Performance test method and device of mechanical circulation auxiliary device and electronic equipment

Abstract

The embodiment of the invention provides a performance testing method and device for a mechanical circulation auxiliary device and electronic equipment. The method comprises the following steps: determining a mechanical circulation auxiliary device to be tested; in response to a parameter configuration operation for the mechanical circulation auxiliary device, parameter configuration data of the mechanical circulation auxiliary device are obtained; in response to a test instruction for the mechanical circulation auxiliary device, a virtual clinical test result corresponding to the mechanical circulation auxiliary device is output, and the virtual clinical test result comprises a prediction result obtained by using the mechanical circulation auxiliary device to assist the multiple sample objects in blood circulation, the prediction result is used for predicting the physiological state change condition of the cardiovascular system in the sample object when the mechanical circulation auxiliary device works according to the parameter configuration data. According to the embodiment of the invention, the potential risk of the mechanical circulation auxiliary device can be found in advance on the premise of not depending on real clinical operation.

Description

Technical Field

[0001] The present application relates to the field of medical devices, and in particular to a performance testing method, device and electronic equipment for a mechanical circulatory assist device. Background Art

[0002] With technological advancements, mechanical circulatory support (MCS) has been increasingly used in patients with heart failure. Studies have shown that MCS plays a vital role in various clinical scenarios, including acute cardiogenic shock, severe left ventricular dysfunction, pediatric heart failure, and long-term cardiac support.

[0003] Mechanical circulatory assist devices are specialized medical devices for achieving MCS. There are differences in structural design and applicable populations among mechanical circulatory assist devices. How to detect the potential risks of mechanical circulatory assist devices in advance before actual clinical trials is an urgent issue that needs to be addressed. Summary of the Invention

[0004] In view of this, the embodiments of the present application provide a performance testing method, device and electronic equipment for a mechanical circulatory assist device, which can detect potential risks of the mechanical circulatory assist device in advance without relying on actual clinical operations.

[0005] In a first aspect, an embodiment of the present application provides a performance testing method for a mechanical circulatory assist device, the method comprising: determining a mechanical circulatory assist device to be tested; obtaining parameter configuration data of the mechanical circulatory assist device in response to a parameter configuration operation for the mechanical circulatory assist device; and outputting a virtual clinical trial result corresponding to the mechanical circulatory assist device in response to a test instruction for the mechanical circulatory assist device, the virtual clinical trial result comprising a prediction result of using the mechanical circulatory assist device to assist multiple sample subjects in blood circulation, the prediction result being used to predict changes in the physiological state of the cardiovascular system in the sample subjects when the mechanical circulatory assist device operates with the parameter configuration data.

[0006] In some embodiments, the method further includes: outputting a performance evaluation result corresponding to the virtual clinical trial result, wherein the performance evaluation result is used to characterize the performance of the mechanical circulatory assist device in providing blood circulation assistance.

[0007] In some embodiments, the method further includes: sending the virtual clinical trial result to at least one expert account; and receiving performance evaluation index data returned by the at least one expert account, wherein the performance evaluation result includes the performance evaluation index data.

[0008] In some embodiments, the parameter configuration data includes connection position information corresponding to each of the sample objects, and the connection position information includes the drainage position and reflux 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 also includes: parameter setting data corresponding to each of the sample objects, and the parameter setting data is used to control the operation of the power component; and pipeline size information corresponding to each of the sample objects, and the pipeline size information is used to characterize the geometric dimensions of the pipeline component.

[0009] In some embodiments, the parameter setting data includes: flow setting data corresponding to at least one time period, and / or speed setting data corresponding to at least one time period; the prediction result includes cardiovascular parameter prediction data corresponding to the sample object in the at least one time period, and the cardiovascular parameter prediction data includes hemodynamic parameter data and / or blood damage 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 damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

[0010] In some embodiments, the parameter setting data includes: parameter setting data corresponding to multiple set working conditions of the mechanical circulatory assist device; the prediction results include cardiovascular parameter prediction data corresponding to the sample object under multiple set working conditions, and the cardiovascular parameter prediction data includes hemodynamic parameter data and / or blood damage 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, and ventricular elastance data; the blood damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

[0011] In some embodiments, the method further includes: determining the performance evaluation result based on the virtual clinical trial result.

[0012] In some embodiments, determining the mechanical circulatory assistance device to be tested includes: determining the mechanical circulatory assistance device to be tested in response to input of fluid mechanics data, wherein the fluid mechanics data is used to characterize fluid mechanics performance of the mechanical circulatory assistance device.

[0013] In some embodiments, the method further includes: obtaining physiological parameter data corresponding to the multiple sample objects; outputting the virtual clinical trial results corresponding to the mechanical circulatory assist device in response to the test instruction for the mechanical circulatory assist device, including: starting to traverse the multiple sample objects in response to the test instruction; for the currently traversed sample object, obtaining a mechanical circulatory assist model constructed based on the fluid mechanics data and a cardiovascular model corresponding to the currently traversed sample object; based on the physiological parameter data corresponding to the currently traversed sample object and the parameter configuration data, driving the mechanical circulatory assist model and the cardiovascular model to jointly operate to obtain the prediction results corresponding to the currently traversed sample object; after completing the traversal of the multiple sample objects, outputting the virtual clinical trial results.

[0014] In some embodiments, the physiological parameter data includes at least one of the following: mean arterial pressure data, cardiac output data, blood flow data, and atrial pressure data.

[0015] In some embodiments, the parameter configuration data includes connection position information and parameter setting data of the mechanical circulatory assist device, the connection position information is used to indicate the drainage position and the return position of the mechanical circulatory assist device in the cardiovascular system, and the mechanical circulatory assist model and the cardiovascular model are coupled and connected between the drainage position and the return position based on a pressure-flow relationship; based on the physiological parameter data corresponding to the currently traversed sample object and the parameter configuration data, driving the mechanical circulatory assist model and the cardiovascular model to jointly operate to obtain a prediction result corresponding to the currently traversed sample object includes: fitting model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the currently traversed sample object; driving the cardiovascular model to operate based on the model parameter data to output pressure gradient data between the drainage position and the return position; inputting the pressure gradient data and the parameter setting data into the mechanical circulatory assist model to output flow data and blood damage data of the mechanical circulatory assist model; inputting the flow data into the cardiovascular model to update the operating state of the cardiovascular model and output hemodynamic parameter data. Wherein, the prediction result includes the blood damage data and the hemodynamic parameter data.

[0016] 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, global cardiac index data, ventricular pressure-volume loop data, and ventricular elastance data.

[0017] In some embodiments, the blood damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

[0018] In some embodiments, the mechanical circulation assistance model includes a blood pump model and a pipeline model, the blood pump model corresponds to the power component in the mechanical circulation assistance device, and the pipeline model corresponds to the pipeline component in the mechanical circulation assistance device; the inputting the pressure gradient data and the parameter setting data into the mechanical circulation assistance model, and outputting the flow data and blood damage data of the mechanical circulation assistance model, includes: inputting the flow data between the drainage position and the reflux position into the pipeline model, outputting first pressure loss data and second blood damage data, the first pressure loss data is used to characterize the pressure loss at both ends of the pipeline component, and the second blood damage data is used to characterize the blood damage caused by the pipeline component; determining the pump head pressure difference data corresponding to the power component based on the pressure gradient data and the first pressure loss data; inputting the pump head pressure difference data and the parameter setting data into the blood pump model, outputting the flow data and the first blood damage data corresponding to the power component, the first blood damage data is used to characterize the blood damage caused by the power component; fusing the first blood damage data and the second blood damage data to obtain total blood damage data.

[0019] In some embodiments, the model parameters of the cardiovascular model are fitted according to the physiological parameter data to obtain model parameter data matching the currently traversed sample object, including: in a first time period, according to the physiological parameter data corresponding to the first time period, a first number of model parameters in the cardiovascular model are fitted to obtain first fitting data corresponding to the first number of model parameters in the first time period; wherein the first fitting data is used to drive the cardiovascular model; in a second time period after the first time period, according to the physiological parameter data corresponding to the second time period, a second number of model parameters in the cardiovascular model are fitted to obtain second fitting data corresponding to the second number of model parameters in the second time period, wherein the second fitting data is used to drive the cardiovascular model; the first number and the second number are different.

[0020] In some embodiments, the first number of model parameters includes: left ventricular elastance, systemic peripheral resistance, blood volume, right ventricular end-diastolic stiffness and right ventricular elastance; the second number of model parameters includes: the left ventricular elastance and the systemic peripheral resistance.

[0021] 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, the blood pump model is a reduced-order model obtained by reducing the order of the computational fluid dynamics model corresponding to the power component, and the pipeline model is a reduced-order model obtained by reducing the order of the computational fluid dynamics model corresponding to the pipeline component.

[0022] In a second aspect, an embodiment of the present application provides a performance testing device for a mechanical circulatory assist device, the device comprising a processing module, the processing module being configured to: determine a mechanical circulatory assist device to be tested; obtain parameter configuration data of the mechanical circulatory assist device in response to a parameter configuration operation for the mechanical circulatory assist device; and output virtual clinical trial results corresponding to the mechanical circulatory assist device in response to a test instruction for the mechanical circulatory assist device, the virtual clinical trial results comprising predicted results of using the mechanical circulatory assist device to assist multiple sample subjects in blood circulation, the predicted results being used to predict changes in the physiological state of the cardiovascular system in the sample subjects when the mechanical circulatory assist device operates with the parameter configuration data.

[0023] In a third aspect, an embodiment of the present application provides a mechanical circulatory assist device, which is connected and coupled to the cardiovascular system in the sample subject when in use, and has a drainage position and a reflux position between the mechanical circulatory assist device and the cardiovascular system. The mechanical circulatory assist device includes: a control module, 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 reflux position; the control module is used to control the operation of the drive assembly to drive the power assembly to pump blood, wherein the power assembly drives the blood to flow from the drainage position into the pipeline assembly and out of the reflux position; the control module has a controller, and the controller is used to execute the steps of the performance testing method of the mechanical circulatory assist device as described in any one of the first aspects.

[0024] In a fourth aspect, an embodiment of the present application provides an electronic device comprising a processor, a memory, and an executable program stored in the memory and capable of being run by the processor, wherein when the processor runs the executable program, the processor executes the steps of the performance testing method of the mechanical circulatory assist device as described in any one of the first aspects.

[0025] In a fifth aspect, an embodiment of the present application provides a storage medium having an executable program stored thereon, which, when executed by a processor, implements the steps of the performance testing method of the mechanical circulatory assist device as described in any one of the first aspects.

[0026] The embodiments of the present application provide a performance testing method, device, and electronic device for a mechanical circulatory assist device. After determining the mechanical circulatory assist device to be tested, the parameter configuration data of the mechanical circulatory assist device is obtained by responding to the parameter configuration operation for the mechanical circulatory assist device, so that the user (such as a clinician) can flexibly configure the relevant parameters of the mechanical circulatory assist device according to clinical needs, thereby improving the flexibility of the performance test. By responding to the 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 multiple sample subjects in blood circulation, and the prediction result is used to predict the changes in the physiological state of the cardiovascular system in the sample subject when the mechanical circulatory assist device works 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 subjects under the set parameter configuration in a virtual clinical trial test environment.

[0027] 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. It not only helps users to understand the performance of the mechanical circulatory assist device in assisting blood circulation in advance, so as to evaluate the safety and effectiveness of the mechanical circulatory assist device and discover the potential risks of the mechanical circulatory assist device in advance, but also helps users to identify the scope of its applicable objects in advance, thereby improving the safety of mechanical circulatory assist therapy in real clinical applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 FIG1 is a structural schematic diagram of a mechanical circulatory assist device according to an embodiment;

[0029] Figure 2 FIG2 is a structural schematic diagram of a mechanical circulatory assist device according to an embodiment;

[0030] Figure 3 FIG3 is a structural schematic diagram of a mechanical circulatory assist device according to an embodiment;

[0031] Figure 4 is a schematic flow chart of a method for monitoring cardiovascular system performance according to an embodiment;

[0032] Figure 5 is a schematic flow chart of a method for monitoring cardiovascular system performance according to an embodiment;

[0033] Figure 6 is a schematic flow chart of a method for monitoring cardiovascular system performance according to an embodiment;

[0034] Figure 7 is a schematic flow chart of a method for monitoring cardiovascular system performance according to an embodiment;

[0035] Figure 8 is a schematic structural diagram of a blood circulation model according to an embodiment;

[0036] Figure 9 is a schematic flow chart of a method for monitoring cardiovascular system performance according to an embodiment;

[0037] Figure 10 is a schematic structural diagram of a blood circulation model according to an embodiment;

[0038] Figure 11 is a schematic flow chart of a method for monitoring cardiovascular system performance according to an embodiment;

[0039] Figure 12 is a schematic flow chart of a method for monitoring cardiovascular system performance according to an embodiment;

[0040] Figure 13 FIG1 is a schematic diagram of a mean arterial pressure data curve according to an embodiment.

[0041] Figure 14 FIG1 is one of fitting schematic diagrams shown according to an embodiment.

[0042] Figure 15 FIG2 is a second fitting schematic diagram according to an embodiment.

[0043] Figure 16 FIG3 is a fitting schematic diagram according to an embodiment.

[0044] Figure 17 is a schematic flow chart of a method for monitoring cardiovascular system performance according to an embodiment;

[0045] Figure 18 is a schematic flow chart of a method for monitoring cardiovascular system performance according to an embodiment;

[0046] Figure 19 is a schematic structural diagram of a mechanical circulatory assist device according to an embodiment;

[0047] Figure 20 FIG1 is a schematic diagram of a cardiac index data curve according to an embodiment;

[0048] Figure 21 FIG2 is a second schematic diagram of a cardiac index data curve according to an embodiment;

[0049] Figure 22 FIG1 is a schematic diagram showing a cardiac index data curve and a hemolytic index data curve according to an embodiment;

[0050] Figure 23 FIG2 is a second schematic diagram showing a cardiac index data curve and a hemolytic index data curve according to an embodiment;

[0051] Figure 24 FIG1 is a schematic diagram of a pressure-volume loop curve according to an embodiment;

[0052] Figure 25 FIG2 is a schematic diagram of a pressure-volume loop curve according to an embodiment;

[0053] Figure 26 FIG3 is a schematic diagram of a cardiac index data curve according to an embodiment;

[0054] Figure 27 is a schematic diagram of a hemolysis index data curve according to an embodiment;

[0055] Figure 28 is a flow chart illustrating a performance testing method for a mechanical circulatory assist device according to an embodiment;

[0056] Figure 29 is a flow chart illustrating a method for predicting the effect of a mechanical circulatory assist device on a circulatory assist device according to an embodiment;

[0057] Figure 30 FIG1 is a schematic structural diagram of a device for monitoring cardiovascular system performance according to an embodiment. DETAILED DESCRIPTION

[0058] To make the technical solutions and beneficial effects of the present invention more clearly understood, the following detailed description is given by way of specific embodiments. The accompanying drawings are not necessarily drawn to scale, and local features may be enlarged or reduced to more clearly illustrate 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 field to which this application belongs.

[0059] The embodiments of the present disclosure are not exhaustive and are merely illustrative of some embodiments, and are not intended to be a specific limitation on the scope of protection of the present disclosure. In the absence of contradiction, each step in a certain embodiment can be implemented as an independent embodiment, and the steps can be arbitrarily combined. For example, a solution after removing some steps in a certain embodiment can also be implemented as an independent embodiment, and the order of the steps in a certain embodiment can be arbitrarily exchanged. In addition, the optional implementation methods in a certain embodiment can be arbitrarily combined; in addition, the embodiments can be arbitrarily combined. For example, some or all steps of different embodiments can be arbitrarily combined, and a certain embodiment can be arbitrarily combined with the optional implementation methods of other embodiments.

[0060] In each embodiment of the present disclosure, unless otherwise specified or provided for by logic, the terms and / or descriptions between the embodiments are consistent and can be referenced by each other. The technical features in different embodiments can be combined to form a new embodiment based on their inherent logical relationships.

[0061] The terms used in the embodiments of the present disclosure are only for the purpose of describing specific embodiments and are not intended to limit the present disclosure.

[0062] In the embodiments of the present disclosure, unless otherwise specified, elements expressed in the singular, such as "a", "an", "the", "above", "said", "the", "the", etc., may mean "one and only one", or "one or more", "at least one", etc. For example, when using articles such as "a", "an", "the" in English in translation, the noun following the article may be understood as a singular expression or a plural expression.

[0063] In the embodiments of the present disclosure, “plurality” refers to two or more.

[0064] In some embodiments, the terms "at least one", "one or more", "aplurality of", "multiple", etc. can be used interchangeably.

[0065] In some embodiments, descriptions such as "at least one of A and B," "A and / or B," "in one case A, in another case B," or "in one case A, in another case B" may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed); and in some embodiments, A and B (both A and B are executed). The same applies when there are more branches such as A, B, and C.

[0066] In some embodiments, "A or B" and other descriptions may include the following technical solutions depending on the situation: in some embodiments, A (A is executed independently of B); in some embodiments, B (B is executed independently of A); in some embodiments, execution is selected from A and B (A and B are selectively executed). The above is also applicable when there are more branches such as A, B, C, etc.

[0067] The prefixes such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different description objects and do not constitute any restriction on the position, order, priority, value or content of the description objects. For the statement of the description object, please refer to the description in the context of the claims or embodiments, and no unnecessary restriction should be constituted due to the use of prefixes. For example, if the description object is a "field", the ordinal number before the "field" in the "first field" and the "second field" does 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". For another example, if the description object is a "level", the ordinal number before the "level" in the "first level" and the "second level" does not limit the priority between the "levels". For another example, the value of the description object is not limited by the ordinal number and can be one or more. Taking "first device" as an example, the value of "device" can be one or more. In addition, the objects modified by different prefixes can be the same or different. For example, if the description object is "device", then the "first device" and the "second device" can be the same device or different devices, and their types can be the same or different; for another example, if the description object is "information", then the "first information" and the "second information" can be the same information or different information, and their contents can be the same or different.

[0068] In some embodiments, “including A,” “comprising A,” “used to indicate A,” and “carrying A” can be interpreted as directly carrying A or indirectly indicating A.

[0069] In some embodiments, terms such as "...", "determine...", "in the case of...", "at the time of...", "when...", "if...", "if...", etc. can be used interchangeably.

[0070] 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 less than", and "above" can be replaced 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", and "below" can be replaced with each other.

[0071] In addition, each element, each row, or each column in the table of the embodiment of the present disclosure can be implemented as an independent embodiment, and the combination of any elements, any rows, and any columns can also be implemented as an independent embodiment.

[0072] Mechanical circulatory assist devices can include internal mechanical circulatory assist devices and extracorporeal mechanical circulatory assist devices. The power components of internal mechanical circulatory assist devices, such as the blood pump impeller, are located inside the subject, while the power components of extracorporeal mechanical circulatory assist devices, such as the blood pump impeller, are located outside the subject.

[0073] In some embodiments, an extracorporeal mechanical circulatory assist device 100 (eg, LVAD) is Figure 1 and Figure 2 As shown, it includes a control device 110 , a drive component 120 and a power component 130 .

[0074] The control device 110 can be used for at least one of the following: interacting with a user (e.g., a medical professional), controlling the speed of the power assembly via the drive assembly to provide varying degrees of circulatory assistance, monitoring the status of the power assembly, and monitoring the physiological state of the target subject. For example, the control device 110 can include a controller for controlling the speed and / or flow of the power assembly.

[0075] In one 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 to interact with the user. For example, the user interface can obtain user input information and display interactive information to the user. The user interface can interact with the user through sound, light, electricity, or other means.

[0076] The drive assembly 120 may include a motor, and the power assembly 130 may include a blood pump, wherein the blood pump includes a housing and an impeller (not shown), and 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 through the rotation of the impeller. The blood inlet 131 is used to connect a drainage cannula (not shown), for example, connected to the drainage cannula through a pipeline (not shown) to drain blood from the target object's body. The blood inlet 131 is used to connect a reflux cannula (not shown), for example, connected to the reflux cannula through a pipeline (not shown) to return blood to the target object's body. The above-mentioned drainage cannula, reflux cannula and pipeline can be the pipeline assembly in the extracorporeal mechanical circulatory assist device 100.

[0077] In one possible implementation, the motor can be configured as a magnetic levitation motor. The actuators of the magnetic levitation motor may include a rotational actuator and a levitation actuator. The rotational actuator is used to rotate the impeller through magnetic coupling, while the levitation actuator is used to levitate 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.

[0078] In some embodiments, an in vivo mechanical circulatory assist device 200 is Figure 3 As shown, it includes a control device 210, a drive assembly 220 and a catheter pump 230. The drive assembly 220 includes a housing and a motor located in the housing.

[0079] The catheter pump 230 further includes a coupler, a catheter, a drive shaft, a pump head assembly 231, and a flexible support. When in use, the drive assembly 220 is usually located outside the subject (the subject may be a human body), and the pump head assembly 231 may be inserted into the target subject's body, and the specific installation location may be the left ventricle, for example. Figure 3 As shown, it is used to assist the heart in pumping blood to reduce the burden on the heart. The pump head assembly 231 can assist the left ventricle in pumping blood from the left ventricle to the aorta. Of course, the pump head assembly 231 can also be inserted into other target locations of the subject as desired through interventional surgery. For example, the pump head assembly 231 is inserted into the right ventricle, and the in-body mechanical circulatory assist device 200 is used to assist the right ventricle in working. Furthermore, the pump head assembly can also be inserted into blood vessels or other organs.

[0080] 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 in the subject's body, the flexible support member can guide the insertion of components such as the pump housing. After the pump head assembly 231 and other components are inserted into the desired position in the human body, the flexible support member can maintain the posture of the pump head assembly 231 in the heart during the operation of the in vivo mechanical circulatory assist device, thereby preventing damage to the patient's tissue. In some embodiments, the distal end of the flexible support member is a flexible end that can be supported on the inner wall of the ventricle in a non-invasive or non-destructive manner, separating the blood inlet of the pump head assembly 231 from the inner wall of the ventricle.

[0081] The catheter is a hollow structure, with a drive shaft passing through it. The motor and drive shaft can be driven by magnetic coupling or eddy current coupling. The motor is connected to the catheter and the proximal end of the drive shaft via a coupler and is configured as a power component to provide power. The coupler can be detachably mounted on the drive assembly 220. Typically, the coupler is also provided with an infusion port, through which external infusion fluid can be injected into the catheter to flush or lubricate components such as bearings in the catheter pump.

[0082] When the coupler is engaged with drive assembly 220, the motor's power output couples to the proximal end of the drive shaft, driving the drive shaft to rotate. The distal end of the catheter is connected to pump head assembly 231. Pump head assembly 231 includes a pump housing, a bracket, and an impeller, which is located within the pump housing. The pump housing can be formed of a membrane, serving as a flexible tubing assembly that provides a blood flow path.

[0083] A bracket is provided inside the pump casing. The bracket may be a metal lattice made of an alloy such as nickel or titanium. The metal lattice has a mesh design to facilitate the expansion and folding of the bracket in the radial direction. The pump casing also includes a coating, which is installed on the bracket. The middle part of the bracket is covered by the coating to form a fluid channel, and the area at the distal end of the bracket not covered by the coating forms a blood inlet. The area at the proximal end of the bracket not covered by the coating forms a section of a blood outlet. The bracket also has a distal leg extending from the blood inlet to the distal end. The bracket also has a proximal leg extending from the blood outlet to the proximal end. The distal leg is fixedly connected to the flexible support member and the distal bearing chamber, and the proximal leg is fixedly connected to the proximal bearing chamber and other components.

[0084] Specifically, the impeller is supported inside the bracket and includes a hub fixed on a hard shaft of the drive shaft, and both ends of the hard shaft are supported in the pump housing through a proximal bearing and a distal bearing.

[0085] The hard 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 hard shaft. The proximal end of the flexible shaft is connected to the driven rotor, and the driven rotor is magnetically coupled to the active rotor connected to the power output shaft in the drive assembly 220. The distal end of the flexible shaft is fixedly connected to the hard shaft. The flexible shaft is usually passed through the inside of the catheter to prevent the drive shaft from contacting the outside world. On the one hand, it ensures the normal operation of the drive shaft. On the other hand, it prevents the drive shaft from directly contacting the subject's blood vessels during operation and causing harm to the subject. The hub is connected to the hard shaft. The two ends of the hard shaft are rotatably supported at the two ends of the pump housing. The two ends of the pump housing are connected to 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 hard 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.

[0086] When the catheter pump is in use, the drive shaft passes through the blood vessels outside the body and into the heart. The impeller is inserted into the heart, and the drive shaft is used to drive the impeller to rotate, thereby pumping blood from the heart to the blood vessels. Specifically, the motor in the drive 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 and draws blood from the blood inlet of the pump housing into the pump housing, and then pumps it out from the blood outlet of the pump housing, thereby enabling the pump head assembly 231 to pump and aspirate blood. The above-mentioned impeller assembly can be the power component of a mechanical circulatory assist device.

