Monitoring of myocardial contractility during cardiac treatment

Abstract

A computer-aided method may be provided. The method may include receiving the pulmonary capillary wedge pressure and cardiac output of a human heart by a computing system. The method may also include determining the myocardial contractility of a human heart by the computing system, based on the left ventricular end-systolic elastance generated as the inverse function of the gradient of the relationship between pulmonary capillary wedge pressure and cardiac output, while avoiding singularities in the inverse function. The method may further include generating the optimal drug combination to reach a target myocardial contractility from the determined myocardial contractility by the computing system.

Description

[Technical Field]

[0001] Cross-reference of related applications This application was filed on 31 March 2023 and claims priority to U.S. Provisional Application No. 63 / 493,619, entitled “Monitoring Cardiac Contractility During Cardiac Treatments,” which is incorporated in its entirety by reference.

[0002] This disclosure is incorporated in its entirety by reference in U.S. Provisional Application No. 63 / 346,143, filed on 26 May 2022, entitled "Optimizing Drug Combinations for Treating Acute Heart Failure."

[0003] This disclosure relates to a system, method, and device for continuously or nearly continuously monitoring cardiac contractility during cardiac treatment, such as drug therapy. [Background technology]

[0004] The human heart is a pumping organ that actively circulates blood throughout the body. This pumping action is caused by the contraction of the myocardium, which reduces the size of parts of the heart, such as the atrial appendages or ventricles, and applies mechanical force to the blood contained within. These contractions are repeated periodically to create the human heartbeat. Myocardial contractility represents the ability of the myocardium to contract for this pumping action and is an important cardiovascular parameter that must be monitored for patients undergoing cardiac treatment.

[0005] One way to determine myocardial contractility is by measuring left ventricular end-systolic elastance (E). es ) is based on measurement or estimation. esElastance, which includes [the relevant part], is generally measured by the ratio of the pressure to the volume in the corresponding cardiac chamber. Both the pressure and the volume in the cardiac chamber are determined by the contraction state of the cardiac chamber. For example, during the systolic phase when the heart contracts, the pressure rises and the volume decreases as blood is pushed out from the corresponding cardiac chamber. Thus, end-systolic elastance is the ratio of the pressure to the volume immediately after the end of the systolic phase. In a pressure-volume (PV) diagram, end-systolic elastance is the slope of the end-systolic pressure-volume relationship (ESPVR) curve. Therefore, E es is the slope of the ESPVR curve of the left ventricle. E es Traditional techniques for measuring [it] rely on this definition. That is, to estimate / measure E es , in order to generate the ESPVR curve, the left ventricular pressure (P LV ) and the left ventricular volume (V LV ) at multiple points must be determined.

[0006] E es These traditional methods for measuring or estimating E LV have significant technical challenges. For example, traditional V LV estimation methods are inaccurate, and it is experimentally known that the relationship between the true V LV and the estimated V LV varies with changes in the state. Furthermore, P LV is not measured for the purpose of monitoring hemodynamics in clinical practice. This is because continuously measuring P LV by inserting a catheter is more significant in terms of complications such as the risk of arrhythmia than its benefits. When accurate V LV and P es measurements cannot be made, machine learning techniques are used for the estimation of E

[0007] Due to these technical difficulties, cardiac treatment, especially in the intensive care unit (ICU) / critical care unit (CCU), regarding E esThis is being done without sufficient knowledge. For example, clinicians may not know if a particular drug or combination of drugs is effective. es There is no way to know how it will affect E. Therefore, clinicians rely on experience and speculation. es The effects of drugs on the patient must be estimated mentally. Furthermore, this lack of knowledge prevents the implementation of a closed-loop hemodynamic control system that can automatically inject the optimal drug combination. In situations involving vital organs like the human heart, this lack of knowledge can be fatal for the patient, even with clinical expertise and experience.

[0008] Thus, E es Significant improvements in systems, methods, and devices for measuring (and generally myocardial contractility) are therefore desired. [Overview of the project]

[0009] In some embodiments, a computer-aided method may be provided. The method may include receiving the pulmonary capillary wedge pressure and cardiac output of a human heart by a computing system. The method may also include determining the myocardial contractility of the human heart by the computing system, based on the left ventricular end-systolic elastance, which is generated as the inverse function of the gradient of the relationship between pulmonary capillary wedge pressure and cardiac output, while avoiding singularities in the inverse function. The method may further include generating an optimal drug combination to achieve a target myocardial contractility from the determined myocardial contractility by the computing system.

[0010] In some embodiments, a system may be provided. The system may include a non-temporary storage medium for storing computer program instructions, and a processor configured to execute computer program instructions to trigger an action. The action may include receiving the pulmonary capillary wedge pressure and cardiac output of a human heart. The action may further include determining the myocardial contractility of a human heart based on the left ventricular end-systolic elastance, which is generated as the inverse function of the gradient of the relationship between pulmonary capillary wedge pressure and cardiac output, while avoiding singularities in the inverse function. The action may also include generating an optimal combination of drugs to reach a target myocardial contractility from the determined myocardial contractility.

