Systems and methods for treating hypertrophic cardiomyopathy

Abstract

In some aspects, disclosed herein are systems and methods for treating hypertrophic cardiomyopathy (HCM) in a subject. In some embodiments, the systems and methods described herein include monitoring heart performance, and optionally, in some cases, detecting abnormalities in the heart performance after administration of an initial HCM treatment. In some cases, the initial HCM treatment includes the administration of a myosin inhibitor and / or a septal muscle resection. In some embodiments, the cardiac performance is associated with right ventricular function and / or pulmonary arterial pressure. In some embodiments, the systems and methods described herein include identifying a therapy for a subject in order to help reduce the risk of worsening cardiac performance. In some cases, identifying the therapy includes predicting the effectiveness of the therapy with respect to cardiac performance. In some embodiments, suggestions for adjusting therapy are provided based on observed outcomes of cardiac performance.

Description

[0001] Cross-reference

[0002] This application claims the benefit and priority of U.S. Patent Application No. 63 / 380,500, filed on October 21, 2022, titled "System and Method of Treatment of Hypertrophic Cardiomyopathy", which is incorporated herein by reference in its entirety. BACKGROUND OF THE INVENTION

[0003] Hypertrophic cardiomyopathy (HCM) involves a heart disease that affects the myocardium, such as most commonly causing thickening of the myocardium at the septum between the right and left ventricles, which can lead to a stiffening of the heart wall and mitral valve changes, etc., which can impede normal blood flow out of the heart. Thus, in some embodiments, the heart will begin to pump faster in order to provide sufficient blood to the subject, and / or in some cases, the heart will pump harder in order to overcome the pressure buildup due to reduced blood flow. Existing HCM treatments include providing drug therapies in order to reduce the contractility of the heart's pumping and / or reduce the rate at which the heart pumps, thereby improving the overall function of the heart. Exemplary drug therapies include administering myosin inhibitors, one or more beta blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, and / or one or more anticoagulants. In some cases, such as obstructive HCM, HCM treatment includes one or more invasive measures, such as septal myectomy, septal ablation, implantable cardioverter defibrillators, or other measures known in the art. SUMMARY OF THE INVENTION

[0004] In some aspects, a method for treating hypertrophic cardiomyopathy in a subject is disclosed herein, the method comprising: detecting an abnormality in the cardiac performance of the subject, wherein the abnormality corresponds to one or more of an abnormal pulmonary artery pressure, pulmonary hypertension, an abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular strain, and right ventricular size; optionally predicting the efficacy of a first therapy for reducing the abnormality; optionally administering to the subject a first therapy or a second therapy based on the predicted efficacy of the first therapy; and monitoring the abnormality and optionally detecting negative effects after administering the first therapy or the second therapy to the subject.

[0005] In some embodiments, the first and / or second therapy comprises one or more types of therapy, the one or more types of therapy comprising myosin inhibition, therapy for reducing pulmonary vascular resistance, one or more beta blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, and / or one or more blood thinners or any combination thereof. In some embodiments, the negative effects include an increased abnormality in cardiac performance from (a).

[0006] In some embodiments, predicting the efficacy of a first therapy includes: obtaining one or more health parameters of a subject; and applying the one or more health parameters to one or more correlations associated with the first therapy. In some embodiments, the one or more correlations are based on: one or more health parameters received from a population of individuals; and for each individual in the population of individuals, i) a negative effect on the corresponding cardiac performance due to the first therapy being administered, ii) a positive outcome on the corresponding cardiac performance due to the first therapy being administered, or iii) no effect on the corresponding cardiac performance due to the first therapy being administered, such that when combined with the administration of the first therapy, at least one of the one or more health parameters (from the corresponding individual in the population of individuals) is associated with an effect on cardiac performance. In some embodiments, applying the one or more correlations of the data includes using a machine learning algorithm.

[0007] In some embodiments, detecting an anomaly and / or monitoring an anomaly includes using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof. In some embodiments, detecting an anomaly and / or monitoring an anomaly is performed in a medical environment, a dynamic environment, or both. In some embodiments, detecting an anomaly and / or monitoring an anomaly includes obtaining an ECG, where the ECG includes a single-lead ECG, a 2-lead ECG, a 6-lead ECG, or a 12-lead ECG.

[0008] In some embodiments, the method further includes adjusting the first or second therapy based on detecting a corresponding negative effect or corresponding no effect of an anomaly on cardiac performance. In some embodiments, adjusting the first or second therapy includes administering a third therapy and / or reducing the amount of the first or second therapy being administered. In some embodiments, reducing the amount of the first or second therapy being administered includes reducing the frequency of the first or second therapy being administered and / or reducing the dose of the first or second therapy being administered. In some embodiments, prior to detecting an anomaly in cardiac performance, the method further includes administering an initial HCM treatment to the subject. In some embodiments, the initial HCM treatment includes administering a myosin inhibitor to the subject.

[0009] In some embodiments, hypertrophic cardiomyopathy includes obstructive hypertrophic cardiomyopathy (oHCM). In some embodiments, prior to detecting an anomaly in cardiac performance, the method further includes at least partially removing an obstruction associated with oHCM. In some embodiments, at least partially removing the obstruction includes performing a septal myectomy.