[0087] The above-mentioned mechanical circulatory assist devices are increasingly used in patients with heart failure, and have even gradually changed from the last resort to save lives to a routine treatment option, covering areas such as acute cardiogenic shock, severe left ventricular dysfunction and long-term cardiac support. Mechanical circulatory assist devices can significantly improve patient survival rates, 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 design differences in mechanical circulatory assist devices, mechanical circulatory assist devices currently lack standardized parameter settings (such as speed and flow), which limits the optimization effect of individualized treatment. Studies have shown that the risk of LVAD-related thrombosis and hemolytic complications can be reduced through individualized parameter setting adjustments. Continuous hemodynamic monitoring helps to customize patient management and can significantly reduce mortality.

[0088] Hemodynamic parameters include cardiac index (CI), pressure-volume loop (PV loop), and ventricular elastance. Monitoring hemodynamic parameters facilitates real-time adjustment of mechanical circulatory assist device settings and optimizes treatment strategies. Hemodynamic parameters can be measured using thermodilution, bioimpedance, PiCCO monitoring systems, and echocardiography, but these methods still face challenges such as high invasiveness and limited accuracy.

[0089] Hemolytic index is also one of the common complications of mechanical circulatory assist devices. Serum lactate dehydrogenase (LDH) monitoring is an important basis for clinical diagnosis, but there is currently a lack of unified and standardized monitoring protocols and warning data.

[0090] During the operation of a mechanical circulatory assist device such as LVAD, in order to achieve better clinical results of the mechanical circulatory assist device, the target subject can be subjected to standardized monitoring of cardiovascular parameters, and the operating setting parameter data of the mechanical circulatory assist device can be adjusted based on the monitoring results. Cardiovascular parameters may include at least one of hemodynamic parameters and blood damage parameters. For example, the cardiovascular parameters monitored during LVAD support may include but are not limited to at least one of the following: blood flow, LVAD blood pump speed, power, pulsatile pressure changes, hemodynamic parameters (such as mean arterial pressure (MAP), cardiac output, central venous pressure, etc.) and / or biochemical markers (coagulation function, inflammatory index). By detecting cardiovascular parameters and adjusting the operating setting parameter data of the mechanical circulatory assist 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 total medical costs. Among cardiovascular parameters, hemodynamic parameters play a crucial role and can include at least one of the following: ventricular elastance changes, pressure-volume (PV) loop, and cardiac index (CI). Hemodynamic parameters provide real-time insights into cardiac function, enabling timely adjustments to MCS operating parameters to optimize treatment strategies and improve patient outcomes.

[0091] Cardiac index can be derived using thermodilution, Doppler echocardiography, or impedance cardiography (ICG), whereas PV loop and ventricular elastance can be obtained by invasive catheterization or estimated by echocardiographic measurements combined with pressure recordings. Therefore, these monitors have the disadvantages of being highly invasive or having suboptimal accuracy.

[0092] The impellers of mechanical circulatory assistance devices and parts that come into contact with blood can damage red blood cells in the blood, which is also known as hemolysis. Cardiovascular parameters also include the hemolysis index. Mechanical circulatory assistance devices such as LVADs can affect the blood during operation, such as hemolysis. Therefore, continuous monitoring of the hemolysis index during the use of mechanical circulatory assistance devices also has clinical benefits. The hemolysis index can also be used to provide data for adjusting the operating setting parameters of mechanical circulatory assistance devices to reduce the incidence of complications. Similarly, there is a lack of low-invasive or high-accuracy detection methods for the hemolysis index.

[0093] Here, before describing the implementation methods of the present application, the research contents and definitions involved in the embodiments of the present application are explained.

[0094] To achieve low-invasive, accurate, and real-time hemodynamic assessment, the technical solution provided in this application establishes a digital twin model to simulate the complete human circulatory interaction. A zero-dimensional lumped parameter model (LPM), a three-dimensional computational fluid dynamics (CFD) model, and a neural network approach can be used to simulate human blood circulation. Each of these models can be developed to meet different clinical needs.

[0095] With the increase in the amount of available clinical data and the advancement of modeling technology, integrating multiple complex factors into cardiovascular models is also a highly promising direction that deserves in-depth exploration. LPM has the potential for clinical application. For example, LPM can simulate heart failure and Fontan circulation with an error of less than 5%. LPM can be used to explore the effects of different ventricular assist devices (VAD) and veno-arterial extracorporeal membrane oxygenation (VA-ECMO) connection methods on hemodynamics; the cardiopulmonary simulator that integrates ECMO simulation has been verified through experimental data to have an error control within 17.6%. The above data confirm the practical potential and development prospects of LPM as a clinical decision-making tool.

[0096] Despite this, LPM still faces several challenges. The parameters of LPM are mostly calibrated by experimental or clinical data. There are differences in the parameters used between different studies, which affects the comparability of the results; and when clinical data are limited, the difficulty of parameter fitting also increases. In addition, due to the lack of spatial resolution capability of LPM, it is impossible to simulate the shear stress field, which limits its application in the prediction of phenomena such as hemolysis. To overcome this limitation, the zero-dimensional LPM can be combined with a three-dimensional model (such as a CFD model or a finite element method (FEM) model) to introduce the internal flow field information of the mechanical circulatory assist device to construct a more complete and accurate simulation framework.

[0097] Traditional CFD calculations often take 12 to 24 hours, making it difficult to support real-time clinical decision-making. The disclosed embodiments utilize reduced order model (ROM) technology to shorten simulation time, for example, to under 15 minutes. Further integration with LPM can achieve a response within seconds, with an accuracy of 1–10%, while preserving flow field information and shear stress analysis results within mechanical circulatory assist devices. Individualized simulations can accurately fit multiple physiological parameters (such as ventricular elastance, vascular resistance, and compliance). A variety of algorithms can be used for parameter fitting optimization, including Markov chain Monte Carlo (MCMC) methods, genetic algorithms (GAs), simulated annealing (SA), and Bayesian optimization (BO). These algorithms facilitate automated fitting. Furthermore, sensitivity analysis and parameter subset reduction can improve model identifiability and reduce computational burden. Currently, clinical practice lacks a low-invasive tool for continuous monitoring of hemodynamics and hemolysis risk.

[0098] In view of this, this application proposes a digital model framework that integrates a lumped parameter model and a reduced-order model, which can be individually configured according to different patient conditions and mechanical circulatory assist device support modes, and combined with an automated calibration algorithm to reduce the complexity of parameter setting, thereby achieving fast and accurate hemodynamic simulation, pressure-volume conversion reconstruction, and hemolysis risk estimation.

[0099] It is hoped that through standardized and real-time monitoring, the clinical management strategy of MCS can be optimized, thereby improving patient prognosis.

[0100] Example 1

[0101] Please refer to Figure 4 , Figure 4 A method for monitoring cardiovascular system performance provided by an embodiment of the present application is shown. Figure 4 As shown, methods for monitoring cardiovascular system performance may include:

[0102] Step 401: Obtain a cardiovascular model corresponding to the cardiovascular system in the target subject and a mechanical circulatory assist model corresponding to the mechanical circulatory assist device.

[0103] 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.

[0104] Here, the method for monitoring cardiovascular system performance may be executed by a control device of a mechanical circulatory assist device.

[0105] The target subject may be a patient with cardiovascular defects or cardiovascular disease requiring circulatory support with a mechanical circulatory assist device. The target subject may also be a laboratory animal or a human dummy. For example, the target subject may be a patient with cardiovascular defects, or a laboratory animal or human dummy with cardiovascular defects.

[0106] The cardiovascular model may include an equivalent model of the target subject's cardiovascular system. The cardiovascular model's model structure matches the target subject's cardiovascular structure. The cardiovascular model may be equivalent to the target subject's entire cardiovascular system, or the cardiovascular model may be equivalent to a portion of the target subject's cardiovascular system.

[0107] In one possible implementation, the cardiovascular model may include multiple model units. Each model unit may be used to simulate one or more organs / tissues in the cardiovascular system of the target subject. For example, the cardiovascular model may include a model unit for simulating the pulmonary circulation, a model unit for simulating the systemic circulation, a model unit for simulating the left coronary circulation, a model unit for simulating the right coronary circulation, a model unit for simulating the left heart, and a model unit for simulating the right heart.

[0108] In one possible implementation, the cardiovascular model may adopt a lumped parameter model to simulate the interaction between organs, vascular systems, and blood flow.

[0109] Here, the mechanical circulatory assist device may include an intracorporeal mechanical circulatory assist device and an extracorporeal mechanical circulatory assist device.

[0110] A mechanical circulatory assist model may be used to simulate a mechanical circulatory assist device, such as a left ventricular assist device and / or a right ventricular assist device.

[0111] Here, a mechanical circulatory assist device may include an active portion and a passive portion. The active portion may include power-generating components such as a blood pump. The passive portion may include components such as cannulae and tubing through which blood flows. In one possible implementation, a mechanical circulatory assist model may be used to simulate at least one component of the mechanical circulatory assist device.

[0112] Mechanical loop-assisted models can be implemented using reduced-order models (ROMs). ROMs are derived from high-dimensional models of complex systems (such as partial differential equations and high-degree-of-freedom systems) through mathematical or physical simplification. ROMs can preserve the key dynamic characteristics of high-dimensional models while significantly reducing computational costs. ROMs enable rapid simulation, real-time control, and / or parameter optimization while maintaining sufficient accuracy.

[0113] Specifically, the mechanical circulatory assist device can be analyzed based on computational fluid dynamics (CFD) to determine a high-dimensional computational fluid dynamics model of the mechanical circulatory assist device, and then the computational fluid dynamics model can be simplified and reduced to obtain a reduced-order model for simulating the mechanical circulatory assist device based on computational fluid dynamics.

[0114] Mechanical circulatory assist devices may include components such as blood pumps, cannulas, and / or catheters. All components of a mechanical circulatory assist device can be simulated using a single reduced-order model. Alternatively, multiple reduced-order models can be used to simulate multiple components.

[0115] In one possible implementation, the mechanical circulatory assist model simulating the mechanical circulatory assist device may include predicting the operating results of the mechanical circulatory assist device using the mechanical circulatory assist model. For example, the reduced-order model may predict a hemolytic index and / or output flow data of the mechanical circulatory assist device.

[0116] 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. ROM can capture the flow field details of the mechanical circulatory assist device, and 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 analytical 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 properties related to the mechanical circulatory assist device. Compared with the computational fluid dynamics model, the reduced-order model can compress the calculation amount by 4 to 6 orders of magnitude, and only calculates the 5% to 10% of modes that have the greatest impact on the system behavior, thereby improving the calculation efficiency of the reduced-order model and shortening the calculation time.

[0117] Step 402: Acquire the drainage position and the return flow position of the mechanical circulatory assist device in the cardiovascular system.

[0118] A mechanical circulatory assist device may be an internal left ventricular assist device with its working components (e.g., a blood pump impeller) located within the subject's body, or an external left ventricular assist device with its working components (e.g., a blood pump) located outside the subject's body. A right ventricular assist device may be an internal right ventricular assist device with its working components (e.g., a blood pump impeller) located within the subject's body, or an external right ventricular assist device with its working components (e.g., a blood pump) located outside the subject's body.

[0119] 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.

[0120] For example, for an intracorporeal ventricular assist device, the drainage site may be a ventricle (such as the right ventricle or the left ventricle), and the return site may be an artery (such as the pulmonary artery or the aorta). For an extracorporeal ventricular assist device, the drainage site may be an atrium (such as the right atrium or the left atrium), and the return site may be an artery (such as the pulmonary artery or the aorta).

[0121] The drainage position and the reflux position can be determined based on the actual connection between the mechanical circulatory assistance device and the target object. When used by the user, the drainage position and the reflux position can be input into the mechanical circulatory assistance device.

[0122] Step 403: The cardiovascular model and the mechanical circulation assistance model are coupled according to the drainage position and the backflow position to obtain a blood circulation model.

[0123] The blood circulation model is used to simulate the blood flow after the mechanical circulatory assist device is connected to the cardiovascular system.

[0124] The cardiovascular model is used to simulate the cardiovascular system; therefore, the drainage locations and return locations in the cardiovascular system have corresponding locations in the cardiovascular model.

[0125] The mechanical circulatory assist model is used to simulate mechanical circulatory assist devices, such as ventricular assist devices. Therefore, the reduced-price model also has a blood input and a blood output. The blood input of the reduced-price model can be connected to the location corresponding to the drainage location in the cardiovascular model, and the blood output of the reduced-price model can be connected to the location corresponding to the return location in the cardiovascular model. This simulates the connection between the ventricular assist device and the target object.

[0126] Figure 5 As shown, after determining the mechanical circulatory assistance model and cardiovascular model corresponding to the mechanical circulatory assistance device, a blood circulation model can be generated to simulate the target subject's mechanical circulatory assistance using the mechanical circulatory assistance device. Specifically, the corresponding coupling position on the cardiovascular model can be determined based on the actual connection position of the mechanical circulatory assistance device installed on the target subject during operation, and the mechanical circulatory assistance model can be connected at the coupling position on the cardiovascular model.

[0127] The blood circulation model can simulate the blood circulation of a target object assisted by a mechanical circulatory assistance device.

[0128] 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 converted and then transmitted. 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.

[0129] Step 404: Acquire physiological parameter data associated with the cardiovascular system, and acquire operational setting parameter data of the mechanical circulatory assist device.

[0130] In one possible implementation, the physiological parameter data of the target object may be obtained by monitoring the physiological state of the target object. The physiological parameter data may be a specific value of the physiological parameter. For example, the physiological parameter includes heart rate. The physiological parameter data includes a heart rate value such as 70.

[0131] Physiological parameter data may include: mean arterial pressure data, cardiac output data, blood flow data, and atrial pressure data.

[0132] The mechanical circulatory assist model is used to simulate a mechanical circulatory assist device. During operation, the mechanical circulatory assist device has its own operating parameter data. The mechanical circulatory assist model can load this operating parameter data to simulate the device's actual operating state. This operating parameter data includes the values set for the operating parameters.

[0133] In some embodiments, the operation setting parameter data includes at least one of the following: flow setting data, speed setting data.

[0134] The user can adjust the mechanical circulation assist by setting the flow data or speed setting data of the mechanical circulation assist device.

[0135] In one possible implementation, the speed setting data includes a target speed set by the user for the circulatory assistance device. It is understandable that the speed setting data can be a target speed of the impeller or a target speed of the motor.

[0136] The flow rate setting data includes the target flow rate set by the user for the circulatory assistance device. It is understandable 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 to maintain the whole body blood circulation of the target subject.

[0137] The operating setting parameter data may be input by a user (eg, medical personnel), such as selecting from a plurality of operating setting parameter data supported by the mechanical circulatory assist device. The operating setting parameter data may also adopt default operating setting parameter data.

[0138] Step 405: driving the blood circulation model to run based on the physiological parameter data and the operation setting parameter data, and outputting at least one of the hemodynamic parameter data and blood damage data corresponding to the cardiovascular system.

[0139] The cardiovascular model may be fitted based on 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.

[0140] After obtaining the cardiovascular model and the mechanical circulatory assistance model with loaded operating parameter data, the blood circulation assistance provided by the mechanical circulatory assistance device to the target subject can be simulated based on the blood circulation model formed by combining 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 subject, 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 provided by the mechanical circulatory assistance device to the target subject is simulated to obtain hemodynamic parameter data.

[0141] Cardiovascular models can be used to simulate the cardiovascular system. For example, lumped parameter models offer high computational efficiency and practical value in cardiovascular simulations. However, lumped parameter models lack spatial resolution and cannot simulate shear stress fields, limiting their application in predicting phenomena such as hemolysis. Mechanical circulatory assist models can simulate the internal flow and shear stress fields of mechanical circulatory assist devices, thereby simulating shear stress on red blood cells, predicting blood damage, and generating blood damage data.

[0142] The hemodynamic parameter data may be data values for a hemodynamic parameter.

[0143] In a possible implementation, the blood damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

[0144] 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, global cardiac index data, pressure-volume loop data, and ventricular elastance data.

[0145] The hemodynamic parameter data may be a specific value of a hemodynamic parameter. The cardiac performance parameter data may be a specific value representing a cardiac performance parameter.

[0146] In one possible implementation, the native cardiac index may be determined based on the cardiac output of the native heart. Specifically, the native cardiac index may include the quotient of the native cardiac output divided by the body surface area.

[0147] In one possible implementation, a total cardiac index can be determined based on total cardiac output data. Specifically, the total cardiac index can be the quotient of total cardiac output data divided by body surface area. The cardiac output of the native heart combined with the blood output (flow data) of the mechanical circulatory assist device can be used to obtain total cardiac output data for the subject while the mechanical circulatory assist device is assisting the subject.

[0148] In one possible implementation, the blood circulation model may be provided in a processing device external to the mechanical circulatory assist device and executed within the processing device. It is understood that the mechanical circulatory assist device and the processing device external to the mechanical circulatory assist device, when performing the prediction and display of at least one of hemodynamic parameter data and blood injury data, as a single interconnected system, may also be collectively referred to as the mechanical circulatory assist device.

[0149] In the technical solution provided in the present application, the cardiovascular model is used to simulate the blood circulation of the cardiovascular system in the target object, and the mechanical circulatory assistance model is used to simulate the flow field changes of the mechanical circulatory assistance device. By obtaining the connection position of the target object to the mechanical circulatory assistance device, the mechanical circulatory assistance model and the cardiovascular model are coupled to obtain the blood circulation model, which can simulate the blood circulation situation after the target object is connected to the mechanical circulatory assistance device. After obtaining the physiological parameter data of the target object and the operation setting parameter data of the mechanical circulatory assistance device, the blood circulation model can be driven to operate based on the physiological parameter data and the operation setting parameter data, so as to realize individualized configuration according to different patient conditions and the support mode of the mechanical circulatory assistance model.

[0150] Since the above-mentioned 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, 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 reduced-order model and coupling it with the cardiovascular model, it can not only simulate the blood circulation of the target object, but also simulate the blood damage caused by the mechanical circulatory assistance device when it is working, and then accurately output the hemodynamic parameter data and / or blood damage data of the current object in the current support mode, so that medical staff can observe the blood circulation and / or blood damage of the target object without performing invasive examinations, and reduce damage to the target object.

[0151] 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, which requires 12 to 24 hours of calculation time, the reduced-order model can shorten the simulation time to within 15 minutes. By coupling it with the cardiovascular model, it can achieve a response in seconds and maintain high accuracy, and can continuously and in real time output the above-mentioned 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.

[0152] After the blood circulation model is built, the cardiovascular model and the mechanical circulatory assistance model are integrated together. The two models can be linked together through pressure-flow coupling. The specific method for the interaction between the cardiovascular model and the mechanical circulatory assistance model is as follows.

[0153] In some embodiments, as Figure 6 As shown, step 405 may include:

[0154] 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.

[0155] Step 602: Input the pressure gradient data and the operation setting parameter data into the mechanical circulation assistance model to drive the blood circulation model to operate and output at least one of the hemodynamic parameter data and blood damage data corresponding to the cardiovascular system.

[0156] The cardiovascular model can be run based on physiological parameter data to simulate the real hemodynamics of the target object, and the pressure gradient data between the drainage location and the return location can be obtained.

[0157] 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 may include coupling of pressure gradient data. The blood pressure difference between the drainage and return points of the target subject will affect the flow field within the mechanical circulatory assistance device. The mechanical circulatory assistance model can simulate the operation of the mechanical circulatory assistance device based on the operating 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 blood flow data to the cardiovascular model, thereby simulating the mechanical circulatory assistance device assisting the target subject's blood circulation, and further predicting at least one of the hemodynamic parameter data and blood damage data.

[0158] The cardiovascular model simulates the target subject using physiological parameter data. The mechanical circulatory assist model simulates the actual operating conditions of the mechanical circulatory assist device based on the pressure gradient data determined by the cardiovascular model and the operating parameter data of the mechanical circulatory assist device. The resulting blood circulation model can accurately simulate the mechanical circulatory assist device's blood circulation assistance to the target subject, achieving individualized configuration and thereby improving the accuracy of the predicted hemodynamic parameter data and / or blood damage data of the target subject under the mechanical circulatory assist device. At the same time, it can reduce the need for invasive testing to determine the hemolytic index and assisted blood output flow, thereby reducing harm to the target subject.

[0159] After the cardiovascular model outputs the pressure gradient data, the specific workflow of the blood circulation model is as follows. In some embodiments, Figure 7 As shown, step 602 may include:

[0160] Step 701: Input pressure gradient data and operation setting parameter data into the mechanical circulation assistance model to drive the blood circulation model to operate, and output flow data and blood damage data of the mechanical circulation assistance model.

[0161] Step 702: Input the flow data into the cardiovascular model to update the operating state of the cardiovascular model and output hemodynamic parameter data.

[0162] 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 simulating the target object by the cardiovascular model. The mechanical circulatory assistance model simulates the mechanical circulatory assistance device based on the pressure gradient data and the operating setting parameter data of the mechanical circulatory assistance model (such as the blood pump speed). The mechanical circulatory assistance device has two important indicators in blood circulation assistance: flow data and blood damage data. The flow 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 flow data and blood damage data. The flow data is the blood output of the mechanical circulatory assistance device, that is, the blood return volume returning to the return position. Therefore, the flow data can be input into the cardiovascular model, and the cardiovascular model can simulate the blood flow input by the mechanical circulatory assistance device at the return position based on the flow data, thereby realizing the coupling between the cardiovascular model and the mechanical circulatory assistance model.

[0163] The cardiovascular model can simulate the hemodynamics of the cardiovascular system based on the updated flow data, thereby achieving the update of hemodynamic parameter data.

[0164] By 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 subject, the target subject's blood circulation can be accurately simulated using the mechanical circulatory assistance device. The mechanical circulatory assistance model accurately simulates the actual operation of the mechanical circulatory assistance device based on the pressure gradient data between the drainage position and the return position and the operating setting parameter data, thereby improving the accuracy of the predicted hemodynamic parameter data and blood damage data of the target subject 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. This can reduce the need for invasive testing to determine the hemolytic index and assisted output blood flow, thereby reducing harm to the target subject.

[0165] A mechanical circulatory assist device can be composed of multiple components, and its internal working process is as follows.

[0166] 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 the first pressure loss data at both ends of the first pipeline component based on the flow data.

[0167] Step 702 may include:

[0168] The pump head pressure difference data corresponding to the power assembly is determined based on the pressure gradient data and the first pressure loss data.

[0169] The pump head pressure difference data and operation setting parameter data are input into the blood pump reduced-order model to drive the blood circulation model to run, and the flow data and the first blood damage data corresponding to the blood pump reduced-order model are output. The first blood damage data is used to characterize the blood damage caused by the power component.

[0170] Here, the power component may be a component that pressurizes the blood flow introduced from the drainage site. For example, the power component may include a blood pump.

[0171] During operation, a mechanical circulatory assist device introduces blood into the bloodstream from a drainage location of a target subject, pressurizes blood with a power assembly, and then returns blood to a return location. Therefore, the mechanical circulatory assist device may include a power assembly and a first conduit assembly for introducing and / or returning blood.

[0172] Here, a blood pump reduced-order model and a pipeline reduced-order model can be separately configured for components of the mechanical circulatory assist device that involve blood circulation and may cause hemolysis, such as the power assembly and the first pipeline assembly. This can improve the realism of the simulation of the mechanical circulatory assist device. The power assembly is used to pressurize the blood, so the blood pump produces changes in blood flow. At the same time, the mechanical movement of the blood pump also causes damage to the blood. Blood flowing through the first pipeline assembly will cause pressure loss.

[0173] For example, computational fluid dynamics numerical simulations can be performed on the blood pump and the first pipeline assembly. Steady-state flow analysis can be performed based on the k-ωSST turbulence model, and a non-Newtonian fluid model can be used to characterize blood rheological properties, thereby determining a reduced-order model for the blood pump and the pipeline.

[0174] The blood pump reduced-order model is obtained by reducing the order of a model obtained by computational fluid dynamics analysis of the blood pump. The pipeline reduced-order model is obtained by reducing the order of a model obtained by computational fluid dynamics analysis of the first pipeline component.

[0175] The blood pump reduced-order model simulates the blood pump driving all the blood in the mechanical circulatory assist device to flow. The pump head pressure differential data borne by the blood pump can be determined based on the pressure gradient data and the first pressure loss data.

[0176] 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 operation 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 data and the first blood damage data. Among them, the flow data of the blood pump is also the flow data of the mechanical circulatory assist device. By determining the first pressure loss data, the actual pump head pressure difference data at both ends of the power component can be determined more accurately, thereby improving the prediction accuracy of the blood pump reduced-order model.

[0177] The pipeline reduced-order model is used to simulate first pressure loss data generated when the blood flow passes through the first pipeline component.