[0011] In some embodiments, a non-temporary storage medium for storing computer program instructions may be provided. Computer program instructions that, when executed, cause a computing system to perform an action. The action may include receiving the pulmonary capillary wedge pressure and cardiac output of a human heart. The action may further include determining the myocardial contractility of a human heart based on the left ventricular end-systolic elastance, which is generated as the inverse function of the gradient of the relationship between pulmonary capillary wedge pressure and cardiac output, while avoiding singularities in the inverse function. The action may also include generating an optimal combination of drugs to reach a target myocardial contractility from the determined myocardial contractility. [Brief explanation of the drawing]

[0012] [Figure 1] This disclosure illustrates an exemplary computing environment for monitoring myocardial contractility during cardiac treatment, according to exemplary embodiments of this disclosure. [Figure 2] An exemplary analytical model is shown according to an exemplary embodiment of the present disclosure. [Figure 3] A flowchart of an exemplary method based on exemplary embodiments of this disclosure is shown. [Figure 4] An exemplary graph illustrating a comparison between different methods is shown in an exemplary embodiment of the present disclosure. [Figure 5]An exemplary graph illustrating hemodynamic adjustment based on exemplary embodiments of this disclosure is shown. [Figure 6] An exemplary graph illustrating hemodynamic adjustment based on exemplary embodiments of this disclosure is shown. [Figure 7] A block diagram of an exemplary computing device that implements various features and processes according to exemplary embodiments of this disclosure is shown. [Modes for carrying out the invention]

[0013] Embodiments disclosed herein generally relate to systems and methods for estimating myocardial contractility using readily available clinical data. As described above, conventional methods for estimating myocardial contractility generally require continuous measurement of left ventricular pressure and volume, which is difficult to achieve in clinical settings. One or more techniques disclosed herein improve upon conventional methods by utilizing pulmonary capillary wedge pressure and cardiac output, which are indices regularly measured in clinical settings using pulmonary artery catheterization. In some embodiments, such techniques may extrapolate a relationship curve between left ventricular end-diastolic pressure and volume using only a single point of left ventricular end-diastolic pressure and volume, which are non-invasively estimated using echocardiography. This curve is enhanced by physiological constraints and optimized parameters for singularity avoidance, and left ventricular end-systolic elastance (E) es Left ventricular end-systolic elastance can be used in conjunction with pulmonary capillary wedge pressure and cardiac output to estimate left ventricular end-systolic elastance, which is an index of myocardial contractility. Therefore, the clinical feasibility and accuracy of cardiac monitoring and treatment using the disclosed embodiments are significantly improved compared to conventional systems. This improved clinical feasibility is not only useful for clinical decision-making, such as adjusting drug dosages in current clinical cardiac treatment, but also for Ees It can also support closed-loop hemodynamic control systems that require accurate and continuous estimation.

[0014] Figure 1 shows an exemplary computing environment 100 for monitoring myocardial contractility during cardiac treatment, according to an exemplary embodiment of the present disclosure. In some embodiments, the exemplary computing environment 100 uses E as an index of myocardial contractility. es It may be configured to estimate. As illustrated, the computing environment 100 may be based on a client-server model in which a server 102 is connected via a network 104 to a number of clients 106a-106d (commonly referred to as clients 106, or collectively as clients 106) and a cardiac monitoring system 122 (which may also be considered a client to server 102). However, it should be understood that the client-server model is for illustrative purposes only and for ease of explanation and should not be considered limiting. Accordingly, any type of computing environment performing the functions disclosed herein should be considered within the scope of this disclosure. Furthermore, the individual components of computing environment 100 are merely illustrative, and computing environments with alternative, additional, or fewer components should be considered within the scope of this disclosure.

[0015] The computing environment 100 generally measures myocardial contractility, particularly E, for patients with cardiac disease, such as those with acute heart failure. esTo monitor this, it may be present in a clinical setting. In some exemplary use cases, server 102 may store different software modules 108 that can be accessed by clients 106 and cardiac monitoring system 122 using network 104. Client 106 itself may have a standalone application (not shown) for accessing software modules 108. Alternatively, client 106 may access software modules 108 through, for example, a browser application. Similarly, cardiac monitoring system 122 may access software modules 108 through any type of firmware and / or software installed on cardiac monitoring system 122. In some embodiments, cardiac monitoring system 122 may communicate with server 102 using one or more of the clients 106.

[0016] The hardware of the server 102 that stores the software module 108 may include any type of computing device. For example, server 102 may include, but is not limited to, server computers, desktop computers, laptop computers, tablet computers, and smartphones. Server 102 may not necessarily be in a single location and may be implemented by a network of computers. Furthermore, server 102 may not necessarily be jointly installed within the clinical setting itself and may be hosted by a third-party cloud computing provider. Therefore, any type of server 102 should be considered to be within the scope of this disclosure.

[0017] As described above, client 106 and cardiac monitoring system 122 may access server 102 through network 104. Network 104 may include any combination of one or more packet-switched networks (e.g., IP-based networks) and one or more circuit-switched networks (e.g., cellular telephone networks). Some non-limiting examples of network 104 include local area networks, metropolitan area networks, wide area networks such as the Internet, etc. Similarly, non-limiting examples of client 106 may include desktop terminals (e.g., desktop terminal 106a), laptop computers (e.g., laptop computer 106b), tablet computers (e.g., tablet computer 106c), smartphones (e.g., smartphone 106d), etc. Any type of computing device that allows access to server 102 through network 104 should be considered within the scope of this disclosure. Furthermore, the functions described in this disclosure may be distributed in any manner; that is, the functions of server 102 may be performed by one or more clients 106, and vice versa.

[0018] The cardiac monitoring system 122 may include any combination of diagnostic and therapeutic devices used in patients with cardiac disease. For example, the cardiac monitoring system 122 may include a cardiac monitor, a Holter ECG monitor, an echocardiogram device, a cardiac nuclear stress testing device, a cardiac catheter, a cardiac drug infusion device, an external pacemaker, and / or any other type of cardiac diagnostic and therapeutic device. Although a single cardiac monitoring system 122 is shown, any number of monitoring systems should be considered to be within the scope of this disclosure. Furthermore, a single monitoring system 122 does not necessarily have to be localized within a single device and may include a combination of devices (and configuration software / firmware) distributed throughout the clinical setting. In addition, the functions between the monitoring system 122 and the server 102 may be interchangeable, for example, some of the software modules 108 implemented in the server 102 may be implemented by the monitoring system 122. In some embodiments, the cardiac monitoring system 122 monitors myocardial contractility (e.g., E). es A closed-loop hemodynamic control system can be implemented to control the amount of drug injected in response to ).