[0010] In some aspects, the present disclosure provides a non - transitory computer - readable medium for treating a subject with hypertrophic cardiomyopathy. The non - transitory computer - readable medium includes instructions that, when executed by a processor, cause the processor to perform operations including: detecting an abnormality in the cardiac performance of the subject, where the abnormality corresponds to one or more of abnormal pulmonary artery pressure, pulmonary hypertension, abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular strain, and right ventricular size; optionally predicting the efficacy of a first therapy for reducing the abnormality; optionally determining a treatment comprising the administration of the first therapy or a second therapy to the subject based on the predicted efficacy of the first therapy; and monitoring the abnormality and optionally detecting negative effects after administering the first therapy or the second therapy to the subject.

[0011] In some embodiments, the first and / or second therapy includes one or more types of therapies, and the one or more types of therapies include myosin inhibition, therapies for reducing pulmonary vascular resistance, one or more beta - blockers, one or more calcium - channel blockers, one or more anti - arrhythmic drugs, and / or one or more blood thinners or any combination thereof. In some embodiments, the negative effects include an abnormal increase in cardiac performance from (a).

[0012] In some embodiments, predicting the efficacy of the first therapy includes: obtaining one or more health parameters of the subject; and applying the one or more health parameters to one or more correlations associated with the first therapy. In some embodiments, the one or more correlations are based on: one or more health parameters received from a population of individuals; and for each individual in the population of individuals, i) a negative effect on the corresponding cardiac performance due to the first therapy being administered, ii) a positive outcome on the corresponding cardiac performance due to the first therapy being administered, or iii) no effect on the corresponding cardiac performance due to the first therapy being administered, such that at least one of the one or more health parameters (from the corresponding individual in the population of individuals) is associated with an effect on cardiac performance when combined with the administration of the first therapy. In some embodiments, applying the one or more correlations of the data includes using a machine - learning algorithm.

[0013] In some embodiments, detecting the abnormality and / or monitoring the abnormality includes obtaining one or more cardiac parameters of the subject using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof. In some embodiments, detecting the abnormality and / or monitoring the abnormality is performed in a medical environment, a dynamic environment, or both. In some embodiments, detecting the abnormality and / or monitoring the abnormality includes obtaining an ECG, where the ECG includes a single - lead ECG, a 2 - lead ECG, a 6 - lead ECG, or a 12 - lead ECG.

[0014] In some embodiments, the method further includes determining an adjustment to the first or second therapy based on detecting a corresponding negative effect or a corresponding lack of effect on cardiac performance. In some embodiments, the adjustment to the first or second therapy includes determining the administration of a third therapy and / or reducing the amount of the first or second therapy administered. In some embodiments, reducing the amount of the first or second therapy administered includes reducing the frequency of the first or second therapy administered and / or reducing the dose of the first or second therapy administered.

[0015] In some embodiments, an initial HCM treatment is administered to a subject prior to detecting an abnormal cardiac performance. In some embodiments, the initial HCM treatment includes administering a myosin inhibitor to the subject. In some embodiments, hypertrophic cardiomyopathy includes obstructive hypertrophic cardiomyopathy (oHCM). In some embodiments, prior to detecting an abnormal cardiac performance, at least a portion of the obstruction associated with oHCM is removed. In some embodiments, at least a portion of the obstruction is removed via septal myectomy.

[0016] In some aspects, a system for treating hypertrophic cardiomyopathy in a subject is disclosed, the subject including: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform operations including: detecting an abnormality in the cardiac performance of the subject, wherein the abnormality corresponds to one or more of an abnormal pulmonary artery pressure, pulmonary hypertension, an abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular strain, and right ventricular size; optionally predicting the efficacy of a first therapy for reducing the abnormality; optionally determining a treatment including the administration of a first therapy or a second therapy to the subject based on the predicted efficacy of the first therapy; and monitoring the abnormality and optionally detecting a negative effect after administering the first therapy or the second therapy to the subject.

[0017] In some embodiments, the first and / or second therapy includes one or more types of therapy, the one or more types of therapy including myosin inhibition, therapies for reducing pulmonary vascular resistance, one or more beta blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, and / or one or more blood thinners or any combination thereof.

[0018] In some embodiments, the negative effects include an abnormal increase in cardiac performance from (a). In some embodiments, predicting the efficacy of a first therapy includes: obtaining one or more health parameters of a subject; and applying the one or more health parameters to one or more correlations associated with the first therapy. In some embodiments, the one or more correlations are based on: one or more health parameters received from a population of individuals; and for each individual in the population of individuals, i) a negative effect on the corresponding cardiac performance due to the first therapy being administered, ii) a positive outcome on the corresponding cardiac performance due to the first therapy being administered, or iii) no effect on the corresponding cardiac performance due to the first therapy being administered, such that when combined with the administration of the first therapy, at least one of the one or more health parameters (from the corresponding individual in the population of individuals) is associated with the effect on cardiac performance. In some embodiments, applying the one or more correlations of the data includes using a machine learning algorithm.

[0019] In some embodiments, detecting an abnormality and / or monitoring an abnormality includes obtaining one or more cardiac parameters of a subject using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof. In some embodiments, detecting an abnormality and / or monitoring an abnormality is performed in a medical environment, a dynamic environment, or both. In some embodiments, detecting an abnormality and / or monitoring an abnormality includes obtaining an ECG, where the ECG includes a single-lead ECG, a 2-lead ECG, a 6-lead ECG, or a 12-lead ECG.