[0178] In this application, in order to effectively predict the risk of hemolysis under a 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 steady-state conditions and the k–ωSST turbulence model, and the blood rheological characteristics can be constructed based on the non-Newtonian model. The pump body geometry of the blood pump is established by CAD data. For example, an extracorporeal left ventricular assist device (LVAD) can be used as the blood pump used in the study, and the space is discretized using an unstructured tetrahedral mesh and up to 12 layers of wall-attached prismatic elements. The total number of elements is approximately 17.5 million, and the average y value of all surfaces is 2.5. +The value is less than 1. A moving reference frame was used to simulate the interaction between the rotor and stator. A total of 30 operating conditions were simulated, covering flow rate settings Q = [0.5–5] L / min and speed settings n = [1500–3000] rpm, using Latin hypercube sampling.

[0179] The simulation covers four catheter sizes (19, 23, 28, and 32 Fr), with a flow rate range of 0.5 L / min to 5 L / min. The catheter geometry uses a simplified vascular structure, including the catheter tip and side port configuration. The inter-dimensional discretization uses an unstructured tetrahedral mesh with near-wall prismatic densification. The total number of elements ranges from approximately 1.18 million to 2.2 million, depending on the catheter size.

[0180] The modified index of hemolysis (MIH) was numerically predicted using the Euler method. This method calculates the blood damage source term by linearizing the transport equation and integrating the entire computational domain. The hemolysis model applies a power-order relationship between exposure time and shear stress to the experimental data, using the parameter C = 1.745 × 10 -6 , α=1.963, β=0.7762.

[0181] The hemolysis reduction model for the pump (blood pump reduced-order model) is constructed using a non-invasive polynomial chaos expansion (PCE) method, providing continuous predictions within the training data range. Predictions for the pressure drop and hemolysis index of the tubing are constructed using a combined polynomial approach, effectively describing the discrete variations brought about by different catheter sizes. The ROM outputs include MIH (hemolysis index), PD (pressure drop), Q (flow rate), and D (tubing size, Fr). Fitting parameters are used to describe the dependence of flow rate and size on the predicted indicators. The blood pump reduced-order model and the tubing reduced-order model, which comprise the mechanical circulatory assist model, simulate the power component and the first tubing component of the mechanical circulatory assist device, respectively, to simulate the output parameters of each component in the device. This improves the realism of the mechanical circulatory assist model in simulating the device, thereby enhancing the accuracy of the parameters output by the reduced-order model. Furthermore, the use of the blood pump reduced-order model and the tubing reduced-order model improves model efficiency and shortens prediction time while maintaining prediction accuracy.

[0182] In some embodiments, the method for monitoring cardiovascular system performance further includes: obtaining first size information corresponding to the first pipeline component; and fitting a pipeline reduced-order model based on the first size information.

[0183] Specifically, the size of the first tubing assembly affects the pressure drop of blood flowing through the first tubing assembly and is also associated with the damage caused by the first tubing assembly to the blood.

[0184] Here, the first size information may be determined based on a first pipeline assembly actually used in the mechanical circulatory assistance device.

[0185] In a possible implementation, the first size information is used to indicate at least one of the following items of the first pipeline assembly: a diameter of the first pipeline assembly, a length of the first pipeline assembly, and a cross-sectional shape of the first pipeline assembly.

[0186] The pipeline reduced-order model can determine first pressure loss data and damage to blood in combination with the first size information.

[0187] By introducing the first dimension information, the pipeline reduced-order model can more accurately simulate the first pipeline component, thereby improving the accuracy of the pipeline reduced-order model prediction.

[0188] In some embodiments, step 701 may include:

[0189] The flow data is input into the pipeline reduction model for processing, and first pressure loss data and second blood damage data corresponding to the first pipeline component are output. The second blood damage data is used to characterize the blood damage caused by the first pipeline component.

[0190] Specifically, the size of the first pipeline assembly affects the pressure drop of blood flowing through the first pipeline assembly. The size of the first pipeline assembly is also associated with the damage caused to the blood by the first pipeline assembly. Therefore, a pipeline reduced-order model can be used to predict the first pressure loss data and the second blood damage data.

[0191] Specifically, the pipeline reduced-order model can simulate first pressure loss data and second blood damage data of the first pipeline component when blood flows through the first pipeline component based on the flow data and the first size information.

[0192] By introducing the first dimension information, the pipeline reduced-order model can more accurately simulate the first pipeline component, thereby improving the accuracy of the pipeline reduced-order model in predicting the first pressure loss data and the second blood damage data.

[0193] The damage to blood caused by a mechanical circulatory assistance device includes the damage to blood caused by all components within the mechanical circulatory assistance device. Therefore, the total data on blood damage caused by the mechanical circulatory assistance device is determined in the following manner.

[0194] In some embodiments, step 701 may include:

[0195] The first blood damage data and the second blood damage data are fused to obtain total blood damage data.

[0196] Multiple components within a mechanical circulatory assist device may cause blood damage, such as the power assembly and first tubing assembly, which may cause hemolysis. Therefore, total blood damage data for the mechanical circulatory assist device can be derived based on the blood damage data for these multiple components. By combining the first and second blood damage data, the total amount of blood damage for each component of the mechanical circulatory assist device can be realistically simulated, thereby improving the accuracy of the determined total blood damage data.

[0197] In some embodiments, the mechanical circulation assist device also includes a second pipeline assembly, and the mechanical circulation assist model also includes a linear pipeline model corresponding to the second pipeline assembly; the method also includes: obtaining second dimension information corresponding to the second pipeline assembly; fitting the linear pipeline model based on the second dimension information; inputting the flow data into the linear pipeline model for processing, and outputting the second pressure loss data at both ends of the second pipeline assembly; determining the pump head pressure difference data corresponding to the power assembly based on the pressure gradient data, the first pressure loss data and the second pressure loss data.

[0198] Here, a second pipeline assembly may be further provided between the power assembly and the first pipeline assembly to facilitate adjustment of the distance between the power assembly and the target object.

[0199] During the operation of the mechanical circulatory assist device, blood needs to flow between the second pipe components, which will also cause pressure loss.

[0200] Here, a linear pipeline model can be set for each of the second pipeline components, thereby improving the authenticity of the simulation of the mechanical circulatory assist device. The linear pipeline model can be used to simulate the pressure loss generated by the second pipeline component.

[0201] For example, the linear pipeline model can be modeled based on the Darcy-Weisbach formula, which describes the quadratic relationship between flow pressure drop, flow rate (ie, auxiliary output blood flow) and pipeline geometry.

[0202] Specifically, the size of the second tubing assembly will affect the pressure drop of blood flowing through the second tubing assembly. Here, the second size information can be determined based on the second tubing assembly actually used in the mechanical circulatory assist device.

[0203] In a possible implementation, the second size information is used to indicate at least one of the following items of the second pipeline assembly: a diameter of the second pipeline assembly, a length of the second pipeline assembly, and a cross-sectional shape of the second pipeline assembly.

[0204] Specifically, the size of the second pipeline assembly will affect the pressure drop of blood flowing through the second pipeline assembly. Therefore, a linear pipeline model can be used to predict the second pressure loss data.

[0205] Specifically, the linear pipeline model can simulate the second pressure loss data generated when the blood flows through the second pipeline component based on the flow data and the second size information.

[0206] By introducing the second dimension information, the linear pipeline model can simulate the second pipeline component more accurately, thereby improving the accuracy of the pipeline reduced-order model in predicting the second pressure loss data.

[0207] Specifically, the pump head pressure difference data can be the sum of the pressure gradient data, the first pressure loss data and the second pressure loss data. The blood pump reduction model uses the pump head pressure difference data as an input quantity and the operation setting parameter data (such as the blood pump speed) as the second input quantity to simulate the blood pump. The blood pump reduction model can calculate the flow data and the first blood damage data. Among them, the flow data of the blood pump is also the flow data of the mechanical circulatory assist device. The pipeline reduction 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 it with the first pressure loss data, the pressure drop loss caused by the pipeline component in the mechanical circulatory assist device can be determined, thereby combining the pressure gradient data to more accurately determine the true pump head pressure difference data at both ends of the power component, thereby improving the prediction accuracy of the blood pump reduction model.

[0208] The reduced-order blood pump model and reduced-order piping model, which comprise the mechanical circulatory assist model, simulate the power assembly, first piping assembly, and second piping assembly, respectively, to simulate the output parameters of each component in the device. This improves the fidelity of the model's simulation of the device and, in turn, the accuracy of the parameters output by the reduced-order model.

[0209] In addition, for the second pipeline component, the hemolytic damage it causes to the blood during blood circulation is relatively small, but the pressure drop loss at both ends needs to be considered. Therefore, a linear pipeline model is used instead of a reduced-order model, which can predict the pressure drop loss. This can effectively reduce the calculation complexity of the model and improve the real-time performance of data prediction.

[0210] In some embodiments, the above-mentioned mechanical circulatory assist device includes an extracorporeal ventricular assist device, and the first pipeline assembly in the extracorporeal ventricular assist device includes: a drainage cannula and a reflux 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 reflux cannula, and the output end of the reflux cannula is used to connect to the reflux position.

[0211] like Figure 8 As shown, the pipeline reduction order model includes: a drainage cannula reduction order model corresponding to the drainage cannula, and a reflux cannula reduction order model corresponding to the reflux cannula; the drainage cannula reduction order model is used to determine the pressure loss data at both ends of the drainage cannula based on the flow data; the reflux cannula reduction order model is used to determine the pressure loss data at both ends of the reflux cannula based on the flow 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.

[0212] 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 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 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.

[0213] 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 data; the reflux cannula reduced-order model is used to determine the blood damage data of the reflux cannula based on the flow data; the blood damage data of the drainage cannula and the blood damage data of the reflux cannula are used to determine the second blood damage data corresponding to the first pipeline assembly.

[0214] The reduced-order model for drainage cannulas is derived from a computational fluid dynamics analysis of drainage cannulas. Based on flow data and cannula dimensions, the reduced-order model simulates the pressure loss and blood damage caused by blood flow through the cannula.

[0215] The reduced-order model for the reflux cannula is derived by further reducing the model obtained through computational fluid dynamics analysis of the reflux cannula. Based on flow data and cannula dimensions, the reduced-order model simulates the pressure loss caused by blood flow through the cannula, as well as the blood damage caused by the cannula.

[0216] The sum of the pressure loss data generated by the drainage cannula and the pressure loss data generated by the return cannula is the first pressure loss data. The sum of the blood damage data generated by the drainage cannula and the blood damage data generated by the return cannula is the second pressure loss data.

[0217] The first linear pipeline model and the second linear pipeline model can be modeled based on the fluid mechanics of the first pipeline and the second pipeline, respectively. The first linear pipeline model can simulate pressure loss data generated by blood flow through the first pipeline based on flow data and dimensional information of the first pipeline. Similarly, the second linear pipeline model can simulate pressure loss data generated by blood flow through the second pipeline based on flow data and dimensional information of the second pipeline.

[0218] 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.

[0219] In a possible implementation, the first linear pipeline model and the second linear pipeline model may further respectively predict the blood damage data of the first pipeline and the blood damage data of the second pipeline.

[0220] Here, a specific example is proposed in combination with the above embodiment:

[0221] In the embodiment of the present application, the cardiovascular model and the mechanical circulatory assistance model are integrated together, and the interaction between the two is linked through pressure-flow coupling. The total effective pressure difference (i.e., pump head pressure difference data) required for the mechanical circulatory assistance device 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 (for example, from the left atrium / left ventricle to the aorta); the pressure loss caused by the cannula, including: ΔPdrain (pressure loss data generated by the drainage cannula) and ΔPreturn (pressure loss data generated by the return cannula); and the pressure loss ΔPtubing (second pressure loss data) of the pipeline in the mechanical circulatory assistance device circuit. The mechanical circulatory assistance model of the mechanical circulatory assistance device takes the total effective pressure difference and the blood pump speed (RPM) as input, calculates the corresponding pump flow (Q_pump) (i.e., flow data) and the mechanical hemolysis index (MIH), i.e., the total blood damage data, and feeds the results back to the cardiovascular model to update the systemic hemodynamic state. The cannula and circuit models (i.e., the reduced-order circuit model and the linear circuit model) are also integrated into the overall model to consider their impact on hemodynamics.

[0222] Both the drainage cannula (inflow) and the return cannula (outflow) were modeled using CFD-derived reduced-order models (resulting in the drainage cannula reduced-order model and the return cannula reduced-order model), mapping the pump flow rate (Q_pump) and the cannula diameter to the pressure loss (ΔP_drain, ΔP_return). The tubing connecting the mechanical circulatory assist assembly (the second tubing assembly) was modeled using the Darcy–Weisbach equation, describing the quadratic relationship between pressure loss (ΔP_tubing), flow rate (Q), and tubing geometry. This quadratic relationship can be expressed as follows:

[0223]

[0224] 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.

[0225] 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, first linear pipeline model, and second linear pipeline model can be considered to be 0.

[0226] 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 (i.e., the pump head pressure difference data) as input, and obtain the current speed when the blood pump is started. Based on the speed and the pump head pressure difference data, the flow data is output, and the hemolytic index of the blood pump reduced-order model (blood pump) is calculated.

[0227] 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 based on 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 the hemolysis index.

[0228] By combining the pressure gradient data between the drainage position and the reflux position with ΔPdrain, ΔPreturn, and ΔPtubing, the true pressure difference (that is, the pump head pressure difference data) at both ends of the blood pump reduced-order model (that is, the blood pump inlet and the blood pump outlet) can be obtained. The true pressure difference (that is, the pump head pressure difference data) is then used as the input of the blood pump reduced-order model, combined with the current speed to output real-time flow data, and the hemolytic index of the blood pump reduced-order model is calculated.

[0229] The reduced-order blood pump model, drainage cannula model, return cannula model, first linear circuit model, and second linear circuit model, which comprise the mechanical circulatory assist model, simulate each component of the device: the blood pump, drainage cannula, return cannula, first circuit, and second circuit, respectively. This model simulates the output parameters of each component in the device, improving the authenticity of the model's simulation of the device and, in turn, the accuracy of the parameters output by the reduced-order model.

[0230] The cardiovascular model can be implemented using a variety of models, such as a machine learning model, etc. The cardiovascular model provided in this embodiment is as follows.

[0231] In some embodiments, as Figure 9 and Figure 10 As 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 charging and discharging circuit corresponding to the heart in the cardiovascular system. The lumped parameter model is connected to the mechanical circulatory assist model through pressure-flow coupling.

[0232] Inputting flow data into a cardiovascular model to update the operating state of the cardiovascular model and outputting hemodynamic parameter data includes: inputting flow data into a lumped parameter model to update current changes in a circuit structure; obtaining electrical signals from a charge-discharge circuit; and determining cardiac parameter data based on the electrical signals from the charge-discharge circuit.

[0233] The lumped parameter model (LPM) is a simplified mathematical model that can describe the overall characteristics of the blood circulation of a target object through lumped parameters (such as equivalent resistance, capacitance, inductance, etc.).

[0234] A lumped parameter model can simulate the blood circulation of a target subject (e.g., a human body). A lumped parameter model can represent the target subject's cardiovascular system as a simplified equivalent circuit. The equivalent circuit can include resistance, capacitance, and inductance, among others. Voltage can correspond to the target subject's blood pressure, current can correspond to the target subject's blood flow rate (blood flow per unit time), resistance can correspond to the resistance to blood flow in the target subject's blood vessels, capacitance can correspond to the target subject's vascular compliance (the ability of blood vessels to expand and store blood) and / or cardiac compliance (the ability of the heart to store blood), and inductance can correspond to the target subject's blood flow inertia.

[0235] In one possible implementation, a 0-dimensional lumped parameter model may be used to simulate the blood circulation of the target object. The 0-dimensional lumped parameter model may not consider the spatial distribution characteristics of the blood circulation, but only considers the dynamic behavior of the blood circulation changing with time.

[0236] In this embodiment, the lumped parameter model includes a simplified zero-dimensional (0D) mathematical model. The lumped parameter model is established based on the principles of Windkessel theory. This theory uses a set of partial differential equations to describe the hemodynamic behavior using circuit analysis. The lumped parameter model can be implemented through simulation software to provide a dynamic simulation operating environment. Figure 9 and Figure 10 As shown, the equivalent circuit of the lumped parameter model can include six interconnected regions: the pulmonary circulation, the systemic circulation, the left coronary circulation, the right coronary circulation, the left heart, and the right heart. The lumped parameter model and the mechanical circulatory assistance model are connected via pressure-flow coupling. Specifically, the lumped parameter model provides pressure gradient data to the mechanical circulatory assistance model, which then predicts flow data based on the pressure gradient data and feeds it back to the lumped parameter model to simulate the hemodynamics of the target subject's cardiovascular system under the assistance of a mechanical circulatory assistance device.

[0237] For example, Figure 9 and Figure 10 The lumped parameter model equivalent circuit of the blood circulation is shown as an example. The heart can be equivalent to a charge-discharge circuit. The hemodynamics of the heart are simulated by the current and voltage changes of the charge-discharge circuit. 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 (for maintaining 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 for maintaining unidirectional blood flow). The lumped parameter model can determine cardiac parameter data based on the electrical signals in the lumped parameter model equivalent circuit.

[0238] In some embodiments, the charge and discharge circuit includes a variable capacitor for simulating a ventricle;

[0239] Based on the electrical signal of the charge and discharge circuit, cardiac parameter data is determined, including at least one of the following: determining native cardiac index data based on the current data of the variable capacitor; determining ventricular pressure change data based on the voltage change data of the variable capacitor; determining ventricular volume change data based on the charge change data of the variable capacitor; determining total cardiac index data based on the native cardiac output data and flow data; determining at least one of pressure-volume loop data and ventricular elasticity data based on the ventricular pressure change data and the ventricular volume change data.

[0240] Specifically, the mechanical circulatory assistance model can feed the output flow rate data obtained by simulating a mechanical circulatory assistance device into the equivalent circuit of the lumped parameter model, which is equivalent to the mechanical circulatory assistance device providing circulatory assistance to the target subject. The predicted results obtained by simulating the target subject using the lumped parameter model are equivalent to the cardiac parameter data of the target subject during circulatory assistance.

[0241] like Figure 9 and Figure 10 As shown, the equivalent circuit model may include multiple model units. Each model unit may be used to simulate one or more organs / tissues in the cardiovascular system of the target subject. Each model unit may be composed of at least one electronic component.

[0242] like Figure 9 and Figure 10 As shown, the equivalent circuit model may 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 left heart, and a model unit for simulating right heart.

[0243] like Figure 9 and Figure 10 In the figure, the variable capacitor ELA, the diode MV, the variable capacitor ELV and the diode AoV are combined to simulate the left heart, wherein 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.

[0244] The variable capacitor ERA, the diode TriV, the variable capacitor ERV and the diode PV are combined to simulate the left heart, wherein 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.

[0245] Here, the cardiac output of the ventricle can be represented based on the current output by the variable capacitor (variable capacitor ELV and / or variable capacitor ERV) that simulates the ventricle. For example, the cardiac output of the left ventricle, i.e., the native cardiac output data of the target subject, can be determined based on the current output by the variable capacitor ELV.

[0246] In one possible implementation, native cardiac index data may 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.

[0247] The cardiac output of the native heart combined with the 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 object.

[0248] In one possible implementation, total cardiac index data may be determined based on total cardiac output data. Specifically, the total cardiac index data may be the quotient of total cardiac output data divided by body surface area.

[0249] The voltage of the variable capacitor used to simulate the ventricle can represent the ventricle pressure, so the ventricle pressure change data can be determined based on the voltage change of the variable capacitor used to simulate the ventricle. The ventricle pressure change data can include the ventricle pressure change data in a complete cardiac cycle.

[0250] The charge amount of the variable capacitor used to simulate the ventricle can represent the volume of the ventricle. Therefore, the volume change data of the ventricle can be determined based on the change in the charge amount of the variable capacitor used to simulate the ventricle. The volume change data of the ventricle can include the volume change data of the ventricle during a complete cardiac cycle.

[0251] Combining ventricular pressure change data over a complete cardiac cycle with data on ventricular pressure change over a complete cardiac cycle can determine ventricular pressure-volume loop data. Ventricular pressure-volume loop data can be used as a graphical tool to describe the dynamic relationship between ventricular pressure and volume over a complete cardiac cycle, and can intuitively reflect the heart's systolic and diastolic function, work efficiency, and pathological status.

[0252] The ventricular elasticity can be determined by combining the ventricular pressure change data and the ventricular volume change data. Specifically, the ventricular elasticity (E) can be the derivative of the ventricular pressure change with the ventricular volume change.

[0253] In this way, the mechanical circulatory assist model feeds the assisted blood flow output obtained by simulating the mechanical circulatory assist device into the equivalent circuit model. The equivalent circuit then simulates cardiac output data, pressure-volume loop data, ventricular elastance data, and other cardiac parameter data for the target subject undergoing ventricular assist. This reduces the need for invasive testing to determine cardiac parameter data, minimizing harm to the target subject. Furthermore, the equivalent circuit model accurately simulates the target subject's cardiac parameter data during ventricular assist, improving the accuracy of predicted vascular-related parameters.

[0254] Example 2

[0255] The various embodiments or implementations in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between the various embodiments can be referenced. For example, in Example 1, the blood circulation model can be constructed by coupling a cardiovascular model with a mechanical circulatory support model. The implementation of the blood circulation model is similar to any of the implementations in Example 1 and will not be further described here.

[0256] In some embodiments, the blood circulation model can also be implemented by a machine learning model, an artificial neural network, etc., to simulate a mechanical circulatory assist device to assist the target object.

[0257] The blood circulation model is used to simulate the assistance provided by a mechanical circulatory assist device to a target subject. The target subject has individual characteristics, meaning that each target subject (e.g., each patient) may have different physiological parameter data, and the physiological parameter data of the same target subject may also vary at different times. Therefore, to improve the accuracy of the target subject simulation, the blood circulation model needs to be fitted so that the hemodynamics of the target subject's cardiovascular system simulated by the blood circulation model more closely matches the actual cardiovascular system of the target subject. The specific fitting method is as follows:

[0258] In some embodiments, a method for monitoring cardiovascular system performance, such as Figure 11 As shown in the figure, methods for monitoring cardiovascular system performance include:

[0259] Step 1101: Obtain a blood circulation model in which a mechanical circulatory assist device is connected to the cardiovascular system of a target subject.

[0260] The blood circulation model is used to simulate the blood flow after the mechanical circulatory assist device is connected to the cardiovascular system.

[0261] The blood circulation model can be obtained in the same manner as in any embodiment of Example 1 and will not be described in detail here. The blood circulation model can also be implemented by a machine learning model, an artificial neural network, etc., to simulate a mechanical circulatory assist device to assist the target object.

[0262] In one possible implementation, a machine learning model or an artificial neural network can be trained based on operating 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 the operating setting parameter data.

[0263] Step 1102: Acquire physiological parameter data associated with the cardiovascular system, and acquire operating setting parameter data of a mechanical circulatory support device.

[0264] The specific implementation method for obtaining physiological parameter data is the same as any implementation method in Example 1 and will not be repeated here.

[0265] The operation setting parameter data may also include the values of the operation setting parameters of the mechanical circulatory assist device when the blood circulation model simulates the mechanical circulatory assist device. For example, the operation setting parameter data may include the operation setting parameter data of the mechanical circulatory assist model in Example 1.

[0266] Step 1103: After the mechanical circulatory assist device is installed, the model parameters of the blood circulation model are periodically fitted according to the physiological parameter data to obtain model parameter data that matches the target object.

[0267] Among them, the model parameters fitted in at least two periods are different.

[0268] When fitting a blood circulation model, physiological parameter data can be used as a fitting reference. For example, model parameter data where the difference between the physiological parameter data derived from the blood circulation model and the physiological parameter data of the cardiovascular system is less than a predetermined difference threshold can be used as model parameter data matching the target subject. Model parameters can be internal parameters of the blood circulation model and can be obtained through fitting or training.

[0269] In one possible implementation, the model parameters used for fitting may be model parameters that are highly correlated with the physiological parameter data.

[0270] After the mechanical circulatory assist device is installed, the clinical status of the target subject will change, and the physiological parameter data of the target subject will also change over time. Therefore, periodic fitting can be performed to reduce the difference between the cardiovascular system simulated by the blood model and the cardiovascular system of the target subject.

[0271] Step 1104: driving the blood circulation model to run based on the model parameter data and the operation setting parameter data, and outputting cardiovascular parameter data corresponding to the cardiovascular system.

[0272] Cardiovascular parameter data are used to characterize the physiological state of the cardiovascular system.

[0273] The operating setting parameters may be external parameters of the blood circulation model, which may be set by the user or automatically tuned. The operating setting parameters may be parameters used by the mechanical circulatory assist model to simulate the operating state of the mechanical circulatory assist device, such as flow rate or speed.