[0019] As described above, server 102 may include multiple software modules. Figure 1 shows some non-limiting and exemplary software modules: cardiac data input module 110, myocardial contractility calculation module 112, closed-loop hemodynamic control module 114, optimal dosage calculation module 116, model development and simulation module 118, and experimental data acquisition module 120. It should be understood that this modularization of the functions of server 102 described is merely for the sake of clarity and should not be considered limiting. Therefore, any kind of alternative modularization should be considered within the scope of this disclosure.

[0020] The cardiac data input module 110 can receive cardiac data from the client 106 and the cardiac monitoring system 122. The received cardiac data may include any type of cardiac data measured or estimated in the clinical setting. For example, the cardiac data may include left ventricular end-diastolic pressure and volume, which may be generated by echocardiography. The cardiac data may further include pulmonary capillary wedge pressure and cardiac output measured via a pulmonary artery catheter. In addition, the received cardiac data may include the patient's current cardiovascular metrics and target cardiovascular metrics. For example, as current cardiovascular metrics, the cardiac data input module 110 may receive one or more current measurements of left atrial pressure, cardiac output, mean atrial pressure, or myocardial oxygen consumption. Similarly, as target cardiovascular metrics, the cardiac data input module 110 may receive one or more desired measurements of left atrial pressure, cardiac output, mean atrial pressure, or myocardial oxygen consumption. Cardiovascular index data, in addition to other data, may be used to enable a closed-loop hemodynamic control module. In some embodiments, the cardiac data input module 110 may support real-time data input (e.g., for closed-loop hemodynamic control), in which case the server 102 processes the received cardiac data in real time. In other embodiments, the cardiac data input module 110 may support batch processing, in which case individual cardiac data are batched (e.g., buffered or stored), and processing may be performed on the batched data together (e.g., during the server 102's off-peak hours). Thus, the cardiac data input module 110 can manage the reception of cardiac data from the client 106 and the cardiac monitoring system 122 for any type of processing.

[0021] The myocardial contractility calculation module 112 may calculate myocardial contractility using the analysis models disclosed throughout this disclosure. In some embodiments, the myocardial contractility module uses E as the index of myocardial contractility.es It is possible to calculate such E es The calculation can be based on left ventricular end-diastolic pressure and volume measured non-invasively via echocardiography. Additionally, E es The calculation is based on pulmonary capillary wedge pressure and cardiac output. These measurements can be passed to the myocardial contractility calculation module 112 by the cardiac data input module 110.

[0022] The closed-loop hemodynamic control module 114 can perform automated infusion of the optimal drug combination (calculated, for example, by the optimal dosage calculation module 116). That is, the closed-loop hemodynamic control module 114 calculates the E es The patient's myocardial contractility may be monitored regularly, and based on this monitoring, commands may be sent to the cardiac monitoring system 122 to infuse the optimal combination of drugs. This automated infusion may not necessarily require clinician intervention to maintain the patient's myocardial contractility within the desired range.

[0023] The optimal dosage calculation module 116 calculates E by the myocardial contractility calculation module 112. es The optimal dose can be calculated based on the calculated E. For example, the optimal dose calculation module 116 calculates the optimal dose based on the E. es Using this, it becomes the target reference value and / or the received desired E es In comparison, one or more analytical models disclosed herein can be invoked to calculate the optimal dosage of a drug. The optimal dosage calculation module 116 generates the optimal combination by considering different effects (e.g., if the first drug is E es It is possible to consider that the first drug may cause a change in one direction, and the second drug may cause a change in the opposite direction.

[0024] The model development and simulation module 118 may enable a model developer to develop and simulate one or more analysis modules. For example, the model development and simulation module 118 may provide a model developer with an interface, such as a graphical user interface, for defining one or more analysis models. Furthermore, the model development and simulation module 118 may enable a model developer to upload and / or port predefined analysis modules to the server 102. Thus, the model development and simulation module 118 may, in general, provide support for any kind of computing environment for developing the analysis models described throughout this disclosure.

[0025] The model development and simulation module 118 may further enable model developers to simulate analysis models. The simulation may include, for example, numerical simulations, in which case the collected numerical data may be used in the analysis model to observe the output. The simulation may produce, for example, numerical data, graphical data, and / or any other type of output data. The model development and simulation module 118 may generally enable evaluation of the analysis model based on these simulations (for example, to determine whether the analysis model should be run as desired using the simulated scenarios).

[0026] The experimental data acquisition module 120 may acquire experimental data to be used for model development. For example, the experimental data may include measured cardiovascular parameters and / or indicators of animals. Such experimental data may be used by the model development and simulation module 118 to simulate the analysis model. Other experimental data may include detailed experimental data, including both inputs and outputs, which may be used to compare real-world results with simulated results. In yet another example, the experimental data may include a continuous data stream of a patient being treated in a clinical setting, which may be used to continuously modify and / or improve the analysis model. Thus, the experimental data acquisition module 120 may receive any kind of real-world numerical data that may be used to develop, improve and / or modify the analysis modules disclosed throughout this disclosure.

[0027] After one or more analysis models have been developed and validated, clinicians may use the software module 108 in the server 102 in the treatment of patients such as those with acute heart failure. In exemplary operation, a clinician may use a cardiac monitoring system 122 to non-invasively measure a single point of left ventricular end-diastolic volume and pressure using echocardiographic techniques. This measurement may be entered by the clinician into a client 106 to be sent to the server 102, and / or sent directly to the server 102 by the cardiac monitoring system 122. The clinician may further measure pulmonary capillary wedge pressure and cardiac output using a pulmonary artery catheter (which may be functionally supported by the cardiac monitoring system 122). This measurement may also be sent to the server 102 through the client 106 and / or the cardiac monitoring system 122. A cardiac data input module 110 may receive these measurements, perform preprocessing as necessary, and provide them to the myocardial contractility calculation module 112. The myocardial contractility calculation module 112 uses these measurements to calculate myocardial contractility (e.g., E esThe estimated myocardial contractility can be estimated. This estimated myocardial contractility can be used by the optimal dose calculation module 116 to provide the clinician with a recommended dose (e.g., on client 106 and / or cardiac monitoring system 122). Alternatively, the estimated myocardial contractility can be used by the closed-loop hemodynamic control module 114 to provide the cardiac monitoring system 122 with a signal for infusion based on the optimal dose.