[0020] In some embodiments, the method further includes determining an adjustment of the first or second therapy based on detecting a corresponding negative effect or corresponding lack of effect of the abnormality on cardiac performance. In some embodiments, the adjustment of the first or second therapy includes determining the administration of a third therapy and / or reducing the amount of the first or second therapy administered. In some embodiments, reducing the amount of the first or second therapy administered includes reducing the frequency of the first or second therapy administered and / or reducing the dose of the first or second therapy administered.

[0021] In some embodiments, an initial HCM treatment is administered to the subject prior to detecting an abnormality in cardiac performance. In some embodiments, the initial HCM treatment includes administering a myosin inhibitor to the subject.

[0022] In some embodiments, hypertrophic cardiomyopathy includes obstructive hypertrophic cardiomyopathy (oHCM). In some embodiments, prior to detecting an abnormality in cardiac performance, at least a portion of the obstruction associated with oHCM is removed. In some embodiments, at least a portion of the obstruction is removed via septal myectomy. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] These and other features, aspects, and advantages of some embodiments will become better understood from the following description and the accompanying drawings.

[0024] FIG. (Figure) 1 depicts an exemplary flowchart of a method for treating HCM according to embodiments described herein.

[0025] Figure 2 Depicts an exemplary system flowchart for treating HCM according to embodiments described herein.

[0026] Figure 3A Depicts a block diagram of a cardiac performance tool according to an embodiment.

[0027] Figure 3B Depicts a block diagram of exemplary clinical data parameters according to an embodiment.

[0028] Figure 4 Depicts an exemplary computer system according to an embodiment.

[0029] Figure 5 Depicts exemplary data correlating elevated pulmonary artery pressure with HCM.

[0030] Figure 6 Depicts exemplary data showing that elevated pulmonary artery pressure does not necessarily depend on changes in LVOT gradient pressure.

[0031] Figure 7 Depicts exemplary data depicting pulmonary artery pressure before, shortly after, and much longer after septal reduction therapy.

[0032] Figure 8 Depicts an exemplary flowchart depicting causation due to changes in certain cardiac conditions. DETAILED DESCRIPTION

[0033] I. Definitions

[0034] Unless otherwise noted, terms used in the claims and specification are defined as set forth below.

[0035] The terms "subject" or "patient" are used interchangeably and include cells, tissues, or organisms, human or non-human, whether in vivo, ex vivo, or in vitro, male or female.

[0036] The terms "treating," "treatment," or "therapy" are used interchangeably.

[0037] It must be noted that, unless the context clearly dictates otherwise, as used in the specification, the singular forms "a," "an," and "the" include plural referents.

[0038] The terms "dynamic", "dynamic measurement", "dynamic monitoring", "dynamic monitoring parameter", etc. refer to obtaining health data (e.g., subject health parameters as described herein) outside of a hospital or other medical facility (e.g., outside of a medical clinic). For example, dynamic monitoring can refer to monitoring health data (e.g., electrocardiogram, blood pressure, weight, etc.) at home.

[0039] As used in the specification and claims, the phrase "and / or" shall be understood to mean "any one or both" of the elements so joined, i.e., elements that co-exist in some cases and separate in other cases. Multiple elements listed with "and / or" shall be construed in the same manner, i.e., "one or more" of the elements so joined. In addition to the elements specifically identified by the "and / or" clause, other elements may optionally exist, whether related or unrelated to those specifically identified. Thus, as a non-limiting example, when used in conjunction with open-ended language such as "comprising", a reference to "A and / or B" may, in one embodiment, refer only to A (optionally including elements other than B); in another embodiment, only to B (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements).

[0040] As used in the specification and claims, "or" shall be understood to have the same meaning as "and / or" as defined above. For example, when separating items in a list, "or" or "and / or" shall be interpreted inclusively, i.e., including at least one of the multiple elements or the list of elements, but also including more than one element, and optionally, additional unlisted items. Only terms that explicitly indicate the contrary, such as "only one of... " or "exactly one of... ", or "consisting of... " when used in a claim, shall refer to including exactly one element of the multiple elements or the list of elements. Generally, the term "or" as used shall only be interpreted as indicating exclusive alternatives (i.e., "one or the other, but not both") when preceded by exclusive terms such as "any one", "one of... ", "only one of... " or "exactly one of... ".

[0041] II. Overview of HCM Treatment

[0042] In some embodiments, systems and methods for treating hypertrophic cardiomyopathy (HCM) in a subject are described herein. In some embodiments, the systems and methods described herein are provided in combination with an initial HCM treatment administered. In some embodiments, the systems and methods described herein include monitoring cardiac performance and optionally detecting an abnormality of the cardiac performance. In some embodiments, the cardiac performance is associated with right ventricular function and / or pulmonary artery pressure (PAP), which relates to the delivery of deoxygenated blood through the lungs, where the deoxygenated blood is oxygenated and then flows to the left atrium / ventricle for the delivery of oxygenated blood to the body. In some embodiments, the systems and methods described herein include identifying a therapy for the subject to help reduce the risk of deterioration of cardiac performance, wherein deterioration of cardiac performance may lead to a reduction in cardiac output (through the heart) for delivering sufficient blood to the subject's body, and / or may lead to the development of systolic dysfunction and associated heart failure. In some embodiments, such identification of a therapy includes predicting the effectiveness of the therapy relative to cardiac performance. In some embodiments, the systems and methods described herein include providing a recommendation for adjusting a therapy based on an observed effect on cardiac performance (e.g., an effect on right ventricular function, PAP, etc.).