[0274] In one possible implementation, in a blood circulation model comprised of a cardiovascular model and a mechanical circulatory assist model, the model parameters may be configuration parameters of the cardiovascular model, and the operational setting parameters may be configuration parameters for the mechanical circulatory assist model. Model parameter data is obtained by fitting the blood circulation model and the target subject's physiological parameter data. Therefore, driving the blood circulation model with the model parameter data can improve the match between the blood circulation model and the target subject's cardiovascular system. The fitted model parameter data matches the target subject's physiological parameter data for each cycle.

[0275] The operational setting parameters enable the blood circulation model to accurately simulate the operating state of a mechanical circulatory assist device. Therefore, using model parameter data and operational setting parameter data to drive the blood circulation model can improve the accuracy of blood circulation model simulation and the accuracy of predicted cardiovascular parameter data for the target subject while receiving mechanical circulatory assist.

[0276] For a blood circulation model composed of a cardiovascular model and a mechanical circulatory assist model, the cardiovascular model simulates the target subject's cardiovascular system. Therefore, it is necessary to fit the target subject's cardiovascular system to the cardiovascular model. The cardiovascular model outputs pressure gradient data. As shown in Example 1, this pressure gradient data is directly related to the accuracy of the blood circulation model's predictions. Accurate pressure gradient data can be obtained through the following implementation.

[0277] In some embodiments, as Figure 12 As shown, step 1103 or step 601 in embodiment 1 may include:

[0278] Step 1201: Fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object.

[0279] Step 1202: Drive the cardiovascular model to run based on the model parameter data, and determine first pressure data corresponding to the drainage position in the cardiovascular model, and second pressure data corresponding to the backflow position in the cardiovascular model.

[0280] Step 1203: Output pressure gradient data based on the first pressure data and the second pressure data.

[0281] The cardiovascular model can be run based on model parameter data to simulate the hemodynamics of the target subject's cardiovascular system. Physiological parameter data can be used as a basis for determining whether the cardiovascular model fits the target subject's cardiovascular system. 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 fit between the cardiovascular model and the cardiovascular system.

[0282] In some embodiments, the physiological parameter data includes at least one of the following: mean arterial pressure data, cardiac output data, blood flow data, and atrial pressure data. Mean arterial pressure data, cardiac output data, blood flow data, and atrial pressure data are highly weighted in hemodynamics. Using these four data sets can improve the accuracy of fitting judgments. Furthermore, using less data to perform fitting judgments can improve fitting efficiency.

[0283] In one possible implementation, the model parameter data when the difference between the physiological parameter data simulated by the blood vessel 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.

[0284] The cardiovascular model can be run based on physiological parameter data to simulate the actual hemodynamics of the target subject, simulating the hemodynamics between the drainage location and the return location, thereby obtaining first pressure data at the drainage location and second pressure data at the return location. Pressure gradient data can be obtained based on the first and second pressure data. For example, the pressure gradient data can be the difference between the first and second pressure data.

[0285] In one possible implementation, a Bayesian optimization algorithm can be used to automatically fit the model parameters. Bayesian optimization can find a solution close to the global optimal solution with a very small number of evaluations.

[0286] The cardiovascular model, by fitting the target subject with physiological parameter data, improves the accuracy of the cardiovascular model's simulation of the target subject. Based on the fitted cardiovascular model, accurate pressure gradient data can be obtained, further improving the accuracy of the predicted hemodynamic parameters and / or blood damage data for the target subject under mechanical circulatory assist devices. This also reduces the need for invasive testing to determine the hemolytic index and assisted blood flow, minimizing harm to the target subject.

[0287] The model parameters of the blood circulation model are periodically fitted according to the physiological parameter data, and the following implementation methods may be used.

[0288] During a first period after the mechanical circulatory assist device is placed on the patient, a first number of model parameters in the blood circulation model are fitted based on the physiological parameter data of the target subject corresponding to the first period, to obtain first fitting data in which the first number of model parameters match the target subject during the first period;

[0289] In a second time period after the first time period, a second number of model parameters in the blood circulation model are fitted according to the physiological parameter data corresponding to the target object in the second time period to obtain second fitting data in which the second number of model parameters matches the target object in the second time period, the first number and the second number are different, and at least two cycles include the first time period and the second time period.

[0290] Here, the first fitting data may be the value of the model parameter obtained by fitting during the first period. Similarly, the second fitting data may be the value of the model parameter obtained by fitting during the second period. The first fitting data and the second fitting data may be the same or different.

[0291] The first fitting data is used to drive the blood circulation model in the first time period, and the second fitting data is used to drive the blood circulation model in the second time period.

[0292] Here, the fitting during the first period may be the initial fitting of the blood circulation model to the target subject. The first period may be the period during which the blood circulation model is first fitted to the target subject, for example, the first day the target subject uses a mechanical circulatory assistance device. During the first period, the blood circulation model may be adjusted using a first number of model parameters. Adjusting the model parameters may affect the hemodynamics simulated by the blood circulation model, thereby affecting the physiological parameter data predicted by the blood circulation model.

[0293] In one possible implementation, a 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 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 may include: model parameters that can change the physiological parameter data by adjusting the parameter value. The model parameter with a greater change in the physiological parameter data under the same adjustment amount has a higher degree of association with 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.

[0294] In one possible implementation, a generalized simulated annealing algorithm (GSA) can be used to determine the degree of correlation between each model parameter and the physiological parameter data, and the model parameter with a higher degree of correlation is selected.

[0295] Before performing the initial fitting, physiological parameter data of the target subject for a first period of time can be detected. The physiological parameter data predicted by the blood circulation model under the current first number of model parameters is compared with the physiological parameter data of the target subject for the first period of time. A fit between the blood circulation model and the target subject is determined until the difference between the predicted physiological parameter data and the physiological parameter data of the target subject for the first period of time falls within a first predetermined range. The specific data values corresponding to the current model parameters can be used as the first fitting data.

[0296] After the first fitting data is determined, the first fitting data may be used to drive the blood circulation model.

[0297] The target subject's blood circulation changes over time, and the blood circulation model may not accurately simulate the target subject's hemodynamics. Therefore, after the initial fitting is completed and the mechanical circulatory assistance device has been operating for a first period of time, the blood circulation model can be refitted to the target subject's hemodynamics. Here, the first period of time can be one or more days.

[0298] Since the target object's blood circulation may change in the second period, such as the third day when the target object uses a mechanical circulatory assist device, the physiological parameter data of the target object in the second period can be detected before fitting, so that the fitted blood circulation model can match the hemodynamics of the target object in the second period.

[0299] For a blood circulation model composed of a cardiovascular model and a mechanical circulation assistance model, the above-mentioned embodiment can be used to implement periodic fitting of the cardiovascular model.

[0300] Here, the fitting during the first period may be the initial fitting between the cardiovascular model and the target object. The first period may be the period during which the cardiovascular model is first fitted to the target object. During the first period, the cardiovascular model may be adjusted using a first number of model parameters. Adjusting the model parameters may affect the hemodynamics simulated by the cardiovascular model, thereby affecting the physiological parameter data determined by the cardiovascular model.

[0301] In one possible implementation, a 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 may include: model parameters that can change the physiological parameter data by adjusting the parameter value. The model parameter with a greater change in the physiological parameter data under the same adjustment amount has a higher degree of association with 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.

[0302] Before performing the initial fitting, physiological parameter data of the target subject for a first period of time can be detected. The physiological parameter data predicted by the cardiovascular model under the current first number of model parameters is compared with the physiological parameter data of the target subject for the first period of time. Fitting of the cardiovascular model to the target subject is determined until the difference between the predicted physiological parameter data and the physiological parameter data of the target subject for the first period of time falls within a first predetermined range. The specific data values corresponding to the current model parameters can be used as the first fitting data.

[0303] After the first fitting data is determined, the first fitting data may be used to drive the cardiovascular model.

[0304] The target subject's blood circulation changes over time, and the cardiovascular model may not accurately simulate the target subject's hemodynamics. Therefore, after the initial fit is completed and the mechanical circulatory assist device has been operating for a first period, the hemodynamic fit between the cardiovascular model and the target subject can be performed again. Here, the first period can be one or more days.

[0305] Since the target subject's blood circulation may change during the second period, the target subject's physiological parameter data during the second period may be detected before fitting, so that the fitted cardiovascular model can match the target subject's hemodynamics during the second period.

[0306] Here, the first number may be different from the second number. The first number and the second number may be determined in a similar manner for the blood circulation model and the cardiovascular model. Here, the cardiovascular model is used as an example for description, and the blood circulation model and the cardiovascular model are not distinguished for description separately.

[0307] In some embodiments, the second number is greater than the first number. Specifically, the second number can be determined based on the accuracy of the fit. If it is determined that the prediction accuracy of the blood circulation model fitted during the first period is less than an accuracy threshold, more model parameters can be used for fitting. For example, the model parameters used during the first period, as well as model parameters other than the model parameters used during the first period, can be used to fit the blood circulation model. This increases the control dimension of the blood circulation model and improves the accuracy of the blood circulation model.

[0308] In some embodiments, the second number is smaller than the first number. Using a smaller number of model parameters can reduce the complexity of the model parameters, thereby improving fitting efficiency and shortening fitting time.

[0309] Specifically, the second number of model parameters can be selected from the first number of model parameters. A second number of model parameters that are more highly correlated with the physiological parameter data can be selected from the first number of model parameters as model parameters. The physiological parameter data predicted by the cardiovascular model under the current second number of model parameters can be compared with the physiological parameter data of the target subject for the second time period, and the cardiovascular model for the second time period can be determined to be fitted to the target subject until the difference between the predicted physiological parameter data and the physiological parameter data of the target subject for the second time period falls within a second predetermined range. The specific data values corresponding to the current model parameters can be used as the second fitting data.

[0310] In a possible implementation, the first number may be 5, and the second number may be 2.

[0311] In some embodiments, the first number of model parameters includes: left ventricular elastance, systemic peripheral resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elastance;

[0312] The second number of model parameters includes left ventricular elastance and systemic peripheral resistance.

[0313] Fitting requires consideration of both computational effort and accuracy. The computational effort increases exponentially with the number of model parameters. Here, left ventricular elastance, systemic peripheral resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elastance represent the most influential determinants of hemodynamic performance and are the five model parameters with a high degree of correlation with predicted physiological data. Using these five model parameters ensures the fundamental dynamics of the interaction between the cardiovascular model and the mechanical circulatory assist model while maintaining physiological plausibility. Using all five model parameters simultaneously can reduce the computational effort of the fitting process, achieving a balance between fitting complexity and accuracy. In the actual fitting process, the five model parameters of left ventricular elastance, systemic peripheral resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elastance can balance computational efficiency and physiological fidelity. By adjusting the cardiovascular model using left ventricular elastance, systemic peripheral resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elastance during the first period, the required fitting accuracy is maintained while reducing the computational load. This improves the efficiency of cardiovascular model adjustment and reduces the fitting time. By adjusting the cardiovascular model using two model parameters, left ventricular elastance and systemic peripheral resistance, the computational load can be further reduced, improving the efficiency of subsequent fitting after the initial fitting, shortening the time for subsequent fitting, and improving the real-time performance of data monitoring.

[0314] In one possible implementation, Figure 9 For an equivalent circuit model of a cardiovascular model, adjustment of model parameters such as left ventricular elasticity, systemic peripheral resistance, blood volume, right ventricular end-diastolic stiffness and right ventricular elasticity can be achieved by adjusting parameters of at least one electronic component in the equivalent circuit model.

[0315] During the use of the cardiovascular model, multiple fittings can be performed based on the state changes of the target subject, ensuring that the cardiovascular model's hemodynamics conform to the target subject's human hemodynamics. This improves the accuracy of the cardiovascular model's simulation of the target subject. In the first fitting period, fewer model parameters are used as fitting parameters, which can reduce fitting complexity and improve fitting efficiency. In the second fitting period, the cardiovascular model fitted in the first fitting period can be used as a basis for fitting, and the number of model parameters used in the second fitting period is less than the number of model parameters used in the initial fitting period. Using fewer model parameters can reduce the complexity of the model parameters, thereby improving fitting efficiency and shortening fitting time.

[0316] Here, a specific example is provided in combination with the above embodiment to illustrate the fitting of the cardiovascular model.

[0317] This example provides 11 target objects, Pt1 to Pt11. Detailed information is shown in Table 1.

[0318] Table 1

[0319]

[0320]

[0321] Table 2 lists the 37 model parameters associated with the physiological parameter data. Among the 37 model parameters, the ones that have the greatest impact on the three physiological parameters of aortic pressure (mean arterial pressure), cardiac output, and central venous pressure (atrial pressure) are shown in Table 2 after GSA calculation.

[0322] Table 2

[0323]

[0324] Due to the large number of 37 model parameters, Table 2 lists only the calculation results for some of the 37 model parameters, but this does not affect the conclusions. Based on the results in Table 2, the top parameters with the highest influence on each physiological parameter are ranked from highest to lowest as shown in Table 3. Based on Table 3, the top five most influential parameters are ranked from highest to lowest as shown in Table 4. EmaxLV represents left ventricular elastance, V0vn represents blood volume, Rsar represents systemic peripheral resistance, RV_Pd_a represents right ventricular end-diastolic stiffness, and EmaxRV represents right ventricular elastance.

[0325] Table 3

[0326]

[0327]

[0328] Table 4

[0329]

[0330] Table 3 lists the top 10 model parameters with the greatest influence on 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 greatest 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 fitting parameters for the first point (first period) to achieve satisfactory fitting results (average error of 1.5%, average time of about 4 hours). In order to further increase efficiency and reduce the possibility of the model falling into a local solution, it was found after trial that the number of parameters for subsequent points (such as the second period) can be further reduced to a minimum of only two of the most important model parameters (left ventricular elasticity and systemic peripheral resistance), further reducing the calculation time to an average of 2 hours per point and an average error of 8%.

[0331] By selecting model parameters based on the degree of correlation between candidate model parameters and physiological parameter data, the cardiovascular model can more effectively adjust physiological parameter data through adjustment of model parameters during fitting, thereby improving fitting efficiency and shortening fitting time. Furthermore, by fitting the cardiovascular model using this method, fewer model parameters are used as fitting parameters, which can reduce fitting complexity and improve fitting efficiency.

[0332] like Figure 13 The comparison between the clinical physiological parameters of a target subject in Table 1 and the physiological parameters predicted by the cardiovascular model after fitting. Figure 13 As shown in Figure 1, the curve pointed by arrow A1 is the clinical MAP data curve, and the curve pointed 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 shown in Figure 1. Figures 14 to 16 shown. Figure 14 The integrated regression line of MAP is shown in Figure 2, where the average accuracy of mean arterial pressure (AP) 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 between cardiac output and CO reaches 0.75. The above-mentioned verification verifies the automatic fitting ability of the model.

[0333] In one possible implementation, the model parameters may be selected within a reasonable range, as shown in Table 5.

[0334] Table 5

[0335] Model parameters scope blood volume 4000-8000ml Systemic peripheral resistance 0.2-2.8 mmHg / ml Systemic peripheral resistance 0.0-1.5 mmHg.s / ml Right ventricular elasticity 0.5-1.5 mmHg / ml Right ventricular end-diastolic stiffness 0.1-0.8 mmHg

[0336] Example 3

[0337] The various embodiments or implementation methods 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 various embodiments can be referenced to each other.

[0338] The operating parameters of a mechanical circulatory assist device (MACD) directly impact hemodynamic parameters and / or blood damage data. During the device's installation, medical personnel need to set or adjust the device's operating parameters, such as the target speed of the blood pump. The following methods can assist medical personnel in selecting the most appropriate operating parameters for the current patient.

[0339] In some embodiments, as Figure 17 As shown, methods for monitoring cardiovascular system performance also include:

[0340] Step 1701: Start traversing multiple sets of operation setting parameter data.

[0341] Step 1702: When traversing to the current set of operating parameter setting data, the pressure gradient data and parameter setting data are input into the mechanical circulation assistance model to drive the operation of the blood circulation model, and the flow data and blood damage data of the mechanical circulation assistance model are output. The flow data is input into the cardiovascular model to update the operating status of the cardiovascular model and output the hemodynamic parameter data.

[0342] Step 1703: After the traversal of the multiple sets of operation setting parameter data is completed, the hemodynamic parameter data corresponding to the multiple sets of operation setting parameter data and / or the blood damage data corresponding to the multiple sets of operation setting parameter data are obtained;

[0343] Step 1704: Determine 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 second change relationship information between the blood damage parameters and the operating setting parameters based on the blood damage data corresponding to the multiple sets of operating setting parameter data and the multiple sets of operating setting parameter data.

[0344] Here, a mechanical circulatory assistance model and a cardiovascular model may be used to simulate and predict changes in hemodynamic parameter data and / or blood damage data of the target subject's cardiovascular system when the mechanical circulatory assistance device operates with different operating setting parameter data.

[0345] In a possible implementation, the blood damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

[0346] 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.

[0347] In one possible implementation, the multiple sets of operating parameter data may include all operating parameter data supported by the mechanical circulatory assist device, such as all rotational speeds supported by a blood pump. Specifically, the mechanical circulatory assist model may traverse each set of operating parameter data and couple with the cardiovascular model at each set of operating parameter data to predict hemodynamic parameter data and / or blood damage data at each rotational speed.

[0348] In one possible implementation, the plurality of sets of operation setting parameter data may be selected by medical personnel and input into the control device. In another possible implementation, the plurality of sets of operation setting parameter data may be sampled and traversed by the control device itself.

[0349] The specific implementation method of constructing a blood circulation model by using a mechanical circulation assistance model and a cardiovascular model, and predicting hemodynamic parameter data and / or blood damage data based on the operation setting parameter data can be the same as any of the above embodiments and will not be repeated here.

[0350] By running the blood circulation model on multiple sets of operation setting parameter data, the hemodynamic parameter data corresponding to each set of operation setting parameter data can be obtained, so the first change relationship information between the hemodynamic parameters and the operation setting parameters can be determined.

[0351] By running the blood circulation model on multiple sets of operation setting parameter data, blood damage data corresponding to each set of operation setting parameter data can be obtained. Therefore, second change relationship information between the blood damage parameters and the operation setting parameters can be determined.

[0352] The first change relationship information and / or the first change relationship information facilitates medical personnel to evaluate blood damage data and / or hemodynamic parameter data under different operating setting parameter data to select operating setting parameter data that is safer for the target object.

[0353] By using the mechanical circulatory assistance model and cardiovascular model to predict blood damage data and / or hemodynamic parameter data for each operating parameter setting, medical personnel can determine the blood damage data and / or hemodynamic parameter data for the mechanical circulatory assistance device under different operating parameter settings. This allows them to select appropriate operating parameter settings for the mechanical circulatory assistance device, reducing or avoiding safety risks to the subject caused by improper speed or flow settings.

[0354] In some embodiments, as Figure 18 As shown, the cardiovascular system performance monitoring method also includes:

[0355] Step 1801: Output parameter setting suggestion information corresponding to the operation setting parameters according to the first change relationship information and / or the second change information.

[0356] In a possible implementation, outputting parameter setting suggestion information corresponding to the operation setting parameter includes displaying the parameter setting suggestion information on a display screen of the control device and sending the parameter setting suggestion information to a user terminal.

[0357] The control device may be configured with selection conditions for blood damage parameters and / or hemodynamic parameters. The control device may select, from the first change relationship information and / or the second change information, operating setting parameter data corresponding to cardiovascular parameters that meet the selection conditions as recommended parameter settings, such as a recommended speed range or flow rate range.

[0358] Through the parameter setting recommendation information, medical personnel can make preliminary judgments on the operating setting parameter data, reduce the burden on medical personnel to evaluate the operating setting parameter data, and also provide medical personnel with the ability to verify the selected operating setting parameter data, thereby further improving the safety of mechanical circulatory assist devices.

[0359] In some embodiments, step 1101 includes:

[0360] Obtaining a cardiovascular model corresponding to the cardiovascular system in the target subject and a mechanical circulatory assist model corresponding to the mechanical circulatory assist device, wherein 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;

[0361] obtaining a drainage position and a return position of the mechanical circulatory assist device in the cardiovascular system;

[0362] coupling the cardiovascular model and the mechanical circulation assistance model according to the drainage position and the reflux position to obtain a blood circulation model;

[0363] Step 1104 includes: fitting model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0364] Step 1104 also includes:

[0365] driving the cardiovascular model to run based on the model parameter data, and outputting pressure gradient data between the drainage position and the backflow position;

[0366] The pressure gradient data and the parameter setting data are input into the mechanical circulatory assistance model to drive the blood circulation model to run and output the cardiovascular parameter data.

[0367] Here, a cardiovascular model and a mechanical circulatory assistance model are obtained, and the cardiovascular model is fitted to determine the pressure gradient data, and then the pressure gradient data and parameter setting data are input into the mechanical circulatory assistance model to determine the specific implementation method of the cardiovascular parameter data as described in any one of Examples 1 to 4, which will not be repeated here.

[0368] 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:

[0369] Inputting the pressure gradient data and the parameter setting data into the mechanical circulation assistance model to drive the blood circulation model to run, and outputting the flow data and the blood damage data of the mechanical circulation assistance model;

[0370] The flow data is input into the cardiovascular model to update the operating state of the cardiovascular model and output hemodynamic parameter data.

[0371] Here, the specific implementation method of inputting the pressure gradient data and parameter setting data into the mechanical circulatory assistance model to determine the flow data and the blood damage data of the mechanical circulatory assistance model, and outputting the hemodynamic parameter data from the cardiovascular model based on the flow data is as described in any one of the implementation methods in Examples 1 to 4, and will not be repeated here.

[0372] In some embodiments, the mechanical circulatory assist device includes a power assembly, a first pipeline assembly, and a second pipeline assembly, and the mechanical circulatory assist model includes a blood pump reduced-order model corresponding to the power assembly, a pipeline reduced-order model corresponding to the first pipeline assembly, and a linear pipeline model corresponding to the second pipeline assembly;

[0373] The method further includes: acquiring first size information corresponding to the first pipeline component and second size information corresponding to the second pipeline component;

[0374] Inputting the pressure gradient data and the parameter setting data into the mechanical circulation assistance model to drive the blood circulation model to operate, and outputting the flow data and the blood damage data of the mechanical circulation assistance model, includes:

[0375] Inputting the flow data and the first dimension information into the pipeline reduced-order model for processing, and outputting first pressure loss data at both ends of the first pipeline assembly and second blood damage data corresponding to the first pipeline assembly, wherein the second blood damage data is used to characterize the blood damage caused by the first pipeline assembly;

[0376] Inputting the flow data and the second dimension information into the linear pipeline model for processing, and outputting second pressure loss data at both ends of the second pipeline component;

[0377] Determining pump head pressure differential data corresponding to the power assembly based on the pressure gradient data, the first pressure loss data, and the second pressure loss data;

[0378] inputting the pump head pressure difference data and the parameter setting data into the blood pump reduced-order model to drive the blood circulation model to run, and updating and outputting the flow data and first blood damage data corresponding to the blood pump reduced-order model, wherein the first blood damage data is used to characterize the blood damage caused by the power component;

[0379] The first blood damage data and the second blood damage data are fused to obtain total blood damage data.

[0380] Here, based on the specific structure of the mechanical circulatory assist device, the specific implementation method of determining blood flow injury data through the blood pump reduced-order model, the pipeline reduced-order model and the linear pipeline model is as described in any of the implementation methods of Examples 1 to 4, and will not be repeated here.

[0381] 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 a ventricle, and the lumped parameter model is connected to the mechanical circulatory assist model via pressure-flow coupling;

[0382] Inputting the flow data into the cardiovascular model to update the operating state of the cardiovascular model and output hemodynamic parameter data includes:

[0383] inputting the flow data into the lumped parameter model to update the current change in the circuit structure;

[0384] determining native cardiac output data based on the current data of the variable capacitor;

[0385] determining ventricular pressure change data based on the voltage change data of the variable capacitor;

[0386] determining the volume change data of the ventricle based on the charge amount change data of the variable capacitor;

[0387] determining total cardiac output data based on the native cardiac output data and the flow data;

[0388] At least one of cardiac pressure-volume loop data and ventricular elastance data is determined based on the ventricular pressure change data and the ventricular volume change data.

[0389] Here, the specific implementation of determining cardiovascular parameter data based on the lumped parameter model is as described in any of the implementations in Examples 1 to 4, and will not be repeated here.

[0390] Example 4

[0391] The various embodiments or implementation methods 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 various embodiments can be referenced to each other.

[0392] The operating parameters of a mechanical circulatory assist device (MACD) directly impact cardiovascular parameters. During the device's installation, medical personnel need to set or adjust these parameters, such as the speed or flow rate of the blood pump. The following methods can assist medical personnel in selecting the most appropriate operating parameters for the patient.