[0028] Figure 2 shows an exemplary analysis model 200 according to an exemplary embodiment of the present disclosure. The exemplary analysis model 200 may be used by the software module 108 described in Figure 1. For example, the model development and simulation module 118 may be used to define (and / or port) and simulate the analysis model 200, and the experimental data acquisition module 120 may be used to receive experimental data for verifying, modifying, and / or improving the analysis model 200. Furthermore, the cardiac data input module 110 may receive data used by the analysis model 200, and the myocardial contractility calculation may calculate myocardial contractility (e.g., E) based on the received cardiac data. es The optimal dose calculation module 116 may calculate the optimal dose using the analysis model 200, and the closed-loop hemodynamic control module 114 may provide instructions to the cardiac monitoring system 122 to automatically infuse the calculated dose. It should be further understood that the analysis model 200 is merely an example and should not be considered limiting, and analysis models having additional, alternative, or fewer steps and / or components should be considered within the scope of this disclosure.

[0029] As shown in the figure, the analysis model 200 is the pressure volume of the left ventricle (P LV -V LV ) The first graph 202 shows the relationship between cardiac output (CO) and left auricle pressure (P). LA This is based on the second graph 204, which shows the relationship with ).

[0030] In particular, the first graph 202 shows multiple pressure-volume loops 206a to 206d (commonly called pressure-volume loop 206, and collectively referred to as pressure-volume loop 206). In pressure-volume loop 206, the end-systolic pressure-volume relationship (ESPVR) of the left ventricle can be mathematically expressed as follows. P es =E es (V ed -SV-V0) (1) Here, P es (Measured in mmHg) is the end-systolic pressure, V ed (Measured in ml) is the end-diastolic volume, E es (Measured in mmHg / ml) is end-systolic elastance, which indicates cardiac contractility; SV (Measured in ml / beat) is cardiac output per beat; V0 (Measured in ml) is P es This is a constant parameter that indicates the volumetric intercept of ESPVR when = 0.

[0031] P es This can be approximated using mean arterial pressure (MAP).

number

number

[0032] The left ventricular end-diastolic pressure-volume relationship (EDPVR) can be represented using the following three parameter models.

number

[0033] V in equation (3) ed By substituting this into equation (5), we obtain the following analytical representation of the Frank-Stirling curve.

number

[0034] The gradient s of the Frank-Stirling curve L Let's define (measured in ml / min) as follows:

number

number

[0035] s L E as the inverse function of es By solving this, we get the following:

number

[0036] The inverse ESPVR estimation method described above works in most cases. However, sometimes different results are obtained due to singularities caused by division by zero in equations (8) and (9). The following explanation further analyzes these singularities.

[0037] The first singularity is s L This comes from equation (8), which corresponds to the denominator of . That is, the first singularity condition is encountered when the denominator of equation (8) is equal to 0. This singularity condition is as follows: P LA (t) = α0(exp{β0(V0-V d )}-1) (10) Here, LHS is the measured signal, and RHS is a fixed constant value based on the ESPVR and EDPVR model parameters.

[0038] Singularity (P LA Let's define (t) as follows: g(λ):=α0(exp{β0(V0-V d )}-1) (11) Here, λ=[α0,β0,V d [V0] is a vector of constant parameters for both the EDPVR and ESPVR models. g(λ) can be determined by using only the ESPVR and EDPVR parameters, where g(λ) is the P of the Frank-Stirling curve in equation (6). LA Please understand that this corresponds to the intercept.

[0039] In addition,

number

number

[0040] Other findings from the analysis of g(λ) are further as follows. [Number]

[0041] P LA (t) > 0 mmHg in a realistic physiological range, according to Equation (12), it may be necessary to satisfy g(λ) ≤ 0. When the relationship of V d < V0 is fixed, 0 < g(λ) cannot be avoided. However, the singularity condition can be avoided by minimizing g(λ) by optimizing α [Number] assuming acceptable values of, α ed and β ed .

[0042] The second singularity is from Equation (9) for E es and occurs when the denominator equals zero. The singularity condition is as follows. [Number] Here, the LHS is the measured signal or numerically calculated signal, and the RHS is the constant parameter. The physiological range of these measured values is [Number] and [Number] Given this, the constraint to avoid this second singularity condition 214 can be derived as follows.

number

[0043] As an alternative to or addition to the above singularity avoidance, the parameters mentioned above can be optimized to avoid singularities. For example, to estimate the parameter λ that facilitates singularity avoidance, the optimization problem can be formulated as a constrained nonlinear least squares problem as follows:

number

number

[0044] The parameter k is given by the inequality V0 ≤ V, as shown in curve 216. d It is set to impose this, which means P LA We guarantee that there are no singularities in equation (8) when considering (t) > 0 mmHg. By iterating over the parameter k, the optimal parameter λ can be determined when the objective function shown in equation (16) gives the minimum error.

[0045] Figure 3 shows a flowchart of an exemplary method 300 based on an exemplary embodiment of the present disclosure. The exemplary method 300 can be performed using any part of the analysis model 200 shown in Figure 2, and any combination of components of the computing environment 100 shown in Figure 1. It should be understood that the steps of method 300 are merely examples and should not be considered limiting. Methods having additional, alternative, or fewer steps should be considered within the scope of the present disclosure.