[0043] As described herein, in some embodiments, the systems and methods for treating HCM described herein are based on monitoring cardiac performance and detecting any abnormalities thereof. As used herein, an abnormality of cardiac performance refers to a substandard parameter associated with cardiac surgery. For example, right ventricular function (RVF) drives deoxygenated blood through the lungs for oxygenation and to the left atrium / left ventricle for delivery to the whole body. Thus, the accumulation of pressure resistance caused by HCM, and / or the reduction in blood flow due to HCM, may cause the right ventricle to increase its contractility, and / or to pump with greater force, in order to overcome the accumulation of pressure resistance and increase the blood flow supplied to the body, respectively. In some embodiments, such an increase in right ventricular contractility and pumping force is associated with a decrease in right ventricular function (RVF). Thus, such a decrease in right ventricular function is associated with a decrease in cardiac performance or an abnormality of cardiac performance. In this example, an increase in right ventricular contractility may lead to right ventricular hypertrophy, which is a known heart disease, and may lead to cardiac output and / or systolic dysfunction.

[0044] In some embodiments, one or more cardiac parameters measured from a subject are used to determine cardiac performance as described herein. For example, in some embodiments, cardiac performance corresponds to one or more of pulmonary artery pressure (PAP) (including the presence of pulmonary hypertension), pulmonary vascular resistance, right ventricular pressure (systolic and / or diastolic), right ventricular hypertrophy (e.g., right ventricular dilation or increased muscle mass), right ventricular strain, and right ventricular size. In some embodiments, an abnormal reading of any one or any combination of such cardiac parameters is associated with an abnormal cardiac performance and thus with a decrease in cardiac performance. In some embodiments, such abnormal readings of the cardiac parameters include higher measured values compared to a standard range. For example, in some cases, the normal range of PAP for a subject is about 8 to 20 mmHg at rest, such that a higher PAP from this range can be associated with an abnormal cardiac performance and can be an indication of pulmonary hypertension. In some cases, the normal pulmonary vascular resistance is less than about 2 Wood units (WU), such that an increased resistance above 3 WU can be associated with an abnormal cardiac performance. In some embodiments, right ventricular pressure includes one or both of right ventricular systolic pressure and right ventricular diastolic pressure. In some cases, the normal range of right ventricular systolic pressure (related to contractility) is about 15 to 30 mmHg, and the normal range of right ventricular diastolic pressure (related to right ventricular filling) is about 1 to 7 mmHg, such that an increased pressure from one or both of these ranges can be associated with an abnormal cardiac performance. In some embodiments, the normal range of right ventricular size can be about 49 to 101 ml / m2 compared to a range of about 44 to 80 ml / m2 for the left ventricle, such that an increased size from the normal right ventricular size range and / or in proportion to the left ventricle can be associated with an abnormal cardiac performance.

[0045] In some embodiments, the further the measured cardiac parameter reading is from the corresponding normal range, the more abnormal or worse the corresponding cardiac performance. In some cases, the more abnormal or worse the cardiac performance, the higher the risk that the heart is below a sufficient cardiac output and / or the higher the risk of cardiac systolic dysfunction.

[0046] In some embodiments, cardiac performance is based on a combination of one or more cardiac parameters. For example, in some cases, an increase in PAP requires an increase in right ventricular force in order for the heart to maintain a certain cardiac output. Thus, although in some cases an increase in right ventricular force is associated with an abnormal cardiac performance, an increase in PAP with a lower right ventricular force can be a more severe abnormality than an increase in PAP with a higher right ventricular force because a lower right ventricular force may lead to a decrease in cardiac output.

[0047] Figure 1 ( Figure 1)An exemplary flowchart depicting a method for treating HCM in a subject as described herein. In some embodiments, the treatment comprises one or more stages, any of which may be administered alone or in combination with another stage to provide the treatment of HCM.

[0048] In some embodiments, the first stage 501 of the treatment comprises administering an initial treatment 502 for HCM as described herein. In some embodiments, the initial HCM treatment administered to the subject comprises a treatment currently known in the art. For example, in some embodiments, the initial treatment for HCM comprises performing a septal myectomy (for obstructive HCM) on the subject, or performing a septal ablation. Other forms of initial HCM treatment, which may or may not be administered in combination with septal myectomy or septal ablation, include providing drug therapy, such as administering myosin inhibitors, beta blockers, calcium channel blockers, antiarrhythmic drugs, and / or one or more anticoagulants.

[0049] In some embodiments, the second stage 503 of the treatment described herein comprises monitoring the cardiac performance of the subject and identifying the subject as having a reduced cardiac output and / or systolic dysfunction, and other risks of heart failure, based on the detection 504 of an abnormality in the cardiac performance of the subject. In some embodiments, the detection of the abnormality in the cardiac performance is based on measuring one or more cardiac parameters as described herein. In some embodiments, measuring the one or more cardiac parameters is via an implantable pulmonary artery monitor, echocardiogram, electrocardiogram (ECG), one or more biomarkers indicative of the cardiac performance state, or a combination thereof. As used herein, the cardiac performance state refers to the state of cardiac performance as measured via one or more cardiac parameters and includes abnormal, normal, or healthy cardiac performance. In some embodiments, the one or more cardiac parameters are measured in a clinical setting, such as in a medical office, hospital, laboratory, etc. In some embodiments, the one or more cardiac parameters are measured in a dynamic setting, e.g., via an ECG device from the subject's home or other non-clinical setting. As described herein below in the system overview section, in some embodiments, such dynamic monitoring may communicate with a system configured to monitor cardiac performance, provide communication to a healthcare provider (e.g., a doctor, other medical professional), and / or present one or more recommendations to the subject based on the cardiac performance readings, where such recommendations may be automatically generated by the system and / or provided via the healthcare provider.