[0393] In view of this, the present application also provides a mechanical circulation assist device, such as Figure 19 As shown, the mechanical circulatory assist device is connected and coupled to the cardiovascular system in the target subject during use, and a drainage position and a return position are provided between the mechanical circulatory assist device and the cardiovascular system.

[0394] The mechanical circulation 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 a drainage position, and the other end of the pipeline assembly is located at a reflux position.

[0395] The control device is configured to: obtain first parameter setting data corresponding to the operating setting parameters, and control the operation of the driving component based on the first parameter setting data to drive the power component to pump blood, wherein the power component drives the blood to flow from the drainage position into the pipeline component and out from the reflux position.

[0396] The control device is associated with a user interface, which is configured to:

[0397] Display cardiovascular parameter monitoring data corresponding to the cardiovascular parameters, and / or display data change relationship prediction information between the cardiovascular parameters and the operating setting parameters.

[0398] Among them, 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 changes of cardiovascular parameters under different operating setting parameters of the mechanical circulatory assist device.

[0399] The operation setting parameter data may be input to the control device by a user through a user interface of the mechanical circulatory assist device or through a network connection. The operation setting parameter data may also be default operation setting parameter data set by the mechanical circulatory assist device.

[0400] In one possible implementation, the operating parameter data includes the rotational speed of the power assembly, the output flow rate of the blood pump, and the like. The operating parameter data may be the value of the operating parameter. For example, if the operating parameter is the rotational speed, the operating parameter data may be a specific rotational speed value, such as 1800. The control device may control the drive assembly based on the operating parameter data to drive the power assembly.

[0401] Cardiovascular parameter data may include cardiovascular-related data of the target subject under the assistance of a mechanical circulatory assist device. Cardiovascular parameters may include cardiovascular-related hemodynamic parameters and blood damage parameters. Correspondingly, cardiovascular parameter data may include cardiovascular-related hemodynamic parameter data and blood damage parameter data. Cardiovascular parameter data may be values corresponding to the cardiovascular parameters. For example, the cardiovascular parameter may be a cardiac index, and the cardiovascular parameter data may be a specific value of the cardiac index, such as 2.2. Blood damage parameter data may be values corresponding to the blood damage parameters. For example, the cardiovascular parameter may be a hemolytic index, and the hemolytic index data may be a specific value of the hemolytic index.

[0402] In one possible implementation, the blood damage parameter includes but is not limited to at least one of the following: a hemolysis index, a coagulation index, and a thrombosis index.

[0403] In one possible implementation, the hemodynamic parameter includes, but is not limited to, at least one of the following: cardiac index, pressure-volume loop, and ventricular elastance.

[0404] The first parameter setting data may be the operating parameter setting data currently set for the mechanical circulatory assist device. The control device may control the drive assembly based on the first parameter setting data, and when the drive assembly is working, it drives the power assembly to pump blood to assist the blood circulation of the target subject.

[0405] The control device can predict cardiovascular parameter data when the mechanical circulatory assist device assists the target subject using different operating parameter settings. Specifically, the control device can use a blood circulation model to predict the cardiovascular parameter data based on the operating parameter settings data. The blood circulation model can include a machine learning model, etc. The machine learning model can be trained based on the operating parameter training data and the cardiovascular parameter training data. The trained machine learning model can then predict corresponding cardiovascular parameter data based on the operating parameter settings data.

[0406] In one possible implementation, a processing device external to the mechanical circulatory assist device can predict cardiovascular parameter data under different operating parameter settings. The external processing device can send the prediction results to a user interface for display. It is understood that the mechanical circulatory assist device and the processing device external to the mechanical circulatory assist device, as a system interconnected during the process of predicting and displaying cardiovascular parameter data, can also be collectively referred to as a mechanical circulatory assist device.

[0407] The cardiovascular parameter monitoring data may be cardiovascular parameter data predicted by the control device based on the first parameter setting data. The control device may also predict cardiovascular parameter data corresponding to at least one operating parameter setting data other than the first parameter setting data. This may further provide data change relationship prediction information characterizing changes in cardiovascular parameter data under different operating parameter settings of the mechanical circulatory assist device.

[0408] The operating setting parameter data used to predict cardiovascular parameter data may include all operating setting parameter data supported by the mechanical circulatory assist device, or part of the operating setting parameter data selected from all the operating setting parameter data.

[0409] The user interface can display predicted results, such as cardiovascular parameter monitoring data and / or data change relationship prediction information. The user interface can interact with the user by displaying data, charts, etc. The user interface associated with the control device may include a display screen provided on the control device; the user interface may also include a display screen separate from the control device, such as the display screen of an external display, the display screen of an external monitor, and / or the display screen displayed on a user terminal. The control device can send information to the user interface via a data connection for display on the user interface.

[0410] The data change relationship prediction information can characterize the data changes of the cardiovascular parameters of the mechanical circulatory assist device under different operating setting parameters by listing multiple operating setting parameter data and corresponding cardiovascular parameter data; the data change relationship prediction information can also characterize the data changes of the cardiovascular parameters of the mechanical circulatory assist device under different operating setting parameters by means of a change relationship curve between the operating setting parameter data and the cardiovascular parameter data.

[0411] By displaying cardiovascular parameter monitoring data, a mechanical circulatory assist device allows medical personnel to determine the effectiveness of the device on the cardiovascular system when the device is operating with a first parameter setting. This allows medical personnel to promptly identify problems and adjust the device's operating parameters, reducing or avoiding adverse effects on the target patient and improving the device's safety and effectiveness. By displaying predicted data change relationships between operating parameters and cardiovascular parameters, medical personnel can view changes in cardiovascular parameters when the device is operating with different operating parameter settings without actually adjusting the device's operating parameters. This facilitates medical personnel to select appropriate operating parameter settings for the device and determine whether the current operating parameter settings are effective, thereby reducing the safety risks posed to the target patient by setting inappropriate operating parameter settings. Combining cardiovascular parameter monitoring data with the predicted data change relationship information allows users to evaluate the rationality of the currently used first parameter settings, thereby improving the accuracy of the operating parameter settings.

[0412] In some embodiments, the user interface is further configured to: display parameter setting suggestion information corresponding to the operation setting parameters based on the data change relationship prediction information.

[0413] The control device may be configured with selection conditions for cardiovascular parameters. Based on the data change relationship prediction information, the control device may select operating parameter data corresponding to cardiovascular parameter data that meets the selection conditions as recommended operating parameter data. The control device may also select a data range for operating parameter data corresponding to cardiovascular parameter data that meets the selection conditions as the recommended operating parameter data range based on the data change relationship prediction information.

[0414] The user interface may display the suggested operating setting parameter data and / or operating setting parameter data range to the user via parameter setting suggestion information.

[0415] In one possible implementation, the parameter setting suggestion information may be displayed by displaying the suggested operating setting parameter data, and / or by displaying the suggested operating setting parameter data with graphics, colors, or other markings.

[0416] After the user obtains the recommended parameter setting information, they can select operating parameter data based on the recommended parameter setting information. The control device then uses the recommended operating parameter data selected from the recommended parameter setting information to control the drive component. The recommended parameter setting information can assist the user in making preliminary judgments about the operating parameter data, reducing the burden on the user to assess the rationality of the operating parameter data. It can also allow the user to verify the currently selected first parameter setting data, further improving the safety of the mechanical circulation assist device in assisting the target object.

[0417] In some embodiments, the data change relationship prediction information is presented as a prediction data statistical chart, which includes cardiovascular parameter data corresponding to each of a plurality of operation setting parameter data.

[0418] The plurality of operation setting parameter data include first parameter setting data and a plurality of second parameter setting data, the first parameter setting data being setting data currently corresponding to the operation setting parameter, and the second parameter setting data being different from the first parameter setting data;

[0419] The plurality of cardiovascular parameter data include cardiovascular parameter monitoring data and a plurality of cardiovascular parameter prediction data, and the cardiovascular parameter prediction data correspond one-to-one to the second parameter setting data.

[0420] Here, the cardiovascular parameter monitoring data and the cardiovascular parameter prediction data are both cardiovascular parameter data predicted by the control device. The operation setting parameter data includes first parameter setting data and second parameter setting data.

[0421] The first parameter setting data may be the operation setting parameter data used by the current control device to control the operation of the drive component. The second parameter setting data may be one or more operation setting parameter data other than the first parameter setting data.

[0422] The cardiovascular parameter monitoring data may be data obtained by predicting the cardiovascular parameters of the target subject when the control device drives the component to operate at the first parameter setting data.

[0423] The cardiovascular parameter prediction data may be data obtained by the control device predicting the cardiovascular parameters of the target subject when the driving component operates in the second parameter setting data. The control device may predict the cardiovascular parameter prediction data corresponding to one or more second parameter setting data.

[0424] Each running parameter setting data in the predicted data statistical chart corresponds to 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.

[0425] The predicted 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 in a data listing manner.

[0426] The predicted data statistical chart can also use a change curve to represent 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. For example, the predicted data statistical chart can use a coordinate system with two coordinate axes, the first coordinate axis represents the operating setting parameter, and the second coordinate axis is used to represent the cardiovascular parameter. 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 midpoint in the coordinate system is the operating setting parameter data, and the second coordinate axis coordinate is the predicted cardiovascular parameter data. The corresponding relationship between the changes in the operating setting parameter data and the predicted cardiovascular parameter data is represented by the lines connecting the points in the coordinate system.

[0427] Using statistical charts of predicted data, the cardiovascular support effectiveness of the mechanical circulatory assist device under the first and second parameter settings can be determined, allowing the selection of appropriate operating parameter settings for the mechanical circulatory assist device. Users can evaluate the rationality of the currently used first parameter settings, thereby improving the accuracy of the operating parameter settings. Furthermore, graphically displaying the cardiovascular parameter data corresponding to the operating parameter settings is more intuitive and easier for users to observe.

[0428] In some embodiments, the parameter setting suggestion information includes recommended setting data corresponding to the operating setting parameters.

[0429] 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 in the multiple cardiovascular parameter prediction data.

[0430] The control device may be configured with selection conditions for cardiovascular parameters. The control device may select, from the cardiovascular parameter monitoring data and the plurality of cardiovascular parameter prediction data, operating setting parameter data corresponding to the cardiovascular parameter data that meets the selection conditions as the recommended operating setting parameter data. The control device may also select, from the data change relationship prediction information, a data range of operating setting parameter data corresponding to the cardiovascular parameter data that meets the selection conditions as the recommended setting data.

[0431] The user interface can display the recommended setting data to the user.

[0432] In one possible implementation, the recommended setting data may be displayed and / or the recommended setting data may be marked.

[0433] After obtaining the recommended setting data, the user can verify the current first parameter setting data to determine whether it is correct or reasonable. Furthermore, the user can select target operating setting parameter data based on the recommended setting data, with the control device then controlling the drive assembly using the target operating setting parameter data. The recommended setting data can assist the user in making pre-determined decisions about the operating setting parameter data, reducing the burden on the user to evaluate the operating setting parameter data. It also allows the user to verify the currently selected first parameter setting data, further improving the safety of the mechanical circulation assist device.

[0434] In some embodiments, the parameter setting suggestion information includes first identification information corresponding to the recommended setting data, and the user interface is further configured to:

[0435] Displaying second identification information corresponding to the first parameter setting data;

[0436] When the first identification information and the second identification information indicate that the first parameter setting data does not match the recommended setting data, parameter adjustment prompt information and / or risk prompt information is issued.

[0437] In one possible implementation, the identification information is used to highlight the first parameter setting data and the recommended setting data in the user interface. For example, the identification information may use at least one of a specific font, background color, and mark to identify the operation setting parameter data.

[0438] In one possible implementation, the first identification information may be used to highlight the recommended setting data. The first identification information may 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 may be used to highlight the first parameter setting data. The second identification information may also be used to highlight the first parameter setting data and the cardiovascular parameter monitoring data corresponding to the first parameter setting data.

[0439] In one possible implementation, the first identification information and the second identification information may 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 identification information corresponding to the first parameter setting data is a red background within the range of the first parameter setting data in the coordinate system.

[0440] For example, Figure 20As shown, the horizontal axis in the coordinate system represents the operating setting parameters, and the vertical axis represents the cardiovascular parameter monitoring data (total cardiac index monitoring data). The curve indicated by the arrow X1 in the figure includes the predicted cardiovascular parameter monitoring data corresponding to the operating setting parameters. Among them, the first setting 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 setting parameter data is within the recommended setting data.

[0441] In this manner, the first setting parameter data and the recommended setting parameter data are respectively identified using the first identification information and the second identification information. This improves the recognition of the first setting parameter data and the recommended setting parameter data, allowing the user to intuitively determine the relationship between the recommended setting parameter data and the first setting parameter data and whether the first setting parameter data is within the recommended operating setting parameter data range, thereby improving the visibility of the data display.

[0442] In some embodiments, the operation setting parameter includes a rotational speed, and the data change relationship prediction information includes a first data statistical chart, the first data statistical chart being used to represent a data change trend between the cardiac index and the rotational speed;

[0443] The first data statistical chart includes heart index data corresponding to a plurality of speed setting data;

[0444] The plurality of speed setting data includes first speed setting data and a plurality of second speed setting data, the first speed setting data is a target speed currently set for the mechanical circulatory assist device, and the first speed setting data is different from the second speed setting data;

[0445] The plurality of cardiac index data include cardiac index monitoring data corresponding to the first speed setting data and a plurality of cardiac index prediction data, and the plurality of cardiac index prediction data correspond one-to-one to the plurality of second speed setting data.

[0446] Cardiac index is an important indicator of heart function. It can be calculated by dividing the blood flow data pumped by the heart by the body surface area.

[0447] Here, the first speed setting data may be a target speed used by the current control device to control the operation of the drive component. The second speed setting data may include one or more speed setting data other than the first speed setting data.

[0448] The cardiac index monitoring data may be data obtained by predicting the cardiac index of the target subject when the control device is operating the driving component at the first speed setting data.

[0449] The cardiac index prediction data may be data obtained by the control device predicting the cardiac index of the target subject when the drive assembly operates at the second speed setting data. The control device may predict the cardiac index prediction data corresponding to one or more second speed setting data.

[0450] Each speed setting data in the first data statistical chart corresponds to a predicted cardiac index. For example, the first speed setting data corresponds to cardiac index monitoring data, and each second speed setting data corresponds to a cardiac index prediction data.

[0451] The first data statistics chart may display the cardiac index monitoring data corresponding to the first speed setting data and the cardiac index prediction data corresponding to each second speed setting data in a data listing manner.

[0452] The first data statistical chart can also use a change curve to represent the cardiac index monitoring data corresponding to the first speed setting data and the cardiac index predicted data corresponding to each second speed setting data. For example, the first data statistical chart can use a coordinate system with two axes, the first axis representing the speed and the second axis representing the cardiac index. A point in the coordinate system represents the cardiac index data corresponding to a speed setting data. The lines connecting the points in the coordinate system represent the corresponding change relationship between the operating speed setting data and the predicted cardiac index data.

[0453] The first data statistical chart can be used to determine the cardiovascular assistance effect of the mechanical circulatory assist device under the first speed setting data and the recommended second speed setting data, thereby selecting the appropriate speed setting data for the mechanical circulatory assist device. The user can evaluate the rationality of the currently used first speed setting data to improve the accuracy of the speed setting.

[0454] In some embodiments, the cardiac index includes a native cardiac index and a total cardiac index, and the first data statistical chart includes a first trend line and / or a second trend line, the first trend line is used to represent the data change trend between the native cardiac index and the rotational speed, and the second trend line is used to represent the data change trend between the total cardiac index and the rotational speed.

[0455] Here, when a mechanical circulatory assistance device assists a subject, the subject's heart pumps blood, and the mechanical circulatory assistance device also pumps blood. Therefore, the control device can predict the native cardiac index and total cardiac index based on the speed setting data. The native cardiac index data can be obtained by dividing the subject's heart blood flow data by their body surface area. The total cardiac index data can be obtained by dividing the total blood flow data (the sum of the heart blood flow data and the mechanical circulatory assistance device blood flow data) by their body surface area.

[0456] The first data statistical chart can use trend lines to represent the native cardiac index monitoring data and total cardiac index monitoring data corresponding to the first speed setting data, as well as the native cardiac index prediction data and total cardiac index prediction data corresponding to each second speed setting data.

[0457] For example, Figure 21 As shown in the 21 coordinate system, the horizontal axis represents the rotational speed, and the vertical axis represents the cardiac index. The black dots in the figure include the total cardiac index monitoring data corresponding to the first rotational speed setting (2000 RPM) and the total cardiac index predicted data corresponding to the second rotational speed setting. Connecting the dots yields the second trend line indicated by the arrow X2. In one possible implementation, adjacent black dots can be connected directly or through interpolation.

[0458] Figure 21 The "x" symbols in the graph include the native cardiac index monitoring data corresponding to the first speed setting (2000 RPM) and the native cardiac index prediction data corresponding to the second speed setting. Connecting these points yields the first trend line indicated by the arrow Y2. In one possible implementation, adjacent "x" symbols can be connected directly or through interpolation.

[0459] By displaying the first trend line and the second trend line, the user can intuitively observe the native cardiac index data and the total cardiac index data corresponding to multiple speed setting data, thereby judging the physiological state of the native heart and the blood perfusion of the target object, and facilitating the user to select the speed setting data that meets the conditions for both the native cardiac index data and the total cardiac index data based on the trend lines.

[0460] In some embodiments, the blood damage parameter includes a hemolysis index, and the first data statistical chart further includes a third trend line, which is used to represent a data change trend between the hemolysis index and the rotation speed.

[0461] The first data statistical graph may include trend lines (such as a first trend line and / or a second trend line) representing the data change trend between the cardiac index and the rotational speed, and may simultaneously display a third trend line representing the data change trend between the hemolytic index and the rotational speed.

[0462] For example, Figure 22 As shown, Figure 22 The data may include a trend line (as indicated by arrow X3) representing the data change trend between the cardiac index and the rotational speed. Figure 22 The figure also includes a trend line (as indicated by arrow Y3) that represents the data change trend between the hemolysis index and the rotation speed.

[0463] In some scenarios, users can select the speed in combination with multiple cardiovascular parameter data. Therefore, the trend line of the data change trend between multiple cardiovascular parameters and the speed can be intuitively displayed in the same data statistical chart, so as to facilitate users to select the speed in combination with multiple cardiovascular parameter data and improve the accuracy of speed selection. For example, the user can combine the trend line of the cardiac index indicated by the arrow X3 and the trend line of the hemolytic index indicated by the arrow Y3 to select the speed setting data that both the cardiac index data and the hemolytic index data meet the conditions. Thereby improving the efficiency of speed setting data selection and reducing the possibility of misselection. In this application, a digital twin model was established for all 11 patients in Table 1 at the first postoperative time point (T1), and multi-pump speed (speed) simulation was performed at the T1 time point. Some of the measured data are described in Tables 6 and 9. By combining the patient-specific body surface area (BSA) data, the native cardiac index (Cl heart) and hemolytic index (MlH) corresponding to each pump speed are calculated, as shown in Figure 6. Figure 26 and Figure 27 As shown. Among them, Figure 26 Figure 3 shows the native cardiac index data of 11 patients at different pump speeds. Figure 27 (For ease of illustration, only a portion of the curve is labeled.) Data for the hemolytic index of 11 patients at different pump speeds are shown. The results show that the Cl heart zeroing threshold speed setting data varied among patients. When the speed setting exceeded this threshold, the mean arterial pressure (MAP) slope in the simulated data changed significantly, indicating that the mechanical circulatory assist device almost completely replaced the patient's own cardiac output. While MlH values varied between patients, they generally showed similar trends.

[0464] Table 6

[0465]

[0466]

[0467] Table 7

[0468] Location 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

[0469] In some embodiments, the first data statistics chart further includes a recommended speed range, and the speed setting data within the recommended speed range satisfies at least one of the following conditions:

[0470] The native cardiac index data corresponding to the maximum speed setting data within the recommended speed range is greater than the first threshold;

[0471] The total cardiac index data corresponding to the minimum speed setting data within the recommended speed range is greater than or equal to a second threshold;

[0472] The maximum speed setting data within the recommended speed range is less than a first speed threshold, the first speed threshold being the speed setting data corresponding to when the native cardiac index on the first trend line is equal to the first threshold, and a first difference exists between the first speed threshold and the maximum speed setting data; optionally, the first threshold is 0;

[0473] The hemolysis index data corresponding to the maximum speed setting data within the recommended speed range is less than the third threshold value.

[0474] When a mechanical circulatory assist device is assisting a subject, the subject's native heart must maintain at least a minimum blood output. Therefore, when the mechanical circulatory assist device is operating at its maximum speed setting, the native heart must still maintain a minimum blood output to prevent the risk of ventricular suction caused by excessive speed. Specifically, the native cardiac index corresponding to the maximum speed setting must be greater than a first threshold. When the mechanical circulatory assist device is operating at its minimum speed setting, both the native heart's blood output and the mechanical circulatory assist device's blood output must also meet the subject's minimum blood circulation needs. Specifically, the total cardiac index corresponding to the minimum speed setting must be greater than a second threshold.

[0475] Therefore, the recommended rotational speed setting data can be selected based on the first threshold value and the second threshold value. The recommended rotational speed interval includes the rotational speed range of the recommended rotational speed setting data.

[0476] Furthermore, the maximum speed setting within the recommended speed range can be lower than the speed setting corresponding to the first threshold. The first difference can be set based on a safe margin of native cardiac index, ensuring that at the maximum speed setting, the subject's native cardiac output is still maintained, preventing ventricular pumping. This improves the subject's safety during operation of the mechanical circulatory assist device. An exemplary first difference can be 500 RPM.

[0477] The speed setting data is positively correlated with the hemolysis index data. The third threshold value can be set based on the safety of the hemolysis index. Setting the hemolysis index data corresponding to the maximum speed setting data within the recommended speed range to be less than the third threshold value can reduce the risk of the mechanical circulatory assist device operating in a speed range where the hemolysis index data is in an unsafe range.

[0478] In one possible implementation, the second threshold is 2.2.

[0479] After obtaining the relationship curve between the speed setting data and the hemolytic index data or the relationship curve between the speed setting data and the cardiac index data, a recommended speed range that is friendly to the patient's blood and is given based on the current machine performance can be determined according to corresponding conditions.

[0480] For example, Figure 22 As shown, Figure 23In the coordinate system, the horizontal axis represents the speed, and the vertical axis represents the native cardiac index. Figure 22 A trend line (indicated by arrow X3) representing the data change trend between the cardiac index and the rotation speed may be included, such as Figure 22 The data line (indicated by arrow Y3 ) representing the data change trend between the hemolysis index and the rotation speed is also included.

[0481] like Figure 23 As shown, Figure 23 In the coordinate system, the horizontal axis represents the speed, and the vertical axis represents the native cardiac index. Figure 23 The trend line (indicated by arrow X4) representing the data change trend between the cardiac index and the speed may be included, such as Figure 22 and Figure 23 The figure also includes a trend line (indicated by arrow Y4) that represents the data change trend between the hemolysis index and the rotation speed.

[0482] For example, Figure 21 As shown in FIG, after obtaining the relationship curve between the speed setting data and the total cardiac index data, the recommended speed range for the current machine performance is determined based on the preset threshold of the cardiac index data and displayed to the doctor through the screen to prevent the risk of aspiration. Specifically, the recommended speed range includes a safe speed rpm range, such as Figure 21 The arrow A3 in FIG. 1 indicates a range in which the total cardiac index (CI_total) exceeds 2.2, the local cardiac index (CI_heart) remains greater than zero, and a conservative 500 rpm margin is subtracted from the lower boundary (e.g., Figure 21 As shown by arrow Y1, the horizontal axis starts at 500 rpm. In the range indicated by arrow A3, the total cardiac index (CI_total) exceeds 2.2, meeting the total cardiac index condition, and the speed data is less than 2500, which can prevent suction. Therefore, the range of 500-2500 rpm is set as the recommended speed range.

[0483] like Figure 22 As shown in the figure, the range indicated by arrow A4 is the recommended speed range. The range indicated by arrow B4 is the set first speed setting data. As shown in the figure, the range indicated by arrow A5 is the recommended speed range. The range indicated by arrow B5 is the set first speed setting data.

[0484] Figure 22 As shown, the first speed setting data is within the recommended speed range (indicated by arrow A4), and the cardiac index data and hemolytic index data corresponding to the first speed setting data both meet the requirements. Therefore, the mechanical circulatory assist device operates in a state that ensures the safety of the target subject. Here, the safe state can be a state where the cardiac index data is greater than the cardiac index data threshold and the hemolytic index data is less than the hemolytic index data threshold.

[0485] Figure 23 As shown, the first speed setting data is outside the recommended speed range, the heart index data corresponding to the first speed setting data cannot meet the requirements, and the first speed setting data is less than the minimum value of the recommended speed range. The mechanical circulatory assist device may have insufficient support at this time. Medical staff can conduct further inspections to confirm whether there is an actual situation of insufficient support.