[0046] Method 300 may begin in step 302. In step 302, the server 102 may receive input of cardiac data. For example, a hospital terminal desktop may be used by a clinician to input cardiac data. In some embodiments, the clinician may input cardiac data on a smartphone or tablet computer. In some embodiments, cardiac data may be transmitted by a cardiac monitoring system. Cardiac data may include, for example, a single point of left ventricular end-diastolic volume and pressure (measured non-invasively by echocardiography), pulmonary capillary wedge pressure, and cardiac output.

[0047] In step 304, server 102 may calculate myocardial contractility based on the received input cardiac data. For example, server 102 may extrapolate the EDPVR curve using a single point of left ventricular end-diastolic volume to generate more samples from a single point (e.g., by using equation 16 associated with the analysis model 200). Server 102 may then update the EDPVR parameters by imposing physiological constraints and singularity avoidance (by the analysis model 200). Server 102 then calculates E based on the received pulmonary capillary wedge pressure and cardiac output. es This can be estimated (as an index of myocardial contractility). For estimation, server 102 may consider estimating cardiac output using pulmonary capillary wedge pressure as left atrial pressure and further using a pulmonary artery catheter.

[0048] In step 306, server 102 may calculate an optimal drug combination based on myocardial contractility. For example, the calculation of the optimal drug combination may be based on the desired myocardial contractility relative to the calculated myocardial contractility.

[0049] In some embodiments, method 300 may include step 308a. In step 308a, server 102 may output an optimal drug combination for assisting in clinical decision-making (e.g., at the requesting device). That is, a clinician may rely on the tested and simulated model to aid in decision-making and reduce reliance on speculation.

[0050] In some embodiments, method 300 may include step 308b. In step 308b, server 102 may use an optimal drug combination for a closed-loop hemodynamic system. That is, server 102 may send instructions to cardiac monitoring system 122 to automatically inject the optimal drug combination.

[0051] In some embodiments, server 102 may perform both step 308a and step 308b.

[0052] The disclosed analytical model 200 has been evaluated through experiments and simulations. In one animal experiment, dogs were anesthetized and both carotid baroreceptors and vagus nerves were removed bilaterally. Thoracotomy was performed, and then the dogs were connected to a system that measured arterial pressure (AP) from the right femoral artery, cardiac output (CO) by an ultrasonic flowmeter around the ascending aorta, left atrial pressure (P LA ) and right atrial pressure (P RA ), left ventricular pressure (P LV ) from a micromanometer, and heart rate (HR) from an electrocardiogram (ECG) sensor. Two pairs of sonomicrometry crystals were placed inside the left ventricle to estimate left ventricular volume (V LV ) using a method based on the modified ellipsoid formula.

[0053] In the drug injection experiment, (P LV,V LV ) Inferior vena cava occlusion (IVCO) was performed to record the end points of space contraction and expansion. Next, the reference value was measured for 1 minute. Next, a single drug was administered for 10 minutes to measure the pharmacological effect until a steady state was reached. Finally, IVCO was performed again to measure the effect of the drug on E es before stopping drug administration. A washout time was given until the mean atrial pressure (MAP) stabilized at the value before drug administration. Then, E es before and after drug administration was calculated by applying linear regression to the end point of contraction. Additionally, the non-linear least squares method was applied to the end point of expansion to fit the EDPVR function in the above equation (4) before drug administration. Both of these fitting algorithms (i.e., linear regression to the end point of contraction and non-linear least squares method to the end point of expansion) were performed independently and used for a comparative study to evaluate the disclosed analytical model. For each of four important drugs used in the treatment of acute heart failure, dobutamine (DOB) at a dose of 5.0 μg / kg / min as an inotropic agent, norepinephrine (NE) at a dose of 0.15 μg / kg / min as a vasoconstrictor, sodium nitroprusside (SNP) at a dose of 5.0 μg / kg / min as a vasodilator, and dextran (DEX) at a dose of 5.0 ml / kg as a fluid, the same procedure was repeated. Note that this animal experiment was approved by the Animal Experiment Committee of the National Cardiovascular Center (NCVC) in Japan.

[0054] The animal experiment was further used for drug library development. The drug library represents the effect of combinations of drugs on cardiovascular parameters, as detailed herein. For this experiment, let x be

Number

number

[0055] This drug infusion system can be described as follows: x f =B u +x0(17) In equation (17) above, the input matrix B is a drug library representing the multiple dependency effects from each drug (e.g., as described above) on a steady-state cardiovascular parameter x, e.g., E es As a result of the changes in MAP(t), CO(t), P LA Multiple cardiovascular indicators such as (t) are adjusted. To develop drug library B (i.e., input matrix), each drug input u is fitted by fitting a one-order single-input single-output process model. i From each cardiovascular parameter x i The gains to were identified. The identified gains were aggregated to form the first drug library B according to an embodiment of the ESPVR estimation method. a and the second drug library B b However, it can be developed as follows:

[0056]

number

[0057] Furthermore, hemodynamic changes (i.e., changes in cardiovascular indices) can be simulated. For example, a mechanical, lumped parameter model of the cardiovascular system was used to simulate hemodynamic changes caused by drug administration. The simulator modeled blood flow through the cardiovascular system by using established electrical analogs for fluid dynamics, representing vascular resistance as an electrical resistor and compliance as an electrical capacitor. The model further modeled the valves at the exit of each chamber with electrical diodes and electrical resistors, using a time-varying elastance function for each chamber. Thus, each time-varying elastance function depends on the chamber-specific parameters of ESPVR and EDPVR, and in each cardiac cycle, the function is E es Contraction was simulated as a sinusoidal increase in elastance up to the peak, followed by relaxation (indicating a decrease in elastance) combining sinusoidal and exponential functions. The modified Windkessel vascular components may also be used to represent systemic and pulmonary circulation, each including characteristic impedance along with resistance and capacitance distributed to the arterial, capillary, and venous portions of the circulation. Together, these cardiac and vascular components, like lumped-parameter models, can enable the simulation of time-varying pressure and flow across the entire cardiovascular system. This model was simulated using MATLAB and Simulink computing software (version R2021b, The MathWorks, Inc.). These software programs have been simulated for different parameter values. LA ,P RA Outputs CO and MAP. The identified drug library can be used to tune cardiovascular parameters within this model and obtain hemodynamic simulation results under relevant drug infusion scenarios.