[0050] Thus, in some embodiments, the second stage treatment 503 includes correlating cardiac performance with an initial HCM treatment (e.g., from step 502). For example, in some embodiments, after providing an initial HCM treatment (e.g., administering a myosin inhibitor) to a subject, the cardiac performance of the subject is then monitored to determine whether an abnormality (e.g., a persistent abnormality) exists and whether such an abnormality is increasing (e.g., whether cardiac performance is deteriorating or becoming more abnormal).

[0051] For example, in some embodiments, septal myectomy (e.g., as an initial HCM treatment) is performed to remove the obstruction (i.e., obstructive HCM), thereby at least helping to reduce the left ventricular outflow tract (LVOT) gradient pressure. However, in some cases, a subject who has undergone septal myectomy may continue to experience high pulmonary artery pressure (PAP) for a period of time after the surgery, e.g., within one year. Thus, in such cases, it is necessary to increase right ventricular contractility in order to maintain sufficient cardiac output (sufficient blood flow to the body) and / or help prevent or reduce the risk of developing systolic dysfunction. See, e.g., Figures 5 - 7 , where Figure 5 depicts exemplary data showing that elevated PAP is common in patients with HCM (Reference: Circulation: Heart Failure. April 2017; 10(4):e003689; doi:10.1161 / CIRCHEARTFAILURE.116.003689), Figure 6 depicts that elevated PAP is not necessarily associated with the LVOT gradient pressure (i.e., a decrease or increase in the LVOT gradient pressure may not affect PAP) (Reference: Circulation: Heart Failure. April 2017; 10(4):e003689; doi:10.1161 / CIRCHEARTFAILURE.116.003689), and Figure 7 depicts exemplary data showing some cases where PAP remains elevated after septal reduction therapy (Reference: European Heart Journal, Volume 35, Issue 30, August 7, 2014, Pages 2032 - 2039, https: / / doi.org / 10.1093 / eurheartj / eht537).

[0052] In another exemplary embodiment, a drug therapy is administered as part of an initial HCM treatment, such as a myosin inhibitor, to help reduce the LVOT gradient pressure. Thus, similar to that described for septal myectomy, the PAP may remain elevated for a period of time. However, in some cases, the myosin inhibitor helps reduce the contractility of the heart, including RV contractility. Thus, in some cases, although the administration of the myosin inhibitor helps reduce the LVOT gradient pressure (due to HCM), the reduction in RV contractility may affect the ability of the RV to overcome the potentially maintained increase in PAP, thereby potentially affecting the heart's ability to maintain cardiac output (e.g., blood flow to the body), and / or may lead to systolic dysfunction. For example, see Figure 8 (Reference: Marvin A. Konstam. "Circulation". Evaluation and Management of Right-Sided Heart Failure: A Scientific Statement From the American Heart Association, Vol. 137, No. 20, pp. e578 - e622, DOI: (10.1161 / CIR.0000000000000560). As Figure 8 depicted, the administration of a myosin inhibitor may result in a reduction in right ventricular stroke volume, thereby reducing right ventricular cardiac output.

[0053] In some embodiments, such monitoring of cardiac performance helps alert the subject or other persons (e.g., healthcare providers) to an unexpected deterioration in cardiac performance after initial HCM treatment. In some embodiments, a therapy is administered to improve cardiac performance. In some embodiments, such therapies include providing a drug therapy and / or a surgical procedure to the subject. For example, in some embodiments, one or more of a myosin inhibitor, a beta blocker, a calcium channel blocker, an antiarrhythmic drug, and a blood thinner are provided to help reduce cardiac performance abnormalities. For example, in some cases, where a myosin inhibitor is provided as an initial HCM treatment and where cardiac performance abnormalities persist or worsen (e.g., due to reduced right ventricular contractility), one or more therapies that reduce pulmonary vascular resistance (e.g., a PDE5 inhibitor) may be provided to reduce the pressure required to pump blood from the right ventricle. Thus, this will help offset the reduction in right ventricular contractility via the myosin inhibitor.

[0054] In some embodiments, the effect 508 of a therapy administered to a subject is first predicted to help reduce the risk that the subject will experience a negative effect (e.g., a negative effect on cardiac performance) due to the therapy. In some embodiments, one or more subject health parameters, which may include one or more of the cardiac parameters described herein, are used to help predict the effect of the therapy (as described below in the System Overview section). For example, in the case where PAP is detected to be high in an individual, it may be predicted that administering a myosin inhibitor may result in an increased risk of reduced cardiac output and / or systolic dysfunction.

[0055] In some embodiments, as described herein, the effect of the therapy is based on one or more subject health parameters (which may include one or more of the cardiac parameters described herein), as described below in the System Overview section of this document, where exemplary subject health parameters are listed. In some embodiments, the predicted effect of the therapy is determined based on one or more correlations that relate one or more subject health parameters to the effect of a given therapy (as described below in the System Overview). In some embodiments, such correlations are based on data received from a plurality of individuals (e.g., a group of individuals), where such data includes one or more subject health parameters from each individual, the therapy administered, and the resulting effect on cardiac performance as described herein. For example, in some embodiments, the resulting effect corresponds to a decrease in cardiac performance (e.g., a negative effect), an improvement in cardiac performance (e.g., a positive effect), or no change in cardiac performance (e.g., no effect). In some embodiments, the correlations are developed using machine learning algorithms (as described herein). Thus, in some embodiments, the systems described herein are used to predict the effect of a therapy on the cardiac performance of a subject, which may include using machine learning algorithms.