[0486] By setting the recommended speed range, the user can instantly determine whether the current speed setting data is reasonable through the recommended speed range, and can make timely adjustments, thereby improving the safety of the target object when using the mechanical circulatory assist device.

[0487] Here, this embodiment is described in conjunction with the 11 target objects in Table 2. Specifically, the safe range of rpm (i.e., the recommended speed range) can be determined, such as Figure 22 and Figure 23 The ranges indicated by arrows A4 and A5 are composed of rpm with CI_total > 2.2 and rpm with CI_heart > 0 minus 500 rpm, respectively, and compared with the actual clinical data (i.e., the recommended speed range): Figure 22 and Figure 23 The ranges indicated by arrows B4 and B5 in the figure are compared. The two regions consist of the historical adjustment speed range from the first day of clinical use of the mechanical circulatory assist device to the final end of use. In 5 of the 11 patients, the mechanical circulatory assist device settings failed to provide adequate cardiac support, such as Figure 23 For one of them, Figure 23 In the example, the first speed setting data is outside the recommended speed range. Therefore, there is a risk that the cardiovascular parameter data does not meet the requirements. Therefore, selecting other settings (speed setting data) may make the overall application safer. Figure 22 This is one of the overlapping ranges indicated by arrows A4 and B4. The range indicated by arrow B4 is included in the range indicated by arrow A4, indicating that the first speed setting data is within the recommended speed range. Furthermore, the appropriate support range for mechanical circulatory assist devices varies significantly between patients (1780 rpm-2900 rpm, CI_total 1.6-2.9) and also varies between medical centers (1780 rpm-2900 rpm, CI_total 1.7-2.9).

[0488] In some embodiments, the first parameter setting data includes first speed setting data, and the user interface is further configured to perform at least one of the following operations:

[0489] If the first speed setting data does not fall within the recommended speed range, a speed adjustment prompt message is displayed;

[0490] When the first speed setting data is greater than the maximum speed setting data, at least one of over-support risk warning information, aspiration risk information, and blood damage risk information is displayed;

[0491] When the first rotational speed setting data is less than the minimum rotational speed setting data, insufficient support risk prompt information is displayed.

[0492] The maximum and minimum speed setting data define the speed range of the recommended speed interval. If the first speed setting data does not fall within the recommended speed interval, abnormal cardiovascular parameter data of the target subject may occur, such as an excessively high hemolytic index, insufficient support, or excessive support. Therefore, if the first speed setting data does not fall within the recommended speed interval, the user interface can display a speed adjustment prompt to the user, prompting the user to adjust the speed, thereby improving the safety of the target subject during the operation of the mechanical circulatory assist device.

[0493] When the first speed setting exceeds the maximum speed setting, certain risks may arise, such as ventricular or atrium collapse due to excessive pumping, or hemolysis index exceeding a safe range. Therefore, the user interface may display an over-support risk warning message when the first speed setting exceeds the maximum speed setting, to remind the user to adjust the speed.

[0494] When specific safety risks are identified based on cardiovascular parameter data, the specific risk type can be displayed. For example, if the cardiovascular parameter data indicates a high risk of aspiration, aspiration risk information can be displayed. If the cardiovascular parameter data indicates a high risk of blood damage, blood damage risk information can be displayed. By displaying specific risk types, users can directly identify the risk type, improving their risk assessment efficiency, reducing user response time, and improving the safety of the target user.

[0495] If the first speed setting is lower than the minimum speed setting, the cardiovascular system may not receive adequate support, resulting in blood circulation risks. The user interface can promptly display a warning message indicating insufficient support risk to remind the user to adjust the speed. This allows the user to respond promptly, improving the safety of the subject.

[0496] In some embodiments, the operating setting parameter includes a rotational speed, and the data change relationship prediction information includes a second data statistical chart, the second data statistical chart being used to characterize a data change trend between the pressure-volume loop and the rotational speed;

[0497] The second data statistical chart includes pressure-volume loop curves corresponding to a plurality of speed setting data, the pressure-volume loop curves being used to represent the changing relationship between the ventricular pressure and the ventricular volume corresponding to the ventricle;

[0498] The plurality of speed setting data includes first speed setting data and a plurality of second speed setting data, the first speed setting data is a target speed currently set for the mechanical circulatory assist device, and the first speed setting data is different from the second speed setting data;

[0499] The multiple pressure-volume loop curves include a pressure-volume loop monitoring curve corresponding to the first speed setting data and multiple pressure-volume loop prediction curves. The multiple pressure-volume loop prediction curves correspond one-to-one to the multiple second speed setting data.

[0500] The pressure-volume loop describes the changing relationship between ventricular pressure and volume in the heart (usually the left ventricle). A pressure-volume loop curve uses a curve in a coordinate system to represent the changing relationship between ventricular pressure and volume.

[0501] The control device can determine a pressure-volume loop curve by predicting the ventricular pressure data and ventricular volume data. Unless otherwise specified, the process of determining the pressure-volume loop curve by the control device by predicting the ventricular pressure data and ventricular volume data is referred to as "predicting the pressure-volume loop curve" in this embodiment.

[0502] Here, the first speed setting data may be a target speed used by the current control device to control the operation of the drive component. The second speed setting data may include one or more speed setting data other than the first speed setting data.

[0503] The pressure-volume loop monitoring curve may be obtained by predicting the pressure-volume loop curve of the target object by the control device when the drive assembly is running at the first speed setting data.

[0504] The pressure-volume loop prediction curve can be obtained by the control device predicting the pressure-volume loop of the target object when the drive assembly operates at the second speed setting data. The control device can predict the pressure-volume loop prediction curve corresponding to one or more second speed setting data.

[0505] Each speed setting data in the second data statistics chart corresponds to a predicted pressure-volume loop curve. For example, the first speed setting data corresponds to a pressure-volume loop monitoring curve, and each second speed setting data corresponds to a pressure-volume loop prediction curve.

[0506] For example, Figure 24 As shown, Figure 24 There are three pressure-volume loop curves ( Figure 24The three speed setting data are predicted by the control device.

[0507] The second data statistical chart can be used to determine the pressure-volume loop curves of the mechanical circulatory assist device providing cardiovascular assistance under the first and second speed settings, thereby allowing the user to select the appropriate speed setting for the mechanical circulatory assist device. The user can evaluate the rationality of the currently used first speed setting to improve the accuracy of the speed setting.

[0508] In some embodiments, the cardiovascular parameter further includes ventricular elastance, and the second statistical chart is further used to represent the data change trend between ventricular elastance and rotational speed;

[0509] The second data statistical chart includes ventricular elasticity lines corresponding to a plurality of rotational speed setting data. The slope of the ventricular elasticity line is associated with the ventricular elasticity. The slope of the ventricular elasticity line can represent the left ventricular elasticity or the ventricular contractility.

[0510] The plurality of speed setting data includes first speed setting data and a plurality of second speed setting data, the first speed setting data is a target speed currently set for the mechanical circulatory assist device, and the first speed setting data is different from the second speed setting data;

[0511] The multiple ventricular elasticity lines include a ventricular elasticity monitoring line corresponding to the first rotational speed setting data and multiple ventricular elasticity prediction lines. The multiple ventricular elasticity prediction lines correspond one-to-one to the multiple second rotational speed setting data.

[0512] Ventricular elastance usually refers to the elastic properties of the ventricular wall, which 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).

[0513] In one possible implementation, the ventricular elastance line can be determined based on the pressure-volume loop. The ventricular elastance line can be an 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 represent ventricular elastance.

[0514] The control device can predict the ventricular pressure data and ventricular volume data to determine a pressure-volume loop curve, thereby determining the ventricular elasticity line. The process of determining the ventricular elasticity line by predicting the ventricular pressure data and ventricular volume data is referred to as "predicting the ventricular elasticity line" in this embodiment unless otherwise specified.

[0515] Here, the first speed setting data may be a target speed used by the current control device to control the operation of the drive component. The second speed setting data may include one or more speed setting data other than the first speed setting data.

[0516] The pressure ventricular elasticity monitoring line may be obtained by predicting the ventricular elasticity of the target object by the control device when the driving component is running at the first rotation speed setting data.

[0517] The ventricular elastance prediction line can be obtained by the control device predicting the ventricular elastance of the target object when the drive assembly operates at the second speed setting data. The control device can predict the ventricular elastance prediction line corresponding to one or more second speed setting data.

[0518] Each speed setting data in the second data statistics chart corresponds to a predicted pressure ventricular elasticity line. For example, the first speed setting data corresponds to the pressure ventricular elasticity monitoring line, and each second speed setting data corresponds to a ventricular elasticity prediction line.

[0519] For example, Figure 24 As shown, Figure 24 There are three ventricular elasticity prediction lines ( Figure 24 The arrows a, b and c in the middle are obtained by predicting the three speed data by the control device.

[0520] The second data statistical chart can be used to determine the ventricular elasticity line of the mechanical circulatory assist device for cardiovascular support under the first and second speed settings, thereby selecting the appropriate speed setting for the mechanical circulatory assist device. The user can evaluate the rationality of the currently used first speed setting to improve the accuracy of the speed setting.

[0521] In some embodiments, the second data statistical chart further includes recommended speed information, and the speed setting data in the recommended speed information satisfies at least one of the following:

[0522] The pressure-volume loop curve corresponding to the speed setting data in the recommended speed information meets the pressure-volume loop condition;

[0523] The ventricular elasticity line corresponding to the speed setting data in the recommended speed information satisfies the ventricular elasticity condition;

[0524] The user interface is also configured to:

[0525] In a case where the first speed setting data does not match the recommended speed information, speed adjustment prompt information and / or risk prompt information indicating ventricular contraction performance are displayed.

[0526] The control device may be configured with selection criteria for the pressure-volume loop curve and / or the ventricular elasticity line. The control device may select, from the pressure-volume loop curve and / or ventricular elasticity line in the second data statistical chart, speed setting data corresponding to the pressure-volume loop curve and / or ventricular elasticity line that meets the selection criteria as the recommended speed setting data. The user interface may display the recommended speed setting data to the user via the recommended speed information.

[0527] In one possible implementation, the user interface may be displayed by displaying the recommended speed setting data and / or marking the recommended speed setting data.

[0528] After obtaining the speed setting data, the user can determine whether the first speed setting data is correct or reasonable by determining whether the pressure-volume loop curve satisfies the pressure-volume loop condition and whether the ventricular elasticity line satisfies the ventricular elasticity condition. Furthermore, based on the recommended speed information, the user can select target speed setting data based on whether the pressure-volume loop curve satisfies the pressure-volume loop condition and whether the ventricular elasticity line satisfies the ventricular elasticity condition. The control device then uses the speed setting data to control the drive assembly. The speed setting data can assist the user in making pre-determined speed setting data, reducing the burden on the user to evaluate the speed setting data. It can also allow the user to verify the currently selected first speed setting data, further improving the safety of mechanical circulatory assist devices.

[0529] If the first speed setting data does not match the recommended speed information, that is, the first speed setting data exceeds the maximum speed setting data in the recommended speed setting data, or the first speed setting data is lower than the minimum speed setting data in the recommended speed setting data, certain safety risks may arise, such as the volume loop curve and / or the ventricular elasticity line not meeting the selection conditions. Therefore, the user interface can display a speed adjustment prompt message when the first speed setting data does not match the recommended speed information to remind the user to adjust the speed, thereby improving the safety of the target object.

[0530] Based on the pressure-volume loop curve and / or ventricular elasticity line, specific safety risks can be determined. For example, if a ventricular contractility risk is identified, the specific risk type can be displayed. For example, a ventricular contractility risk warning message can be displayed. By displaying this ventricular contractility risk warning message, users can determine the risk type, improve their risk assessment efficiency, reduce user response time, and enhance the safety of the target subject.

[0531] In some embodiments, the operating setting parameter includes a 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 being used to represent a data change trend between ventricular elastance and rotational speed;

[0532] The third data statistical chart includes ventricular elasticity data corresponding to the plurality of speed setting data;

[0533] The plurality of speed setting data includes first speed setting data and a plurality of second speed setting data, the first speed setting data is a target speed currently set for the mechanical circulatory assist device, and the first speed setting data is different from the second speed setting data;

[0534] The plurality of ventricular elasticity data include ventricular elasticity monitoring data corresponding to the first rotation speed setting data and a plurality of ventricular elasticity prediction data. The plurality of ventricular elasticity prediction data corresponds one-to-one to the plurality of second rotation speed setting data.

[0535] Unlike the second data statistical chart, the third data statistical chart can display ventricular elasticity data alone. Of course, the third data statistical chart can also display other cardiovascular parameters, which is not limited in this embodiment of the present application.

[0536] In one possible implementation, the control device may predict the ventricular elasticity data.

[0537] In a possible implementation, the ventricular elasticity data may include a specific ventricular elasticity value, or may include a curve representing the ventricular elasticity.

[0538] Here, the first speed setting data may be a target speed used by the current control device to control the operation of the drive component. The second speed setting data may include one or more speed setting data other than the first speed setting data.

[0539] The pressure ventricular elasticity monitoring data may be obtained by predicting the ventricular elasticity of the target object by the control device when the driving component is running at the first speed setting data.

[0540] The ventricular elastance prediction data may be obtained by the control device predicting the ventricular elastance of the target subject when the drive assembly is operated at the second speed setting data. The control device may predict the ventricular elastance prediction data corresponding to one or more second speed setting data.

[0541] In the third data statistics chart, each speed setting data corresponds to a predicted pressure ventricular elasticity data. For example, the first speed setting data corresponds to the pressure ventricular elasticity monitoring data, and each second speed setting data corresponds to a ventricular elasticity prediction data.

[0542] The third statistical chart can be used to determine the ventricular elastance data of the mechanical circulatory assist device providing cardiovascular support under the first and second speed settings, thereby selecting the appropriate speed setting for the mechanical circulatory assist device. The user can evaluate the rationality of the currently used first speed setting to improve the accuracy of the speed setting.

[0543] In some embodiments, the third data statistical chart further includes recommended speed information, and the ventricular elasticity data corresponding to the speed setting data in the recommended speed information satisfies the ventricular elasticity condition;

[0544] The user interface is also configured to:

[0545] In a case where the first speed setting data does not match the recommended speed information, speed adjustment prompt information and / or risk prompt information indicating ventricular contraction performance are displayed.

[0546] The control device may be configured with a ventricular elasticity condition for the ventricular elasticity data. The control device may select, from the ventricular elasticity data in the third data statistical chart, speed setting data corresponding to the ventricular elasticity data that satisfies the ventricular elasticity condition as the recommended speed setting data. The user interface may display the recommended speed setting data to the user via the recommended speed information.

[0547] In one possible implementation, the user interface may be displayed by displaying the recommended speed setting data and / or marking the recommended speed setting data.

[0548] After obtaining the speed setting data, the user can verify the current first speed setting data by checking whether the ventricular elastance data meets the ventricular elastance conditions to determine whether the first speed setting data is correct or reasonable. Furthermore, the user can select a target speed setting data based on the recommended speed information, and the control device will use this speed setting data to control the drive assembly. The speed setting data can assist the user in making pre-determined speed setting data decisions, reducing the burden on the user to evaluate speed setting data. It also allows the user to verify the currently selected first speed setting data, further improving the safety of mechanical circulatory assist devices.

[0549] If the first speed setting data does not match the recommended speed information, that is, the first speed setting data exceeds the maximum speed setting data in the recommended speed setting data, or the first speed setting data is lower than the minimum speed setting data in the recommended speed setting data, certain safety risks may arise, such as ventricular elastance data not meeting ventricular elastance conditions. Therefore, the user interface can display a speed adjustment prompt message when the first speed setting data does not match the recommended speed information to remind the user to adjust the speed, thereby improving the safety of the target object.

[0550] Specific safety risks are determined based on ventricular elastance data. For example, if a ventricular contractility risk is identified, the specific risk type can be displayed. For example, a risk warning message for ventricular contractility risk can be displayed. This allows users to identify the risk type, improving risk assessment efficiency, reducing response time, and improving the safety of the target subject.

[0551] In some embodiments, the operating setting parameter includes a rotational speed, the blood damage parameter includes a hemolysis index, and the data change relationship prediction information includes a fourth data statistical chart, the fourth data statistical chart is used to represent a data change trend between the hemolysis index and the rotational speed, and the fourth data statistical chart includes hemolysis index data corresponding to a plurality of rotational speed setting data respectively;

[0552] The plurality of speed setting data includes first speed setting data and a plurality of second speed setting data, the first speed setting data is a target speed currently set for the mechanical circulatory assist device, and the first speed setting data is different from the second speed setting data;

[0553] The multiple hemolysis index data include hemolysis index monitoring data corresponding to the first speed setting data and multiple hemolysis index prediction data. The multiple hemolysis index prediction data correspond to the multiple second speed setting data in a one-to-one manner.

[0554] The blood pump in a mechanical circulatory assist device 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.

[0555] In a possible implementation, the control device may predict the hemolysis index data.

[0556] In a possible implementation, the hemolysis index data may include a specific hemolysis index value, or may include a curve representing the hemolysis index.

[0557] Here, the first speed setting data may be a target speed used by the current control device to control the operation of the drive component. The second speed setting data may include one or more speed setting data other than the first speed setting data.

[0558] The hemolysis index monitoring data may be obtained by the control device predicting the hemolysis index of the target object when the driving component is running at the first speed setting data.

[0559] The hemolysis index prediction data can be obtained by the control device predicting the hemolysis index of the target object when the drive component operates at the second speed setting data. The control device can predict the hemolysis index prediction data corresponding to one or more second speed setting data.

[0560] In the fourth data statistics chart, each speed setting data corresponds to the predicted hemolysis index data. For example, the first speed setting data corresponds to the hemolysis index monitoring data, and each second speed setting data corresponds to a hemolysis index prediction data.

[0561] The fourth statistical chart can be used to determine the hemolytic index data of the mechanical circulatory assist device's cardiovascular support under the first and second speed settings, thereby selecting the appropriate speed setting for the mechanical circulatory assist device. The user can evaluate the rationality of the currently used first speed setting to improve the accuracy of the speed setting and reduce the damage to the blood caused by the mechanical circulatory assist device.

[0562] In some embodiments, the fourth data statistical chart further includes a recommended speed range, and the hemolysis index data corresponding to the speed setting data within the recommended speed range is lower than the third threshold;

[0563] The user interface is further configured to perform at least one of the following operations:

[0564] If the first speed setting data does not fall within the recommended speed range, a speed adjustment prompt message is displayed;

[0565] When the first speed setting data is greater than the maximum speed setting data in the recommended speed range, hemolysis risk prompt information is displayed.

[0566] The control device may be configured with a hemolytic index condition, namely, a third threshold, for the hemolytic index data. If the hemolytic index data is below the third threshold, it indicates that the mechanical circulatory assist device has caused minimal blood damage. The control device may select, from the ventricular elastance data in the fourth data statistical chart, the speed setting data corresponding to the hemolytic index data below the third threshold as the recommended speed setting data, and determine a speed range for the recommended speed setting data, namely, a recommended speed range. The user interface may display the recommended speed setting data to the user via the recommended speed range.

[0567] In a possible implementation, the recommended rotation speed range may be determined by the control device in combination with the hemolysis index data and at least one other cardiovascular parameter data.

[0568] In one possible implementation, the user interface may be displayed by displaying the recommended speed setting data and / or marking the recommended speed setting data.

[0569] After obtaining the speed setting data, the user can verify the current first speed setting data and determine whether the hemolysis index data is below a third threshold to determine whether the first speed setting data is correct or reasonable. Furthermore, the user can select a target speed setting data based on the recommended speed information, and the control device will use the speed setting data to control the drive assembly. The speed setting data can assist the user in making pre-determined speed setting data decisions, reducing the burden on the user to evaluate the speed setting data. It also allows the user to verify the currently selected first speed setting data, further improving the safety of the mechanical circulatory assist device.

[0570] If the first speed setting data does not match the recommended speed range, it may pose a safety risk to the target object. The user interface can display a speed adjustment prompt message when the first speed setting data does not match the recommended speed information to remind the user to adjust the speed, thereby improving the safety of the target object.

[0571] If the first speed setting exceeds the maximum speed setting in the recommended speed setting, the hemolysis index may exceed the third threshold, thereby creating a certain safety risk. Therefore, a hemolysis risk warning message may be displayed. By displaying the hemolysis risk warning message, the user can determine the risk type, improve the user's risk assessment efficiency, reduce user response time, reduce the damage to the blood caused by the mechanical circulatory assist device, and improve the safety of the target subject.

[0572] In some embodiments, cardiovascular parameter monitoring data is presented as a monitoring data statistical chart, which includes monitoring data corresponding to cardiovascular parameters at multiple time nodes. The monitoring data statistical chart is used to show changes in the physiological state of the cardiovascular system during support of a mechanical circulatory assist device.

[0573] Here, the multiple time nodes may include multiple historical time nodes.

[0574] Controlling the device to record the monitoring data corresponding to the multiple time nodes may include: controlling the device to predict cardiovascular parameters corresponding to the first parameter setting data at the multiple time nodes. The first parameter setting data at each time node may be the same or different.

[0575] In one possible implementation, the control device may record each time node and the corresponding cardiovascular parameter monitoring data.

[0576] The monitoring data statistical chart can first display each time node and the cardiovascular parameter monitoring data corresponding to each time node.

[0577] Monitoring data statistical charts can present monitoring data corresponding to multiple time points of a mechanical circulatory assist device by listing multiple time points and corresponding cardiovascular parameter monitoring data. Monitoring data statistical charts can also present monitoring data corresponding to multiple time points of a mechanical circulatory assist device by providing a curve showing the changing relationship between multiple time points and cardiovascular parameter monitoring data. Through monitoring data statistical charts, users can intuitively observe the cardiovascular parameter monitoring data of the target subject's heart at multiple time points, thereby determining the recovery of cardiac function.

[0578] By using monitoring data corresponding to multiple time points, users can determine the cardiovascular parameter monitoring data corresponding to different operating parameter settings at historical time points. This allows users to determine the evolution of the target subject's cardiovascular parameter data and more accurately determine the target subject's cardiac function.

[0579] In some embodiments, the monitoring data statistical chart includes at least one of the following:

[0580] A cardiac contractility performance statistical chart, comprising ventricular elastance monitoring data corresponding to multiple time points and / or pressure-volume loop monitoring curves corresponding to multiple time points, the cardiac contractility performance statistical chart being used to characterize changes in cardiac contractility during mechanical circulatory assist device support;

[0581] A cardiac index monitoring data chart, which includes cardiac index monitoring data corresponding to multiple time nodes and is used to represent changes in cardiac output during mechanical circulatory assist device support;

[0582] The blood damage monitoring data chart includes blood damage parameter monitoring data corresponding to multiple time nodes. The blood damage monitoring data chart is used to characterize the damage of blood during the support process of mechanical circulatory assist device.

[0583] Monitoring data statistical charts can be used to display cardiovascular parameter data related to mechanical circulatory assist devices.

[0584] The control device predicts at least one of a pressure-volume loop curve, cardiac index monitoring data, and blood damage parameter monitoring data corresponding to multiple time nodes. Monitoring of the pressure-volume loop curve, cardiac index monitoring data, and blood damage parameter monitoring data can be displayed using a cardiac contractility performance statistics chart, a cardiac index monitoring data chart, and a blood damage monitoring data chart, respectively.

[0585] The cardiac contraction 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 corresponding pressure-volume loop monitoring curves. For example, Figure 25 As shown, the pressure-volume loop monitoring curves indicated by arrows J, K, and L can be monitored at three time points. 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. This shows that the heart's pressure-volume loop data is gradually improving.

[0586] For example, Figure 25 As shown, Figure 25 There are three ventricular elasticity prediction lines ( Figure 25 The arrows j, k and l in the middle are respectively monitored by the control device at three time nodes.

[0587] The cardiac index monitoring data chart can represent the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by listing multiple time nodes and corresponding cardiac index monitoring data; it can also represent the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by a curve of the change relationship between multiple time nodes and cardiac index monitoring data.

[0588] The blood damage monitoring data chart can represent the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by listing multiple time nodes and corresponding blood damage parameter monitoring data; it can also represent the monitoring data corresponding to multiple time nodes of the mechanical circulatory assist device by a change relationship curve between multiple time nodes and blood damage parameter monitoring data.

[0589] By using monitoring data corresponding to multiple time points, users can determine the cardiovascular parameter monitoring data corresponding to different operating parameter settings at historical time points. This allows users to determine the development of the target subject's cardiovascular parameter data and accurately assess their condition. For example, using a statistical chart of cardiac contractility, a chart of cardiac index monitoring data, and a chart of blood damage monitoring data, users can assess the target subject's cardiac contractility, cardiac index, and blood damage, respectively, thereby determining the recovery of cardiac function.