[0058] The implementation of singularity avoidance can also be experimentally verified. For comparison, the following experimental / analysis method, (i) E by inverse estimation using singularity avoidance, can be used. es(ii) E by inverse estimation using ESPVR and EDPVR parameters identified by independent fitting. es (iii) E obtained by linear regression to the end point of contraction es This can be performed. The dataset analyzed in this experiment consists of cardiovascular parameter x and cardiovascular index data obtained as a result of four different drug administrations in the animal experiments described above. Table I shows the parameter settings that can be used in these methods. The optimization library of the SciPy software can be used as the solver.

[0059] [Table 1]

[0060] Figure 4 shows exemplary graphs 402–408 visualizing a comparison between different methods according to exemplary embodiments of the present disclosure. As can be seen in graphs 402–408, singularity avoidance is smoother, particularly for dobutamine and dextran injection. es It produces a curve. As demonstrated using the embodiments disclosed herein, norepinephrine is E es It is clinically expected to increase (Graph 404). Furthermore, as clinically expected, dextran injection (Graph 408) causes minimal change based on the embodiments disclosed herein. Symmetrically, the linear fit shows a decrease, which is likely due to the nonlinearity of ESPVR, and V LV If increases, linear fitting is E es A decrease in [the specified value] can be identified. If an inverse embodiment is used, such nonlinearity does not occur.

[0061] E es This can be further verified by simulating hemodynamic changes (i.e., changes in cardiovascular indicators). In clinical practice, E follows drug administration. esIt may be important to accurately reproduce the hemodynamic behavior (i.e., changes in cardiovascular indicators) caused by changes in E. As described throughout this disclosure, a drug library (e.g., matrix B) can model changes in cardiovascular parameters x induced by drug administration. By using the identified drug library, the simulation may reproduce the pharmacological effects on cardiovascular parameters x, which in turn modulate the cardiovascular indicators. For this purpose, the simulation uses two E es Estimation methods, (i) E estimated by inverse estimation using singularity avoidance es , and (ii) E by linear regression to the end point of contraction es , can be considered. For evaluation, the resulting 3D hemodynamics (ΔP LA The adjustment of ΔCI and ΔMAP is two E values ​​related to animal data. es It can be used to visualize performance comparisons of estimation methods. In particular, in the case of heart failure treatment scenarios, a common index space (ΔP) can be used. LA ΔCI) can be calculated as CO, where cardiac CI measured at L / min / m2 is normalized by body surface area and can be used based on the Forrester classification.

[0062] Figure 5 shows exemplary graphs 502–508 illustrating hemodynamic adjustments based on exemplary embodiments of the present disclosure. In particular, graph 502 shows the index space (ΔP) simulated based on the ESPVR and linear fitting analysis model of ESPVR disclosed herein. LA The graph shows the ΔCI) and both are compared with real-world animal experiment data for the injection of the drug dobutamine. Graph 504 shows the simulated index space (ΔP) based on the ESPVR and the ESPVR analysis model derived by linear fitting disclosed herein. LA The graph shows the index space (ΔP ,ΔCI), both of which are compared to real-world animal experiment data for the injection of the drug norepinephrine. Graph 506 shows the ESPVR disclosed herein and the simulated index space (ΔP ,ΔCI) based on the ESPVR analysis model derived by linear fitting. LAThe graph shows the index space (ΔP ,ΔCI), both of which are compared to real-world animal experiment data for the injection of the drug sodium nitroprusside. Graph 508 shows the ESPVR disclosed herein and the simulated index space (ΔP ,ΔCI) based on the ESPVR analysis model derived by linear fitting. LA The results show ΔP(2, ΔCI), both of which are compared to real-world animal experiment data for drug dextran injection. As can be seen in Graphs 502–508, simulations based on the analytical models disclosed herein are more accurate than linear fitting methods for ΔP(2, ΔCI). LA We reproduced real-world data for ΔCI.

[0063] Figure 6 shows exemplary graphs 602–608 illustrating hemodynamic adjustments based on exemplary embodiments of the present disclosure. In particular, graph 602 shows an index space ΔMAP simulated based on the ESPVR and linear fitting analysis model of ESPVR disclosed herein, both compared with real-world animal experimental data for the infusion of the drug dobutamine. Graph 604 shows an index space ΔMAP simulated based on the ESPVR and linear fitting analysis model of ESPVR disclosed herein, both compared with real-world animal experimental data for the infusion of the drug norepinephrine. Graph 606 shows an index space ΔMAP simulated based on the ESPVR and linear fitting analysis model of ESPVR disclosed herein, both compared with real-world animal experimental data for the infusion of the drug sodium nitroprusside. Graph 608 shows an index space ΔMAP simulated based on the ESPVR and linear fitting analysis model of ESPVR disclosed herein, both compared with real-world animal experimental data for the infusion of the drug dextran. As can be seen in Graphs 602-608, simulations based on the analytical models disclosed herein reproduced real-world ΔMAP data more accurately than linear fitting methods.

[0064] Therefore, the embodiments disclosed herein have reduced sensitivity to the nonlinearity of ESPVR. es The robustness of the estimation can be improved. For example, in the dextran injection shown in Graph 408 of Figure 5, the E estimated by the analytical model disclosed herein is es This may not change substantially after drug administration, and E estimated by linear fitting es This may be affected by the nonlinearity of ESPVR. As further shown in Graph 508 in Figure 5 and Graph 608 in Figure 6, the analytical models disclosed herein reproduced the response to dextran with higher accuracy than linear fitting. In addition, infusion of dobutamine, norepinephrine, and sodium nitroprusside affected the cardiovascular parameter x (i.e., R). s , E es When simultaneous changes in HR and SBV affect both preload and afterload, the results described above indicate that the analytical model is unaffected by such changes, and E es This demonstrates that it is possible to estimate it accurately.