[0056] Referring Figure 1 to step 508, in some embodiments, if a negative effect of the therapy is identified, then another therapy is evaluated. In some embodiments, using the systems described herein, a healthcare professional (e.g., a doctor, other medical professional) recommends one or more therapies to the system for evaluation and prediction of the effect on cardiac performance. In some embodiments, as described herein, the system automatically determines one or more therapies to be evaluated based on the determined cardiac performance and subject health parameters. In some embodiments, the therapy administered at step 506 is based on a therapy identified as predicting a positive effect or at least no effect.

[0057] In other embodiments, as described herein, an initial HCM treatment is not administered to a subject, and thus, step 504 is initially performed on the subject to identify cardiac performance abnormalities, followed optionally by identifying 508 and administering 506 a therapy. In some embodiments, such therapies include any therapy consistent with the initial HCM treatment herein (e.g., septal myectomy, myosin inhibitors, etc.). In some embodiments, the therapy includes one or more of the various therapies described herein, such as myosin inhibitors and PDE5 inhibitors or beta blockers, etc.

[0058] In some embodiments, a third stage 505 of the HCM treatment described herein includes monitoring 510 cardiac performance after administering the therapy from the second stage 503 (e.g., step 506). In some embodiments, monitoring cardiac performance includes using any means described herein, such as at step 504, which includes measuring one or more cardiac parameters, as described herein. In some embodiments, measuring the one or more cardiac parameters is via an implantable pulmonary artery monitor, echocardiogram, electrocardiogram (ECG), one or more biomarkers indicative of cardiac performance status, or a combination thereof. In some embodiments, the one or more cardiac parameters are measured in a clinical setting, such as in a medical office, hospital, laboratory, etc. In some embodiments, the one or more cardiac parameters are obtained in a dynamic setting, e.g., via an ECG device from the subject's home or other non-clinical setting.

[0059] In some embodiments, using the system described herein, dynamic monitoring allows for periodic measurement of one or more cardiac parameters in order to continuously monitor a subject over a period of time without the need to visit a medical office or clinic. In some embodiments, using the system described herein, such monitoring includes communicating the cardiac performance status to the subject and / or healthcare provider, where the progression or regression of abnormalities can be monitored.

[0060] In some embodiments, such monitoring 510 identifies 512 a negative effect on cardiac performance based on the rate of increase of the abnormality (e.g., based on the increase of one or more cardiac parameters deviating from the normal range, either alone or in combination, as described herein). In some embodiments, after detecting a negative effect on cardiac performance, for example, recommendations are provided by the system and / or personnel (e.g., the subject, healthcare professional) to adjust the administered therapy 514. In some embodiments, adjusting the therapy includes maintaining the same therapy, but changing one or more of the therapy dose and frequency. For example, in the case of providing a myosin inhibitor, in some embodiments, adjusting the therapy includes providing a reduced amount of the myosin inhibitor, and / or decreasing the frequency at which the myosin inhibitor is provided.

[0061] In some embodiments, adjusting the therapy includes changing one or more of the therapy dosage and frequency, and / or providing one or more additional therapies. For example, in the case where a myosin inhibitor is provided and it is found that while PAP remains elevated, the contractility of the right ventricle is decreased, it is recommended to administer a PDE5 inhibitor or other therapy to the subject to reduce pulmonary vascular resistance.

[0062] In some embodiments, adjusting the therapy includes stopping the provision of the existing therapy, and it is recommended to provide one or more additional therapies, or alternatively, not to provide any therapy (for a specified duration, or to stop completely).

[0063] In some embodiments, providing one or more additional therapies, with or without the existing therapy (from step 506), includes first predicting the effect of the therapy before recommending or administering it to the subject (via step 508). In some embodiments, in the case where a negative effect on cardiac performance is detected, or in some cases, where no positive effect is identified, the method restarts after step 504.

[0064] In some embodiments, where no negative effect is identified, or in some cases, if a positive effect 512 is identified, then the cardiac performance of the subject is continued to be monitored 510.

[0065] III. System Overview

[0066] In some embodiments, one or more steps of the methods described herein are performed using the systems described herein. For example, in some embodiments, Figure 1 phases 2 and 3 of the HCM treatment methods described are performed at least in part using the systems described herein.

[0067] Figure 2 An overview of an exemplary system 200 for detecting, monitoring, and managing cardiac performance abnormalities of a subject 202 is depicted. In some embodiments, the system 200 receives subject health parameter measurements 204 from one or more devices, and then these measurements are used by a cardiac performance tool (CPT) 206 to generate a cardiac performance output, which may include a cardiac performance status 208, a prediction of the effect of the therapy on the cardiac performance status, and a recommended therapy for reducing cardiac abnormalities.

[0068] In some embodiments, the system 200 provides an integrated management tool for detecting, monitoring, and managing cardiac performance abnormalities of the subject and for communicating cardiac performance monitoring, warnings, and / or recommendations to the subject and / or healthcare provider (e.g., a doctor, nurse, or any other medical professional).