[0590] In some embodiments, the user interface is further configured to:

[0591] When the monitoring data statistics chart indicates that the cardiovascular system's performance has recovered, display the weaning recommendation information;

[0592] And / or, displaying risk warning information when statistical charts of monitoring data indicate deterioration in the performance of the cardiovascular system.

[0593] The monitoring data statistics and charts can serve as a basis for the user to assess the target subject's condition. If the monitoring data and charts indicate that the target subject's cardiovascular system has recovered, the user interface can display weaning recommendations for the user's reference. Based on the displayed weaning recommendations, the user can conduct a comprehensive assessment of the cardiovascular system's performance and determine a subsequent treatment plan. The weaning recommendations can indicate that the target subject's condition is suitable for self-circulation without mechanical circulatory assist devices.

[0594] 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 is lower than the corresponding threshold or does not meet the corresponding conditions, the user interface can display risk warning information.

[0595] By displaying weaning recommendation information and / or risk warning information, automated monitoring of cardiovascular parameters of mechanical circulatory assist devices can be achieved, which can reduce the requirement for users to manually and continuously observe monitoring data, and reduce the burden of manual and continuous judgment of cardiac index data, pressure-volume loop data, and ventricular elastance data.

[0596] In one possible implementation, fitting can be performed for multiple target users, resulting in corresponding cardiovascular models, such as multiple LPM models, which are then integrated into the software platform. Users (typically medical device companies) can upload data related to a mechanical circulatory assist device to be tested. The software platform can then fit a mechanical circulatory assist model, such as a reduced-order model, for the device under test. This reduced-order model is then integrated with the hemodynamic models of the multiple target users to generate trend lines for cardiovascular parameter data corresponding to the multiple target users, thereby evaluating the performance of the device.

[0597] In one possible implementation, corresponding mechanical circulatory assist models, such as multiple reduced-order models, can be created for multiple mechanical circulatory assist devices and integrated into a software platform. A user (e.g., a doctor) can upload data related to a target patient. The software platform then fits a cardiovascular model, such as the LPM model, for the target patient. The LPM model is then coupled with multiple reduced-order models to generate trend lines for cardiovascular parameter data corresponding to each reduced-order model coupling. This allows the platform to identify a suitable ventricular assist device for the target patient before the device is installed.

[0598] For example, based on the aforementioned 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 the patient's 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 under different pump speeds.

[0599] In some embodiments, the control device is further configured to perform:

[0600] obtaining 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;

[0601] Acquiring physiological parameter data associated with the cardiovascular system, and acquiring operating setting parameter data of the mechanical circulatory assist device;

[0602] 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.

[0603] 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 of the implementation methods in Examples 1 to 4, and will not be repeated here.

[0604] In some embodiments, the control device is specifically configured to perform:

[0605] Acquiring 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 includes a reduced-order model obtained by reducing the order of a computational fluid dynamics model corresponding to the mechanical circulatory assistance device;

[0606] 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.

[0607] 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.

[0608] In some embodiments, the control device is specifically configured to perform:

[0609] said fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0610] Traverse multiple groups of parameter setting data corresponding to the running setting parameters;

[0611] When traversing to the current set of parameter setting data, driving the blood circulation model to run based on the model parameter data and the parameter setting data, and outputting cardiovascular parameter data corresponding to the cardiovascular system;

[0612] The data change relationship prediction information is determined based on the cardiovascular parameter data corresponding to the multiple groups of parameter setting data.

[0613] Here, the cardiovascular parameter data corresponding to the multiple sets of parameter setting data are determined by traversing the running setting parameters, and the specific implementation method of the data change relationship prediction information is determined, as described in any implementation method of Examples 1 to 4, which will not be repeated here.

[0614] 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 a ventricle, and the lumped parameter model is connected to the mechanical circulatory assist model via pressure-flow coupling;

[0615] The control device is specifically configured to execute:

[0616] said fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0617] driving the cardiovascular model to run based on the model parameter data, and outputting pressure gradient data between the drainage position and the backflow position;

[0618] Inputting the pressure gradient data and the parameter setting data into the mechanical circulation assistance model to drive the blood circulation model to operate, and outputting the flow data and blood damage data of the mechanical circulation assistance model;

[0619] inputting the flow data into the lumped parameter model to update the current change in the circuit structure;

[0620] determining native cardiac output data based on the current data of the variable capacitor;

[0621] determining ventricular pressure change data based on the voltage change data of the variable capacitor;

[0622] determining the volume change data of the ventricle based on the charge amount change data of the variable capacitor;

[0623] determining total cardiac output data based on the native cardiac output data and the flow data;

[0624] Determine at least one of pressure-volume loop data and ventricular elasticity data based on the ventricular pressure change data and the ventricular volume change data

[0625] determining native cardiac index data based on native cardiac output data;

[0626] Total cardiac index data is determined based on the total cardiac output data.

[0627] Here, the specific implementation of determining cardiovascular parameter data based on the lumped parameter model is as described in any of the implementations in Examples 1 to 4, and will not be repeated here.

[0628] like Figure 26 The present disclosure further provides a cardiovascular system performance monitoring device 10, which includes a processing module 11, which is configured to:

[0629] Obtaining a cardiovascular model corresponding to the cardiovascular system in the target subject and a mechanical circulatory assist model corresponding to the mechanical circulatory assist device, wherein 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;

[0630] Obtain the drainage and return locations of mechanical circulatory assist devices in the cardiovascular system;

[0631] According to the drainage position and the return flow position, the cardiovascular model and the mechanical circulatory assistance model are coupled to obtain a blood circulation model. The blood circulation model is used to simulate the blood flow after the mechanical circulatory assistance device is connected to the cardiovascular system.

[0632] Acquiring physiological parameter data related to the cardiovascular system and obtaining operating setting parameter data of mechanical circulatory assist devices;

[0633] The blood circulation model is driven to run based on the physiological parameter data and the operation setting parameter data, and at least one of the hemodynamic parameter data and blood damage data corresponding to the cardiovascular system is output.

[0634] In some embodiments, the processing module is specifically configured to:

[0635] Drive the cardiovascular model according to physiological parameter data and output the pressure gradient data between the drainage position and the return position;

[0636] The pressure gradient data and the operation setting parameter data are input into the mechanical circulation assistance model to drive the operation of the blood circulation model and output at least one of the hemodynamic parameter data and blood damage data corresponding to the cardiovascular system.

[0637] In some embodiments, the processing module is specifically configured to:

[0638] Fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the target object;

[0639] Driving the cardiovascular model to run based on the model parameter data, determining first pressure data corresponding to the drainage position in the cardiovascular model, and second pressure data corresponding to the return flow position in the cardiovascular model;

[0640] Based on the first pressure data and the second pressure data, pressure gradient data is output.

[0641] In some embodiments, the processing module is specifically configured to:

[0642] During a first period after the mechanical circulatory assist device is placed on the machine, a first number of model parameters in the cardiovascular model are fitted according to the physiological parameter data corresponding to the first period, to obtain 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;

[0643] In a second time period after the first time period, a second number of model parameters in the cardiovascular model are fitted according to the physiological parameter data corresponding to the second time period to obtain second fitting data corresponding to the second number of model parameters in the second time period. The second fitting data is used to drive the cardiovascular model, and the first number and the second number are different.

[0644] In some embodiments, the second amount is less than the first amount.

[0645] In some embodiments, the first number of model parameters includes: left ventricular elastance, systemic peripheral resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elastance;

[0646] The second number of model parameters includes left ventricular elastance and systemic peripheral resistance.

[0647] In some embodiments, the processing module is specifically configured to:

[0648] Inputting pressure gradient data and operation setting parameter data into the mechanical circulation assistance model to drive the operation of the blood circulation model, and outputting flow data and blood damage data of the mechanical circulation assistance model;

[0649] The flow data is input into the cardiovascular model to update the operating status of the cardiovascular model and output hemodynamic parameter data.

[0650] In some embodiments, the mechanical circulatory assist device includes a power assembly and a first pipeline assembly, and the mechanical circulatory assist model includes a blood pump reduced-order model corresponding to the power assembly and a pipeline reduced-order model corresponding to the first pipeline assembly; the pipeline reduced-order model is used to determine first pressure loss data at both ends of the first pipeline assembly based on flow data;

[0651] The processing module is specifically configured to: determine the pump head pressure difference data corresponding to the power assembly based on the pressure gradient data and the first pressure loss data;

[0652] The pump head pressure difference data and operation setting parameter data are input into the blood pump reduced-order model to drive the blood circulation model to run, and the flow data and the first blood damage data corresponding to the blood pump reduced-order model are output. The first blood damage data is used to characterize the blood damage caused by the power component.

[0653] In some embodiments, the processing module is further configured to:

[0654] Obtaining first size information corresponding to the first pipeline component;

[0655] A pipeline reduced-order model is fitted based on the first dimension information.

[0656] In some embodiments, the processing module is specifically configured to:

[0657] The flow data is input into the pipeline reduction model for processing, and first pressure loss data and second blood damage data corresponding to the first pipeline component are output. The second blood damage data is used to characterize the blood damage caused by the first pipeline component.

[0658] In some embodiments, the processing module is specifically configured to:

[0659] The first blood damage data and the second blood damage data are fused to obtain total blood damage data.

[0660] In some embodiments, the mechanical circulatory assistance device further includes a second pipeline assembly, and the mechanical circulatory assistance model further includes a linear pipeline model corresponding to the second pipeline assembly; and the processing module is further configured to:

[0661] Obtaining second size information corresponding to the second pipeline component;

[0662] fitting a linear pipeline model based on the second dimension information;

[0663] Input the flow data into the linear pipeline model for processing, and output second pressure loss data at both ends of the second pipeline component;

[0664] The processing module is specifically used to:

[0665] Based on the pressure gradient data, the first pressure loss data and the second pressure loss data, the pump head pressure difference data corresponding to the power assembly is determined.

[0666] In some embodiments,

[0667] 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;

[0668] The pipeline reduction order model includes: a drainage cannula reduction order model corresponding to the drainage cannula, and a reflux cannula reduction order model corresponding to the reflux cannula; the drainage cannula reduction order model is used to determine pressure loss data at both ends of the drainage cannula based on flow data; the reflux cannula reduction order model is used to determine pressure loss data at both ends of the reflux cannula based on flow 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;

[0669] 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 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 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.

[0670] 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 data; the reflux cannula reduced-order model is used to determine the blood damage data of the reflux cannula based on the flow data; the blood damage data of the drainage cannula and the blood damage data of the reflux cannula are used to determine the second blood damage data corresponding to the first pipeline assembly.

[0671] In some embodiments, the processing module is further configured to:

[0672] Start traversing multiple sets of operation setting parameter data;

[0673] When traversing to the current set of operation setting parameter data, the step of inputting the pressure gradient data and the operation setting parameter data into the mechanical circulation assistance model to drive the blood circulation model to operate, and outputting the flow data and blood damage data of the mechanical circulation assistance model is executed;

[0674] After the multiple sets of operation setting parameter data are traversed, the hemodynamic parameter data corresponding to the multiple sets of operation setting parameter data and / or the blood damage data corresponding to the multiple sets of operation setting parameter data are obtained;

[0675] Determine 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 second change relationship information between the blood damage parameters and the operating setting parameters based on the blood damage data corresponding to the multiple sets of operating setting parameter data and the multiple sets of operating setting parameter data.

[0676] In some embodiments, the processing module is further configured to:

[0677] Parameter setting suggestion information corresponding to the operation setting parameter is output according to the first change relationship information and / or the second change information.

[0678] 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:

[0679] Native cardiac index data, total cardiac index data, pressure-volume loop data, ventricular elastance data;

[0680] Blood damage data includes at least one of the following:

[0681] Hemolysis index data, coagulation index data, thrombosis index data;

[0682] The operation setting parameter data includes at least one of the following:

[0683] Flow rate setting data, speed setting data;

[0684] Physiological parameter data includes at least one of the following:

[0685] Mean arterial pressure data, cardiac output data, blood flow data, atrial pressure data.

[0686] 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 charge and discharge circuit corresponding to the heart in the cardiovascular system, and the lumped parameter model is connected to the mechanical circulatory assist model via pressure-flow coupling;

[0687] The processing module is specifically used to:

[0688] Inputting flow data into the lumped parameter model to update the current changes in the circuit structure;

[0689] Obtaining electrical signals from a charging and discharging circuit;

[0690] Based on the electrical signals of the charge and discharge circuit, cardiac parameter data is determined.

[0691] In some embodiments, the charge and discharge circuit includes a variable capacitor for simulating a ventricle;

[0692] Determine cardiac parameter data based on the electrical signal of the charge-discharge circuit, including at least one of the following:

[0693] Determine native cardiac output data based on current data from the variable capacitor;

[0694] determining ventricular pressure change data based on the voltage change data of the variable capacitor;

[0695] determining the volume change data of the ventricle based on the charge change data of the variable capacitor;

[0696] Determine total cardiac output data based on native cardiac output data and flow data;

[0697] At least one of pressure-volume loop data and ventricular elastance data is determined based on the ventricular pressure change data and the ventricular volume change data.

[0698] Mechanical circulatory assist devices differ in structural design and applicable populations. How to detect the potential risks of mechanical circulatory assist devices in advance before real clinical trials is an urgent problem that needs to be solved.

[0699] In view of this, embodiments of the present application provide a performance testing method, device, and electronic equipment for a mechanical circulatory assist device.

[0700] Figure 28 A flow chart of a method for testing the performance of a mechanical circulatory assist device according to an embodiment is shown. The method may be executed by a controller in the mechanical circulatory assist device.

[0701] The various embodiments or implementation methods 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 various embodiments can be referenced to each other.

[0702] like Figure 28 As shown, the method includes steps 2801 to 2803.

[0703] Step 2801: Determine the mechanical circulatory assist device to be tested.

[0704] Step 2802: In response to a parameter configuration operation for a mechanical circulatory assist device, obtain parameter configuration data of the mechanical circulatory assist device.

[0705] Step 2803: In response to the test instruction for the mechanical circulatory assist device, output a virtual clinical trial result corresponding to the mechanical circulatory assist device.

[0706] Among them, the virtual clinical trial results include the predicted results of using mechanical circulatory assist devices to assist blood circulation in multiple sample subjects. The predicted results are used to predict the changes in the physiological state of the cardiovascular system in the sample subjects when the mechanical circulatory assist devices work with parameter configuration data.

[0707] The mechanical circulatory assist device to be tested can be determined based on the user's clinical trial needs.

[0708] Exemplarily, the mechanical circulatory assist device to be tested may be an internal mechanical circulatory assist device or an external mechanical circulatory assist device. For example, the mechanical circulatory assist device to be tested may be a left ventricular assist device (LVAD) or a right ventricular assist device (RVAD), or the mechanical circulatory assist device to be tested may be an external ventricular assist device or an extracorporeal membrane oxygenation device.

[0709] For example, the mechanical circulatory assistance device to be tested may be determined in response to input of fluid mechanics data, where the fluid mechanics data is used to characterize the fluid mechanics performance of the mechanical circulatory assistance device.

[0710] The fluid mechanics data of the mechanical circulatory assist device may include at least one of the following: flow-related data (e.g., 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 changes in blood flow at different positions of the mechanical circulatory assist device, and the flow velocity data is used to describe the flow speed 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 changes inside the mechanical circulatory assist device, and the peak pressure data is used to indicate the maximum pressure value that may be reached when the mechanical circulatory assist device is in 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 to which the blood is subjected in the mechanical circulatory assist device.

[0711] By determining the mechanical circulatory assist device to be tested in response to the input of fluid mechanics data, the physical characteristics (such as flow characteristics and pressure characteristics) exhibited by the mechanical circulatory assist device when driving blood flow can be fully considered in a virtual clinical trial environment, thereby improving the reliability of the performance test of the mechanical circulatory assist device.

[0712] As another example, the mechanical circulatory assist device to be tested may be determined in response to an input operation of a device identification. The device identification may be, for example, information such as the name and model of the mechanical circulatory assist device.

[0713] For example, sample subjects can be used to test the performance of mechanical circulatory assist devices in virtual clinical trials. For example, the sample subjects may be patients with cardiovascular dysfunction, such as heart failure, arrhythmia, or vascular stenosis. The type, severity, physiological characteristics, or age range of the dysfunction may vary among the sample subjects.

[0714] Illustratively, the parameter configuration operation for the mechanical circulatory assist device may include: selecting or setting parameters such as flow rate, pump speed, pipe size and / or connection position of the mechanical circulatory assist device through user interaction.

[0715] The parameter configuration data for the mechanical circulatory assist device may include one or more of flow rate setting data, speed setting data, pipe size information, and / or connection location information. It will be appreciated that for parameters not explicitly set in the parameter configuration data, default values for the corresponding parameters of the mechanical circulatory assist device may be used during the prediction process.

[0716] For example, for a performance test of the same mechanical circulatory assist device, the flow rate setting data, speed setting data, and / or tubing size information corresponding to multiple sample subjects may be completely consistent, completely inconsistent, or partially consistent. The conditions of different sample subjects may vary, and the user may configure the aforementioned parameter configuration data based on actual conditions.

[0717] Exemplarily, in response to a test instruction, 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 jointly operate based on the physiological parameter data of the sample object and the parameter configuration data of the mechanical circulatory assist device, and the prediction result can be output. The mechanical circulatory assist model can be constructed based on the fluid mechanics data of the mechanical circulatory assist device.

[0718] As another example, in response to a test instruction, the physiological parameter data of the sample subject and the parameter configuration data of the mechanical circulatory assist device can be input into the artificial intelligence model to output a virtual clinical trial result through the artificial intelligence model.

[0719] The virtual clinical trial results include predicted results of using the mechanical circulatory assist device to assist multiple sample subjects in blood circulation. The predicted results can be used to predict changes in the physiological state of the cardiovascular system in the sample subjects when the mechanical circulatory assist device operates with the parameter configuration data. The changes in the physiological state may, for example, include but are not limited to changing trends in one or more cardiovascular parameters such as hemodynamic parameters and / or blood damage parameters.

[0720] Exemplarily, the prediction result may include cardiovascular parameter prediction data, and the cardiovascular parameter prediction data may include hemodynamic parameter data and / or blood injury data corresponding to each of a plurality of sample subjects.

[0721] For example, the virtual clinical trial results can be output in a visual or structured manner such as a chart, curve or numerical list for reference by users (such as clinicians) to provide data support for the scope of subjects for which the mechanical circulatory assist device is applicable.

[0722] Exemplarily, the method may further include: outputting risk warning information when the prediction result indicates that the performance of the cardiovascular system of the sample subject has deteriorated.

[0723] In the above embodiment, after determining the mechanical circulatory assist device to be tested, the parameter configuration data of the mechanical circulatory assist device is obtained by responding to the parameter configuration operation for the mechanical circulatory assist device, so that the user (such as a clinician) can flexibly configure the relevant parameters of the mechanical circulatory assist device according to clinical needs, thereby improving the flexibility of performance testing. By responding to the test instruction for the mechanical circulatory assist device, the virtual clinical trial results corresponding to the mechanical circulatory assist device are output, and the virtual clinical trial results include the predicted results of using the mechanical circulatory assist device to assist blood circulation in multiple sample subjects, and the predicted results are used to predict the changes in the physiological state of the cardiovascular system in the sample subjects 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 subjects under the set parameter configuration in a virtual clinical trial test environment.

[0724] 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. It not only helps users to understand the performance of the mechanical circulatory assist device in blood circulation in advance, so as to evaluate the safety and effectiveness of the mechanical circulatory assist device and discover the potential risks of the mechanical circulatory assist device in advance, but also helps users to identify the scope of its applicable objects in advance, thereby improving the safety of mechanical circulatory assist therapy in real clinical applications.

[0725] In some embodiments, the method may further include: outputting a performance evaluation result corresponding to the virtual clinical trial result, wherein the performance evaluation result is used to characterize the performance of the mechanical circulatory assist device in providing blood circulation assistance.

[0726] In the above embodiment, by outputting the performance evaluation results corresponding to the virtual clinical trial results, the performance evaluation results can be presented in the form of performance scores, performance levels or other quantitative forms, thereby intuitively reflecting the performance of the mechanical circulatory assist device in assisting blood circulation, so as to facilitate the evaluation of the safety and effectiveness of the mechanical circulatory assist device.

[0727] In some embodiments, the method may further include: determining the performance evaluation result according to the virtual clinical trial result.

[0728] Exemplarily, the controller may evaluate the performance of the mechanical circulatory assist device based on 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 a performance score average and / or a performance grade. The performance score average is the average of the performance scores of the mechanical circulatory assist device corresponding to a plurality of sample objects. The performance grade may be determined based on a comparison of the performance score average with the score range corresponding to the performance grade. For example, the prediction results of each sample object may be mapped to the performance scores of cardiovascular parameters in multiple dimensions (such as hemodynamic parameters, blood damage), and the performance scores of the mechanical circulatory assist device corresponding to each sample object may be obtained, and the performance scores of the mechanical circulatory assist device corresponding to each sample object may be averaged to obtain the performance score average.

[0729] In some embodiments, the method may further include: sending the virtual clinical trial results to at least one expert account; and receiving performance evaluation indicator data returned by the at least one expert account, wherein the performance evaluation results include the performance evaluation indicator data. In this manner, by having experts conduct performance evaluations of mechanical circulatory assist devices based on the virtual clinical trial results, leveraging the experts' expertise and experience, the accuracy of the performance evaluation results of the mechanical circulatory assist devices can be improved, thereby providing more reliable data support for clinical decision-making.

[0730] In some embodiments, the parameter configuration data includes connection position information corresponding to each of the sample objects, and the connection position information includes the drainage position and reflux 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 also includes: parameter setting data corresponding to each of the sample objects, and the parameter setting data is used to control the operation of the power component; and pipeline size information corresponding to each of the sample objects, and the pipeline size information is used to characterize the geometric dimensions of the pipeline component.

[0731] Illustratively, the drainage site is the site where the mechanical circulatory assist device extracts blood from the cardiovascular system, and the return site is the site where the mechanical circulatory assist device reinjects the extracted blood (eg, pressurized blood) into the cardiovascular system.

[0732] For example, for an in vivo mechanical circulatory assist device, the drainage location can be a ventricle (such as the right ventricle or left ventricle), and the return location can be an artery (such as the pulmonary artery or aorta). For example, the drainage location of a left ventricular assist device (LVAD) is the left ventricle, and the return location is the aorta; the drainage location of a right ventricular assist device (RVAD) is the right ventricle, and the return location is the pulmonary artery.

[0733] Illustratively, the power assembly can drive blood from the drainage position into the tubing assembly and out from the return position. The power assembly can include a blood pump or an impeller in a blood pump, and the mechanical circulatory assist device can further include a drive assembly, and the power assembly is driven by the drive assembly to pump blood. The drive assembly can include, for example, a motor. The tubing assembly can be used to connect the mechanical circulatory assist device and the cardiovascular system in the sample subject. The tubing assembly provides a flow path for blood in the mechanical circulatory assist device, and blood can flow into the tubing assembly from one end and flow back to the cardiovascular system from the other end of the tubing assembly under the drive of the power assembly.

[0734] For example, the parameter setting data may include flow setting data and / or speed setting data corresponding to the power assembly. The flow setting data may indicate a target flow rate output by the mechanical circulatory assist device (in liters per minute). The speed setting data may be a pump speed (in revolutions per minute), such as a pumping setting of 1800 revolutions per minute or 2000 revolutions per minute.

[0735] The pipeline dimension information corresponding to the pipeline assembly may be used to characterize the geometric dimensions of the pipeline assembly.

[0736] It is understandable that the parameter setting data and the pipeline dimension information corresponding to the pipeline assembly may be constrained by fluid mechanics formulas.

[0737] In the above embodiment, by including the connection position information corresponding to each of the sample objects (the drainage position and reflux position of the mechanical circulatory assist device), the parameter setting data of the power component, and the pipeline size information of the pipeline component in the parameter configuration data, the pipeline size information can be personalized according to the pipeline configuration actually used in clinical practice. This not only takes into account the connection position information corresponding to multiple sample objects and the working characteristics of the power component, but also fully introduces the influence of the pipeline size on the fluid transmission performance, thereby more realistically reflecting the actual operating effect of the mechanical circulatory assist device in the process of simulating assisted blood circulation, thereby further improving the reliability of the virtual clinical trial results corresponding to the mechanical circulatory assist device, thereby improving the reliability of the performance test.

[0738] In order to simulate the dynamic parameter adjustment process of a mechanical circulatory assist device in actual clinical applications, in some embodiments, the parameter setting data may include: flow setting data corresponding to at least one time period, and / or, speed setting data corresponding to at least one time period; the prediction result may include the cardiovascular parameter prediction data of the sample subject corresponding to the at least one time period.