[0065] As shown in Graph 406 of Figure 4, during sodium nitroprusside injection, E es It should be further noted that a decrease may be observed. This is likely caused by hyperperfusion of the coronary circulation due to an excessive drop in blood pressure, resulting in an actual decrease in myocardial contractility. As shown by Graph 506 in Figure 5 and Graph 606 in Figure 6, the simulation using the embodiments disclosed herein es This demonstrates that estimation is advantageous for accurately identifying pharmacological effects.

[0066] Figure 7 shows a block diagram of an exemplary computing device 700 that performs various features and processes according to exemplary embodiments of the present disclosure. For example, in some embodiments, the computing device 700 may function as a server 102, a client 106, a cardiac monitoring system 122, or part or combination thereof. Furthermore, the computing device 700 may host and deploy the analysis model 200 in part or in whole. The computing device 700 may also perform one or more steps of Method 300. The computing device 700 is implemented on any electronic device that runs a software application derived from compiled instructions, including but not limited to personal computers, servers, smartphones, media players, electronic tablets, game consoles, and email devices. In some embodiments, the computing device 700 includes one or more processors 702, one or more input devices 704, one or more display devices 706, one or more network interfaces 708, and one or more computer-readable media 712. Each of these components is coupled by a bus 710.

[0067] The display device 706 includes any display technology, including but not limited to liquid crystal display (LCD) or light-emitting diode (LED) technology. The processor 702 uses any processor technology, including but not limited to graphics processors and multicore processors. The input device 704 includes any known input device technology, including but not limited to keyboards (including virtual keyboards), mice, trackballs, and touch-sensitive pads or displays. The bus 710 includes any internal or external bus technology, including but not limited to ISA, EISA, PCI, PCI Express, USB, Serial ATA, or FireWire. The computer-readable medium 712 includes any non-temporary computer-readable medium that provides instructions to the processor 702 for execution, including but not limited to non-volatile storage media (e.g., optical disks, magnetic disks, flash drives, etc.) or volatile media (e.g., SDRAM, ROM, etc.).

[0068] The computer-readable medium 712 contains various instructions 714 for implementing an operating system (e.g., Mac OS®, Windows®, Linux). The operating system may be multi-user, multi-processing, multi-tasking, multi-threaded, real-time, etc. The operating system performs basic tasks including, but not limited to, recognizing input from input device 704, sending output to display device 706, tracking files and directories on computer-readable medium 712, controlling peripheral devices (e.g., disk drives, printers, etc.) that can be controlled directly or through an I / O controller, and managing traffic on bus 710. Network communication instructions 716 establish and maintain network connections (e.g., software for implementing communication protocols such as TCP / IP, HTTP, Ethernet, telephone, etc.).

[0069] The myocardial contractility calculation instruction 718 includes an instruction that performs the disclosed process for calculating myocardial contractility for clinical decision-making and / or closed-loop hemodynamic control systems, as described throughout this disclosure. An application 720 may include an application that uses or performs the process and / or other processes described herein. The process may also be performed by an operating system.

[0070] The features described may be implemented in one or more computer programs that can be run on a programmable system which includes at least one programmable processor coupled to receive data and instructions from a data storage system, at least one input device, and at least one output device, and to send data and instructions to the data storage system, at least one input device, and at least one output device. A computer program is a set of instructions that can be used directly or indirectly in a computer to perform an activity or to produce a result. A computer program may be written in any form of programming language (e.g., Objective-C, Java), including compiled or interpreted languages, and it may be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. In one embodiment, this may include Python. Thus, a computer program is multilingual.

[0071] Processors suitable for executing instruction programs may include, for example, both general-purpose and dedicated microprocessors, as well as one of a single processor or multiple processors or cores in any type of computer. Generally, a processor may receive instructions and data from read-only memory or random-access memory, or both. Essential elements of a computer may include a processor for executing instructions, and one or more memories for storing instructions and data. Generally, a computer may also include one or more large storage devices for storing data files, or may be operably coupled to them for communication. Such devices include magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and optical disks. Storage devices suitable for tangibly embodying computer program instructions and data may include, for example, semiconductor memory devices such as EPROMs, EEPROMs, and flash memory devices, magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and all forms of non-volatile memory, including CD-ROMs and DVD-ROM disks. Processors and memories may be assisted by or incorporated into ASICs (Application-Specific Integrated Circuits).

[0072] To provide user interaction, the features may be implemented on a computer having a display device such as a CRT (cathode ray tube) or LCD (liquid crystal display) monitor for displaying information to the user, and a pointing device such as a keyboard and mouse or trackball that the user can use to provide input to the computer.

[0073] The features can be implemented in a computer system or any combination thereof, including backend components such as data servers, middleware components such as application servers or internet servers, or frontend components such as client computers having a graphical user interface or internet browser. The components of the system can be connected by any form or medium of digital data communication, such as a communication network. Examples of communication networks include, for example, telephone networks, LANs, WANs, and networks that form computers and the internet.

[0074] A computer system may include clients and servers. Clients and servers may generally be remote from each other and typically interact over a network. The relationship between a client and a server may arise from computer programs running on each computer that have a client-server relationship with each other.

[0075] One or more features or steps of the disclosed embodiments may be implemented using an API. The API may define one or more parameters that are passed between a calling application and other software code (e.g., an operating system, library routines, functions) that provides services, provides data, or performs actions or calculations.

[0076] An API may be implemented as one or more calls in program code that send or receive one or more parameters through a parameter list or other structure based on the calling convention defined in the API specification document. Parameters may be constants, keys, data structures, objects, object classes, variables, data types, pointers, arrays, lists, or other calls. API calls and parameters may be implemented in any programming language. The programming language may define the vocabulary and calling conventions that programmers employ to access the features supporting the API.