[0069] Reference Figure 2, as described herein, the subject health parameter measurements 204 of subject 202 (e.g., ECG data from an ECG device) are received by a cardiac performance tool 206, which then generates an output related to the cardiac performance 208 of the subject. In some embodiments, the cardiac performance output is communicated to a display interface (e.g., a monitor, a screen, a smart device screen, etc.). As described herein, in some embodiments, the subject health parameter measurements 204 are obtained via a device or input (into a computing device that may include system 200) by an individual (e.g., the subject, a healthcare provider, a family member / friend of the subject, another individual). In some embodiments, where the subject health parameter measurements are obtained via a device, the device and the cardiac performance tool 206 are used by different parties. For example, a first party (e.g., subject 202, a medical professional, or any other person) operates an ECG device to obtain ECG data (e.g., subject health parameter measurements 204) from subject 202, where the ECG data is then provided to a second party (e.g., subject 202, a medical professional, or any other person different from the individual operating the ECG device), who implements the cardiac performance tool 206 to determine the cardiac performance output 208. In some embodiments, the device for obtaining the subject health parameter measurements 204 and the cardiac performance tool 206 are used by the same party. Similarly, in some embodiments, where the health parameter measurements are obtained via input into a computing device, the input and the operation of the cardiac performance tool 206 are performed by the same or different parties.

[0070] In some embodiments, the cardiac performance tool is provided by one or more computing devices, where the cardiac performance tool may be embodied as a computer system (e.g., see Figure 4 , reference numeral 400). Thus, in some embodiments, the methods and steps described with reference to cardiac performance tool 206 are performed on a computer. For example, in some embodiments, the cardiac performance tool is configured to apply one or more subject health parameter measurements to one or more decision engines (e.g., a trained model, a decision tree, an analytical expression, etc.) to predict the efficacy of a therapy for reducing cardiac performance abnormalities in a subject. In some embodiments, each of the one or more decision engines applies an algorithm such as a machine learning algorithm (as described herein) to the one or more subject health parameter measurements.

[0071] Refer to Figure 3A, according to an embodiment, a block diagram depicting exemplary computer logic components of the cardiac performance tool 206 is shown. Herein, the cardiac performance tool 206 includes an ECG data module 300, a clinical biomarker module 302, an imaging data module 304, a clinical data module 306, a decision engine module 308, a therapy risk prediction module 310, a monitoring and management module 312, an intervention module 314, a communication module 316, and a decision engine data storage device 318. In some embodiments, the cardiac performance tool 206 may be configured differently with additional or fewer modules. For example, the cardiac performance tool 206 does not need to include the imaging data module 304. In some embodiments, the decision engine module 308 and / or the decision engine data storage device 318 are located on different tools and / or computing devices.

[0072] As described herein, in some embodiments, the cardiac performance tool 206 is configured to determine a cardiac performance output 208 of a subject 202, which may include determining a cardiac performance state and / or predicting the efficacy of a therapy for reducing cardiac performance abnormalities. In some embodiments, the cardiac performance tool 206 applies subject health parameters obtained for the subject to one or more decision engines to determine the cardiac performance state and / or to determine the efficacy of a treatment for reducing cardiac performance abnormalities. In some embodiments, the subject's health parameters are obtained via the ECG data module 300, the clinical biomarker module 302, the imaging data module 304, and / or the clinical data module 306.

[0073] Subject health parameters

[0074] In some embodiments, as described herein, the subject health parameters include one or more cardiac parameters, one or more ECG parameters, one or more clinical biomarkers, one or more imaging data (e.g., echocardiogram, MRI), and / or one or more clinical data.

[0075] In some embodiments, the ECG data module 300 is configured to operatively communicate with an ECG device to obtain ECG data from a subject. Thus, in some embodiments, the ECG data module is configured to receive ECG data from the ECG device and optionally extract one or more parameters of the ECG data. The ECG device can be any device known in the art for obtaining ECG data. For example, in some embodiments, the ECG device includes a 12-lead ECG device, a 6-lead ECG device, a single-lead ECG device, or a dual-lead ECG device. In some embodiments, a 12-lead ECG device is found in a health setting such as a healthcare provider's office or clinic, including a hospital, a doctor's office, a medical clinic, or any other setting staffed with medical and / or health professionals (e.g., doctors, emergency medical technicians, nurses, first responders, psychologists, phlebotomists, medical physicists, nurse practitioners, surgeons, dentists, and any other obvious medical professionals as would be known to one of ordinary skill in the art). In some embodiments, the ECG device is configured to be used outside of a healthcare provider's office or clinic (as described herein). For example, the ECG device can be used by a subject at home (e.g., ambulatory monitoring). In some embodiments, the ECG device, such as a single- or dual-lead ECG device, is incorporated into a wearable device (e.g., a watch, a smartwatch) or other type of mobile device and is thus configured to obtain ECG data when the subject is at rest and / or moving. As used herein, the terms “ambulatory monitoring” or “ambulatory measurement” refer to monitoring and / or measurements obtained outside of a medical setting such as a hospital or other type of medical setting (e.g., a clinic).

[0076] In some embodiments, the ECG device (e.g., a wearable device such as a smartwatch) incorporates its own software (such as a software application or “app”) to store the raw ECG data obtained by the ECG device, which can include, but is not limited to, an ECG waveform. In some embodiments, the ECG device software operatively communicates with the ECG data module 300. In some embodiments, the ECG data module 300 includes a software application for receiving the raw ECG data.