[0739] Exemplarily, at least one time period may include one or more time units for simulating the operation of a mechanical circulatory assist device, such as each of five consecutive days. For the at least one time period, the parameter setting data may be personalized for each time period according to clinical needs, for example, it may include flow setting data and speed setting data corresponding to each day. Specifically, different time periods may be based on changes in the patient's physiological state or adjustments to treatment strategies, and different operating setting parameters may be set accordingly. For example, in order to stabilize the blood flow state as quickly as possible, the flow setting data or the speed setting data may 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 or fifth day), the flow setting data or the speed setting data may be appropriately lowered. Accordingly, the prediction results are also output in time period units, for example, including cardiovascular parameter prediction data for each day.

[0740] Exemplarily, the cardiovascular parameter prediction data may include hemodynamic parameter data and / or blood damage 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, and ventricular elastance data; the blood damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

[0741] Native cardiac index data can be derived by dividing the sample subject's cardiac blood flow data by their body surface area. Total cardiac index data can be derived by dividing the total blood flow data (the sum of the cardiac blood flow data and the blood flow data from the target mechanical circulatory assist device) by their body surface area. Ventricular pressure-volume loop data can be used to describe the relationship between ventricular pressure and ventricular volume in the heart (usually the left ventricle). Ventricular elastance data can characterize the elastic properties of the ventricular wall and describe its ability to resist changes in volume.

[0742] Hemolysis index data includes mechanical hemolysis index (MIH) data. MIH data can be generated based on model predictions and can be used to assess the blood compatibility of mechanical circulatory assist devices. Coagulation index data can be generated based on model predictions and can be used to assess the dynamic equilibrium between bleeding and thrombosis risk in sample subjects in real time. Thrombosis index data can be used to quantify the risk level of thrombosis on the surface of specific medical devices (such as VAD pump heads and ECMO tubing).

[0743] In the above embodiment, by setting flow setting data and / or speed setting data corresponding to at least one time period, and outputting cardiovascular parameter prediction data corresponding to at least one time period for each sample object, the performance of the mechanical circulatory assist device in assisting multiple sample objects in blood circulation at different time periods can be predicted, which helps to evaluate the adaptability of the mechanical circulatory assist device in different clinical scenarios in advance, thereby better providing data support for clinical decision-making.

[0744] In addition, when the prediction results include cardiovascular parameter prediction data corresponding to at least one time period for each sample subject, a relationship diagram can be output between the parameter configuration data and the cardiovascular parameter prediction data for each sample subject at different time periods. This can intuitively display the dynamic relationship between the parameter configuration data (such as flow rate and pump speed) and the cardiovascular parameter prediction data (such as native cardiac index data and hemolytic index) for each sample subject at different time periods, allowing the performance of mechanical circulatory assist devices to be tested in more dimensions, further improving the reliability of performance testing.

[0745] In some embodiments, the parameter setting data may include: parameter setting data corresponding to multiple set working conditions of the mechanical circulatory assist device; the prediction results may include cardiovascular parameter prediction data corresponding to the sample object under multiple set working conditions, and the cardiovascular parameter prediction data includes hemodynamic parameter data and / or blood damage 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 damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

[0746] For example, the parameter setting data corresponding to each set working condition may include flow setting data, speed setting data, and pipeline size information. When the pipeline size information is the same, the flow setting data and speed setting data corresponding to different set working conditions may be completely different.

[0747] In the above embodiment, different set operating conditions may have different effects on the cardiovascular parameters of the sample subject. By outputting the predicted cardiovascular parameter data corresponding to the sample subject under multiple set operating conditions of the mechanical circulatory assist device, the performance of the mechanical circulatory assist device under different operating conditions can be effectively evaluated, further improving the reliability of the performance test.

[0748] In some embodiments, the method may further include: acquiring physiological parameter data corresponding to the multiple sample objects.

[0749] The physiological parameter data of the sample subject is associated with the cardiovascular system of the sample subject. For example, the physiological parameter data of the sample subject can be obtained by monitoring the physiological state of the cardiovascular system of the sample subject. The physiological parameter data of the sample subject can be data pre-stored in the system.

[0750] In some embodiments, the physiological parameter data includes at least one of the following: mean arterial pressure data, cardiac output data, blood flow data, and atrial pressure data.

[0751] Mean arterial pressure data is a numerical representation of mean arterial pressure (MAP), which can characterize the pressure level of the overall circulatory system of the sample subject. Cardiac output data is a numerical representation of cardiac output (CO), which can reflect the heart's ability to deliver blood to the whole body. Blood flow data can be defined as a numerical representation of the volume of blood flowing through a specific blood vessel or organ per unit time, which can be used to describe the circulatory perfusion of a specific part. Atrial pressure data, for example, is a numerical representation of the left atrial pressure (LAP) or right atrial pressure (RAP) of the sample subject, which can be used to reflect the heart's preload and postload status and ventricular filling.

[0752] In the above embodiment, the physiological parameter data of the sample object includes at least one of mean arterial pressure data, cardiac output data, blood flow data, and atrial pressure data. In this way, in the process of predicting the effect of the machine, the physiological parameter data of the target object can be used to fit a cardiovascular model that meets the specific conditions of the target object, thereby better supporting the prediction of the effect of the mechanical circulatory assist device on the individual's specific conditions.

[0753] In the above steps, in response to the test instruction for the mechanical circulatory assist device, outputting the virtual clinical trial result corresponding to the mechanical circulatory assist device may include:

[0754] In response to the test instruction, start traversing the multiple sample objects; for the currently traversed sample object, obtain a mechanical circulatory assistance model constructed based on the fluid mechanics data and a cardiovascular model corresponding to the currently traversed sample object; based on the physiological parameter data and the parameter configuration data corresponding to the currently traversed sample object, drive the mechanical circulatory assistance model and the cardiovascular model to run jointly to obtain a prediction result corresponding to the currently traversed sample object; after completing the traversal of the multiple sample objects, output the virtual clinical trial result.

[0755] For example, the cardiovascular model may include an equivalent model of the target subject's cardiovascular system. The cardiovascular model's structure matches the sample subject's cardiovascular structure. The cardiovascular model may be equivalent to the sample subject's complete cardiovascular system, or the cardiovascular model may be equivalent to a portion of the sample subject's cardiovascular system.

[0756] In one possible implementation, the cardiovascular model may include multiple model units. Each model unit may be used to simulate one or more organs / tissues in the cardiovascular system of the subject. For example, the cardiovascular model may include a model unit for simulating the pulmonary circulation, a model unit for simulating the systemic circulation, a model unit for simulating the left coronary circulation, a model unit for simulating the right coronary circulation, a model unit for simulating the left heart, and a model unit for simulating the right heart.

[0757] In one possible implementation, the cardiovascular model may adopt a lumped parameter model to simulate the interaction between organs, vascular systems, and blood flow.

[0758] For example, a mechanical circulatory assist device may include an active portion and a passive portion. The active portion may include power-generating components such as a blood pump. The passive portion may include components such as cannulae and tubing through which blood flows. In one possible implementation, a mechanical circulatory assist model may be used to simulate at least one component of the mechanical circulatory assist device.

[0759] Mechanical loop-assisted models can be implemented using reduced-order models (ROMs). ROMs are derived from high-dimensional models of complex systems (such as partial differential equations and high-degree-of-freedom systems) through mathematical or physical simplification. ROMs can preserve the key dynamic characteristics of high-dimensional models while significantly reducing computational costs. ROMs enable rapid simulation, real-time control, and / or parameter optimization while maintaining sufficient accuracy.

[0760] In some examples, a mechanical circulatory assist device can be analyzed based on computational fluid dynamics (CFD) to determine a high-dimensional computational fluid dynamics model of the mechanical circulatory assist device, and then the computational fluid dynamics model can be simplified and reduced to obtain a reduced-order model for simulating the mechanical circulatory assist device based on computational fluid dynamics.

[0761] A mechanical circulatory assist device may include components such as a blood pump, cannula, and / or catheter. For example, all components of the mechanical circulatory assist device can be simulated using a single reduced-order model, or multiple components can be simulated using multiple reduced-order models.

[0762] In one possible implementation, the mechanical circulatory assist model simulating the mechanical circulatory assist device may include the mechanical circulatory assist model predicting an operating result of the mechanical circulatory assist device. For example, the reduced-order model may predict a hemolytic index and / or output flow data of the mechanical circulatory assist device.

[0763] Reduced-order models can shorten the simulation time of computational fluid dynamics models from 12 hours to 15 minutes, making them more suitable for real-time clinical predictions. ROM can capture the flow field details of mechanical circulatory assist devices, and the automatically generated 0D / 1D model can keep the calculation error within 1% to 10%, significantly accelerating the simulation process. At the same time, the reduced-order model also provides analytical data on shear stress and hemolysis risk. Compared with computational fluid dynamics models, the core of the reduced-order model lies in the intelligent compression of high-dimensional systems through mathematical methods while retaining the key physical properties related to mechanical circulatory assist devices. Compared with computational fluid dynamics models, the reduced-order model can compress the amount of calculation by 4 to 6 orders of magnitude. By only calculating the 5% to 10% of modes that have the greatest impact on system behavior, the computational efficiency of the reduced-order model can be significantly improved, thereby significantly shortening the calculation time.

[0764] The physiological parameter data of the sample subject may be used to fit the model parameters of the cardiovascular model, so that the cardiovascular model can simulate the hemodynamics of the sample subject, such as the blood circulation of the sample subject.

[0765] In the above steps, based on the physiological parameter data and the parameter configuration data, driving the mechanical circulatory assistance model and the cardiovascular model to jointly operate and outputting the prediction results may include: after fitting the model parameters of the cardiovascular model based on the physiological parameter data and obtaining the mechanical circulatory assistance model loaded with the parameter setting data, driving the cardiovascular model and the mechanical circulatory assistance model to jointly operate and outputting the prediction results.

[0766] In this embodiment, the model parameter data is obtained by fitting the cardiovascular model's model parameters to the sample subject's physiological parameter data, and the fitted model parameter data matches the sample subject. The parameter configuration data can be used by the mechanical circulatory assist model to simulate the operating state of the mechanical circulatory assist device. Thus, by driving the mechanical circulatory assist model and the cardiovascular model to operate in conjunction based on the physiological parameter data and parameter configuration data, it is possible to accurately simulate the mechanical circulatory assist device providing blood circulation assistance to the sample subject, thereby improving the accuracy of predicting the effectiveness of the mechanical circulatory assist device.

[0767] In some embodiments, the parameter configuration data may include connection position information and parameter setting data of the mechanical circulatory assist device, the connection position information is used to indicate the drainage position and reflux position of the mechanical circulatory assist device in the cardiovascular system, and the mechanical circulatory assist model and the cardiovascular model are coupled between the drainage position and the reflux position based on a pressure-flow relationship.

[0768] The method of driving the mechanical circulatory assistance model and the cardiovascular model to jointly operate based on the physiological parameter data and the parameter configuration data corresponding to the currently traversed sample object to obtain the prediction result corresponding to the currently traversed sample object may include: fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the currently traversed sample object; driving the cardiovascular model to operate based on the model parameter data, and outputting the pressure gradient data between the drainage position and the reflux position; inputting the pressure gradient data and the parameter setting data into the mechanical circulatory assistance model, and outputting the flow data and blood damage data of the mechanical circulatory assistance model; inputting the flow data into the cardiovascular model to update the operating status of the cardiovascular model and outputting the hemodynamic parameter data.

[0769] Wherein, the prediction result includes the blood damage data and the hemodynamic parameter data; 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, and ventricular elasticity data; the blood damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

[0770] In some embodiments, the mechanical circulatory assistance model includes a blood pump model and a pipeline model, wherein the blood pump model corresponds to a power component in the mechanical circulatory assistance device, and the pipeline model corresponds to a pipeline component in the mechanical circulatory assistance device.

[0771] In the above steps, inputting the pressure gradient data and the parameter setting data into the mechanical circulatory assistance model and outputting the flow data and blood damage data of the mechanical circulatory assistance model may include:

[0772] The flow data between the drainage position and the reflux position is input into the pipeline model, and first pressure loss data and second blood damage data are output, wherein the first pressure loss data is used to characterize the pressure loss at both ends of the pipeline assembly, and the second blood damage data is used to characterize the blood damage caused by the pipeline assembly; based on the pressure gradient data and the first pressure loss data, the pump head pressure difference data corresponding to the power assembly is determined; the pump head pressure difference data and the parameter setting data are input into the blood pump model, and the flow data and the first blood damage data corresponding to the power assembly are output, wherein the first blood damage data is used to characterize the blood damage caused by the power assembly; the first blood damage data and the second blood damage data are fused to obtain total blood damage data.

[0773] Exemplarily, the model parameters of the cardiovascular model are fitted according to the physiological parameter data, so that when the difference between the physiological parameter data output by the cardiovascular model and the physiological parameter data of the cardiovascular system is less than a predetermined difference threshold, the model parameter data obtained by fitting can be used as model parameter data matching the target object.

[0774] In one possible implementation, the model parameters may be model parameters that are highly correlated with physiological parameter data.

[0775] For example, model parameters may include left ventricular elastance, systemic peripheral resistance, blood volume, right ventricular end-diastolic stiffness, and right ventricular elastance.

[0776] The cardiovascular model can be driven by model parameter data, and the model parameter data matches the target object. Therefore, the cardiovascular model driven by model parameter data can simulate the real hemodynamics of the target object, and then obtain the pressure gradient data between the drainage position and the return position.

[0777] Here, after the cardiovascular model is coupled to the mechanical circulatory assistance model, the interaction between the cardiovascular model and the mechanical circulatory assistance model may include coupling of pressure gradient data.

[0778] Pressure gradient data can be the blood pressure difference between the drainage and return points. This blood pressure difference between the drainage and return points of the target patient affects the flow field within the mechanical circulatory assist device. The mechanical circulatory assist model can simulate the operation of the mechanical circulatory assist device based on parameter setting data (such as blood pump speed) and pressure gradient data output by the cardiovascular model.

[0779] Mechanical circulatory assist devices (MACDs) provide two key indicators for blood circulation support: flow rate data and blood damage data. Flow rate data indicates the simulated blood output of the MACD. Therefore, the MACD model can simulate the MACD's blood circulation assistance process and output blood flow rate data to the cardiovascular model, thereby simulating the MACD's blood circulation assistance for the target subject. This in turn predicts and outputs hemodynamic parameter data and blood damage data.

[0780] Flow data represents the blood output of the mechanical circulatory assist device, i.e., the amount of blood returning to the return point. Therefore, this flow data can be input into the cardiovascular model, which can then simulate the blood flow input by the mechanical circulatory assist device at the return point based on the flow data, thus achieving coupling between the cardiovascular model and the mechanical circulatory assist model.

[0781] The cardiovascular model can simulate the hemodynamics of the cardiovascular system based on the updated flow data, thereby achieving the update of hemodynamic parameter data.

[0782] In this embodiment, the cardiovascular model and the mechanical circulatory assistance model are connected based on the actual connection location of the mechanical circulatory assist device in the target subject's cardiovascular system, thereby accurately simulating the condition of the target subject's blood circulation being assisted by the mechanical circulatory assist device. The mechanical circulatory assistance model accurately simulates the actual operation of the mechanical circulatory assist device based on pressure gradient data between the drainage location and the return location and operational setting parameter data, thereby improving the accuracy of the predicted hemodynamic parameter data and blood damage data of the target subject under the mechanical circulatory assist device. The mechanical circulatory assistance model simulates the mechanical circulatory assist device and is capable of predicting the flow rate data and blood damage data of the mechanical circulatory assist device. Compared to determining the hemolytic index and assisted output blood flow through invasive testing methods, it can effectively reduce harm to the target subject.

[0783] In some embodiments, fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the currently traversed sample object may include:

[0784] In a first time period, a first number of model parameters in the cardiovascular model are fitted according to the physiological parameter data corresponding to the first time period, and first fitting data corresponding to the first number of model parameters in the first time period are obtained; wherein the...

Claims

1. A performance testing method for a mechanical circulatory assist device, characterized in that: The method comprises: Identify the mechanical circulatory assist devices to be tested; In response to a parameter configuration operation for the mechanical circulatory assist device, acquiring parameter configuration data of the mechanical circulatory assist device; In response to a test instruction for the mechanical circulatory assist device, a virtual clinical trial result corresponding to the mechanical circulatory assist device is output, wherein the virtual clinical trial result includes a prediction result of using the mechanical circulatory assist device to assist multiple sample subjects in blood circulation, and the prediction result is used to predict changes in the physiological state of the cardiovascular system in the sample subjects when the mechanical circulatory assist device operates with the parameter configuration data.

2. The method according to claim 1, characterized in that The method further comprises: A performance evaluation result corresponding to the virtual clinical trial result is output, where the performance evaluation result is used to characterize the performance of the mechanical circulatory assist device in assisting blood circulation.

3. The method according to claim 2, characterized in that The method further comprises: Sending the virtual clinical trial results to at least one expert account; The performance evaluation index data returned by the at least one expert account is received, where the performance evaluation result includes the performance evaluation index data.

4. The method according to claim 1, wherein The parameter configuration data includes connection position information corresponding to each of the sample objects, and the connection position information includes the drainage position and the reflux position of the mechanical circulatory assist device; The mechanical circulation assist device includes a power assembly and a piping assembly, and the parameter configuration data also includes: Parameter setting data corresponding to each sample object, wherein the parameter setting data is used to control the operation of the power component; The pipeline dimension information corresponding to each of the sample objects is used to characterize the geometric dimensions of the pipeline assembly.

5. The method according to claim 4, characterized in that The parameter setting data includes: flow rate setting data corresponding to at least one time period, and / or speed setting data corresponding to at least one time period; The prediction result includes cardiovascular parameter prediction data corresponding to the sample subject in the at least one time period, and the cardiovascular parameter prediction data includes hemodynamic parameter data and / or blood damage data; 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 damage data includes at least one of the following: Hemolysis index data, coagulation index data, and thrombosis index data.

6. The method according to claim 4, characterized in that The parameter setting data includes: parameter setting data corresponding to a plurality of set working conditions of the mechanical circulation assist device; The prediction result includes cardiovascular parameter prediction data corresponding to the sample subject under the multiple set working conditions, and the cardiovascular parameter prediction data includes hemodynamic parameter data and / or blood damage data; 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 damage data includes at least one of the following: Hemolysis index data, coagulation index data, and thrombosis index data.

7. The method according to claim 2, characterized in that The method further comprises: The performance evaluation result is determined according to the virtual clinical trial result.

8. The method according to any one of claims 1 to 7, characterized in that The step of determining a mechanical circulatory assist device to be tested comprises: The mechanical circulatory assist device to be tested is determined in response to input of fluid dynamics data, the fluid dynamics data being used to characterize the fluid dynamics performance of the mechanical circulatory assist device.

9. The method according to claim 8, characterized in that The method further comprises: Acquiring physiological parameter data corresponding to the multiple sample objects; The step of outputting a virtual clinical trial result corresponding to the mechanical circulatory assist device in response to a test instruction for the mechanical circulatory assist device includes: In response to the test instruction, start traversing the plurality of sample objects; For the currently traversed sample object, obtaining a mechanical circulatory assistance model constructed based on the fluid mechanics data and a cardiovascular model corresponding to the currently traversed sample object; Based on the physiological parameter data and the parameter configuration data corresponding to the currently traversed sample object, driving the mechanical circulatory assist model and the cardiovascular model to jointly operate, to obtain a prediction result corresponding to the currently traversed sample object; After traversing the plurality of sample objects, the virtual clinical trial result is output.

10. The method according to claim 9, characterized in that The physiological parameter data includes at least one of the following: Mean arterial pressure data, cardiac output data, blood flow data, atrial pressure data.

11. The method according to claim 9, characterized in that The parameter configuration data includes connection position information and parameter setting data of the mechanical circulatory assist device, the connection position information is used to indicate a drainage position and a return position of the mechanical circulatory assist device in the cardiovascular system, and the mechanical circulatory assist model and the cardiovascular model are coupled to each other between the drainage position and the return position based on a pressure-flow relationship; The step of driving the mechanical circulatory assist model and the cardiovascular model to jointly operate based on the physiological parameter data and the parameter configuration data corresponding to the currently traversed sample object to obtain a prediction result corresponding to the currently traversed sample object includes: Fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data that matches the currently traversed sample object; driving the cardiovascular model to run based on the model parameter data, and outputting pressure gradient data between the drainage position and the backflow position; inputting the pressure gradient data and the parameter setting data into the mechanical circulatory assistance model, and outputting the flow data and blood damage data of the mechanical circulatory assistance model; Inputting the flow data into the cardiovascular model to update the operating state of the cardiovascular model and output hemodynamic parameter data; Wherein, the prediction result includes the blood damage data and the hemodynamic parameter data; 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, and ventricular elastance data; The blood damage data includes at least one of the following: hemolysis index data, coagulation index data, and thrombosis index data.

12. The method according to claim 11, characterized in that The mechanical circulatory assistance model includes a blood pump model and a pipeline model, wherein the blood pump model corresponds to the power component in the mechanical circulatory assistance device, and the pipeline model corresponds to the pipeline component in the mechanical circulatory assistance device; Inputting the pressure gradient data and the parameter setting data into the mechanical circulatory assistance model and outputting the flow data and blood damage data of the mechanical circulatory assistance model comprises: Inputting flow rate data between the drainage position and the reflux position into the pipeline model, and outputting first pressure loss data and second blood damage data, wherein the first pressure loss data is used to characterize the pressure loss at both ends of the pipeline assembly, and the second blood damage data is used to characterize the blood damage caused by the pipeline assembly; Determining pump head pressure differential data corresponding to the power assembly based on the pressure gradient data and the first pressure loss data; Inputting the pump head pressure difference data and the parameter setting data into the blood pump model, and outputting the flow data and first blood damage data corresponding to the power component, wherein the first blood damage data is used to characterize the blood damage caused by the power component; The first blood damage data and the second blood damage data are fused to obtain total blood damage data.

13. The method according to claim 11, characterized in that The step of fitting the model parameters of the cardiovascular model according to the physiological parameter data to obtain model parameter data matching the currently traversed sample object includes: During a first time period, fitting a first number of model parameters in the cardiovascular model according to the physiological parameter data corresponding to the first time period to obtain first fitting data corresponding to the first number of model parameters during the first time period; wherein the first fitting data is used to drive the cardiovascular model; In a second time period after the first time period, a second number of model parameters in the cardiovascular model are fitted according to the physiological parameter data corresponding to the second time period to obtain second fitting data corresponding to the second number of model parameters in the second time period, wherein the second fitting data is used to drive the cardiovascular model; and the first number and the second number are different.

14. The method according to claim 13, characterized in that The first number of model parameters includes: left ventricular elastance, systemic peripheral resistance, blood volume, right ventricular end-diastolic stiffness and right ventricular elastance; The second number of model parameters includes: the left ventricular elastance and the systemic peripheral resistance.

15. The method according to any one of claims 12 to 14, characterized in that 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. The blood pump model is a reduced-order model obtained by reducing the order of the computational fluid dynamics model corresponding to the power component. The pipeline model is a reduced-order model obtained by reducing the order of the computational fluid dynamics model corresponding to the pipeline component.

16. A performance testing device for a mechanical circulatory assist device, characterized in that: The apparatus comprises a processing module configured to: Identify the mechanical circulatory assist devices to be tested; In response to a parameter configuration operation for the mechanical circulatory assist device, acquiring parameter configuration data of the mechanical circulatory assist device; In response to a test instruction for the mechanical circulatory assist device, a virtual clinical trial result corresponding to the mechanical circulatory assist device is output, wherein the virtual clinical trial result includes a prediction result of using the mechanical circulatory assist device to assist multiple sample subjects in blood circulation, and the prediction result is used to predict changes in the physiological state of the cardiovascular system in the sample subjects when the mechanical circulatory assist device operates with the parameter configuration data.

17. A mechanical circulatory assist device, characterized in that: The mechanical circulatory assist device is connected and coupled to the cardiovascular system in the sample subject when in use, and a drainage position and a return position are defined between the mechanical circulatory assist device and the cardiovascular system. The mechanical circulatory assist device comprises: a control module, a drive assembly, a power assembly, and a piping assembly, wherein one end of the piping assembly is located at the drainage position, and the other end of the piping assembly is located at the return position; The control module is used to control the driving component to drive the power component to pump blood, wherein the power component drives the blood to flow from the drainage position into the pipeline component and out of the return position; The control module comprises a controller configured to execute the steps of the performance testing method of the mechanical circulatory assistance device according to any one of claims 1 to 15 .

18. An electronic device comprising a processor, a memory, and an executable program stored in the memory and capable of being run by the processor, characterized in that: When the processor runs the executable program, the processor performs the steps of the performance testing method of the mechanical circulatory assist device according to any one of claims 1 to 15.

19. A storage medium having an executable program stored thereon, characterized in that: When the executable program is executed by a processor, the steps of the performance testing method of a mechanical circulatory assist device according to any one of claims 1 to 15 are implemented.

Images