[0077] In some embodiments, an API call may report to the application the capabilities of the device running the application, such as input capabilities, output capabilities, processing capabilities, power supply capabilities, and communication capabilities.

[0078] Additional examples of embodiments of the methods and devices described herein are suggested in accordance with the structures and techniques described herein. Other non-limiting examples may be configured to operate separately or may be combined in any permutation or combination with any one or more of the other examples provided above or throughout this disclosure.

[0079] Those skilled in the art will understand that this disclosure can be embodied in other specific forms without departing from the spirit or essential characteristics of this disclosure. Therefore, the embodiments disclosed herein are intended to be illustrative and not limiting in all respects. The scope of this disclosure is indicated by the appended claims rather than the foregoing description, and all modifications within the meaning, scope, and equivalents thereof are intended to be contained therein.

[0080] Please note that the terms “including” and “comprising” should be interpreted as “including but not limited to.” Unless explicitly stated in the claim, the term “a” should be interpreted as “at least one,” and “the,” “said,” etc., should be interpreted as “the at least one,” “said at least one,” etc. Furthermore, the applicant intends that only claims containing the explicit phrase “means for” or “step for” should be interpreted under § 112(f) of the U.S. Patent Act. Claims that do not explicitly contain the phrase “means for” or “step for” should not be interpreted under § 112(f) of the U.S. Patent Act.

Claims

1. A computer implementation method, The computing system receives the pulmonary capillary wedge pressure and cardiac output of the human heart, Based on the left ventricular end-systolic elastance generated by the computing system as an inverse function of the gradient between the pulmonary capillary wedge pressure and the cardiac output, the myocardial contractility of the human heart is determined while avoiding singularities in the inverse function. The computing system generates the optimal drug combination to reach the target myocardial contractility from the determined myocardial contractility, A computer implementation method, including

2. The computer implementation method according to claim 1, further comprising outputting the optimal drug combination to a client device using the computing system.

3. The computer-aided method according to claim 1, further comprising controlling a closed-loop hemodynamic control system based on the optimal drug combination so that the computing system automatically injects the optimal drug combination.

4. The computer-aided method according to claim 1, further comprising transmitting the optimal drug combination to a closed-loop hemodynamic control system that automatically injects the optimal drug combination using the computing system.

5. The computing system receives a single point in time for left ventricular end-diastolic pressure and volume, The computing system extrapolates the relationship curve between left ventricular end-diastolic pressure and volume from the single point, The computing system applies physiological constraints to the extrapolated left ventricular end-diastolic pressure-volume relationship curve in order to generate updated left ventricular pressure-volume relationship parameters, The computing system generates the optimal drug combination based on the updated left ventricular pressure and volume relationship parameters, The computer implementation method according to claim 1, further comprising:

6. The computer-assisted method according to claim 5, wherein the single point of left ventricular end-diastolic pressure and volume is measured non-invasively using echocardiography.

7. The computer-assisted method according to claim 1, wherein the pulmonary capillary wedge pressure is measured using a pulmonary artery catheter.

8. The computer-assisted method according to claim 1, wherein the cardiac output is measured using a pulmonary artery catheter.

9. The computing system avoids singularities by parameter optimization, as described in claim 1.

10. It is a system, A non-temporary storage medium for storing computer program instructions, A processor configured to execute the computer program instructions for causing an action, wherein the action is Receiving the pulmonary capillary wedge pressure and cardiac output of the human heart, Based on the left ventricular end-systolic elastance generated as the inverse function of the slope between the pulmonary capillary wedge pressure and the cardiac output, the myocardial contractility of the human heart is determined while avoiding the singularity of the inverse function. To generate the optimal drug combination to reach the target myocardial contractility from the determined myocardial contractility, A system that includes this.

11. The system according to claim 10, wherein the operation further comprises outputting the optimal drug combination to a client device.

12. The system according to claim 10, further comprising controlling a closed-loop hemodynamic control system based on the optimal drug combination so as to automatically inject the optimal drug combination.

13. The system according to claim 10, wherein the operation further comprises transmitting the optimal drug combination to a closed-loop hemodynamic control system that automatically injects the optimal drug combination.

14. The aforementioned operation is, Receiving a single point in left ventricular end-diastolic pressure and volume, Extrapolating the relationship curve between left ventricular end-diastolic pressure and volume from the aforementioned single point, To generate updated left ventricular pressure-volume relationship parameters, physiological constraints are applied to the extrapolated left ventricular end-diastolic pressure-volume relationship curve, Based on the updated left ventricular pressure and volume relationship parameters, the optimal drug combination is generated. The system according to claim 10, further comprising:

15. The system according to claim 14, wherein the single point of left ventricular end-diastolic pressure and volume is measured non-invasively using echocardiography.

16. The system according to claim 10, wherein the data on pulmonary capillary wedge pressure and cardiac output are measured using a pulmonary artery catheter.

17. The system according to claim 10, wherein the processor avoids the singularity by parameter optimization.

18. The system according to claim 10, wherein the myocardial contractility includes left ventricular end-systolic resistance.

19. A non-temporary storage medium that stores computer program instructions that cause a computing system to perform an action when executed, wherein the action is Receiving the pulmonary capillary wedge pressure and cardiac output of the human heart, Based on the left ventricular end-systolic elastance generated as the inverse function of the slope between the pulmonary capillary wedge pressure and the cardiac output, the myocardial contractility of the human heart is determined while avoiding the singularity of the inverse function. To generate the optimal drug combination to reach the target myocardial contractility from the determined myocardial contractility, Non-temporary storage media, including [specific type of storage medium].

20. The non-temporary storage medium according to claim 19, further comprising controlling a closed-loop hemodynamic control system based on the optimal drug combination to automatically inject the optimal drug combination.

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