterized in that, When the executable program is executed by the processor, it implements the steps of the mechanical circulatory assist device control method according to claim 25.

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