[0077] In some embodiments, the ECG data module is configured to extract specific parameters from the ECG data for determining a cardiac performance state and / or predicting the efficacy of a therapy. In some embodiments, one or more specific ECG parameters that can be extracted from the ECG data include P-wave parameters, PR-interval parameters, QRS complexes, J points, ST segments, T waves, corrected QT intervals, U waves, or any combination thereof. In some embodiments, the ECG data module is configured to correlate one or more ECG parameters with one or more cardiac parameters for detecting a cardiac performance state.

[0078] In some embodiments, the ECG data module 300 provides one or more of the ECG parameters to the therapy risk prediction module 310 and / or the monitoring and management module 312. In some embodiments, the therapy risk prediction module 310 and / or the monitoring and management module 312 are configured to extract one or more ECG parameters from the ECG data received from the ECG data module 300. In some embodiments, the therapy risk prediction module 310 correlates one or more ECG parameters with one or more subject health parameters to determine the efficacy of the therapy. In some embodiments, the monitoring and management module 312 correlates one or more ECG parameters with one or more cardiac parameters for detecting the cardiac performance status. For example, in some embodiments, the ECG parameters are correlated with one or more of pulmonary artery pressure (PAP) (including the presence of pulmonary hypertension), pulmonary vascular resistance, right ventricular pressure (systolic and / or diastolic), right ventricular hypertrophy (e.g., right ventricular enlargement or increased muscle mass), right ventricular strain, and right ventricular size. In some embodiments, the therapy risk prediction module 310 and / or the monitoring and management module 312 use the ECG data and / or data from one or more of the clinical biomarker module 302, the imaging data module 304, and the clinical data module 306 to correlate one or more cardiac parameters related to the therapy effect and / or the cardiac performance status.

[0079] In some embodiments, the clinical biomarker module 302 is configured to obtain and optionally store data related to one or more clinical biomarkers in a subject. In some embodiments, the one or more clinical biomarkers include one or more blood-borne protein measurements, one or more blood-borne molecule measurements, urinary protein, and / or one or more other molecule measurements. Exemplary blood-borne protein measurements include B-type natriuretic peptide (BNP), N-terminal (NT)-prohormone BNP (NT-proBNP), cardiac troponin, or a combination thereof. In some embodiments, the data of the one or more clinical biomarkers may include the amount, concentration, and / or level of the one or more clinical biomarkers.

[0080] Such clinical biomarkers can be correlated with several factors of cardiac health. For example, blood-borne measurements of wall stress (NT pro BNP) and myocardial injury (troponin), as well as lipid profiles and diabetes parameters (blood glucose and HbA1c), and inflammation (hs CRP) have been well established in cardiovascular assessment and management.

[0081] In some embodiments, data of one or more clinical biomarkers are obtained via a blood sample from a subject, wherein the blood sample is further processed to identify the one or more clinical biomarkers. In some embodiments, the blood sample can be obtained by a healthcare provider (e.g., a medical and / or health professional as described herein), and / or by the subject or a non-medical or non-health professional. In some embodiments, the blood sample can be processed in a laboratory, a hospital, a medical clinic, a health center, or any combination thereof. In some embodiments, a point-of-care device can be used to process the blood sample, such that the subject can process the blood sample at a non-medical location (e.g., at home). In some embodiments, the result of the blood sample processing identifies one or more clinical biomarkers (and relative data, such as quantity, concentration, and / or level), wherein the result can be input or sent to a computing device operatively communicating with the clinical biomarker module 302, such that the clinical biomarker module 302 can receive the one or more clinical biomarkers. In some embodiments, the result of the blood processing is directly input or sent to the clinical biomarker module 302.

[0082] In some embodiments, the imaging data module 304 is configured to obtain and optionally store the obtained images related to the subject. In some embodiments, such images include MRI scans (e.g., cardiac MRI scans), and / or echocardiogram scans. In some embodiments, the images are sent from a healthcare provider (e.g., from a medical or other healthcare clinic) via a computing device operatively communicating with the imaging data module 304. In some embodiments, the subject or other non-medical professionals are configured to upload or send the images to be received by the imaging data module 304.

[0083] In some embodiments, the imaging data module 304 is configured to extract one or more echocardiogram parameters. For example, exemplary left ventricular echocardiogram parameters include outflow tract obstruction, ejection fraction, fractional shortening, mass index, maximum wall thickness, septal thickness, and / or strain. Exemplary mitral valve echocardiogram parameters include presystolic motion and / or regurgitation. Exemplary left atrial echocardiogram parameters include maximum and / or minimum diameter, volume index, ejection fraction, function, and / or strain. Exemplary right ventricular echocardiogram parameters include mass index, wall thickness, strain, right ventricular ejection fraction, and / or right ventricular systolic pressure (e.g., as measured by tricuspid regurgitation velocity Doppler).

[0084] In some embodiments, the clinical data module 306 is configured to obtain and optionally store one or more clinical parameters. Figure 3BExemplary clinical parameters are provided. For example, in some embodiments, the clinical parameters include the age, gender, weight, body mass index, height, etc. of the subject. In some embodiments, the clinical parameter (e.g., weight) is communicated to the clinical data module 306 via a smart device such as a weighing scale operatively communicating with the clinical data module 306. In some cases, weight is an important factor in cardiovascular care, both as a chronic and an acute risk metric. Long-term weight can be an indirect correlate of cardiovascular health and is often measured in routine clinical practice. In some cases, acute changes in weight are mainly related to body fluid, and weight gain can be a sign of worsening heart failure. In some cases, daily home weight measurements are employed in the management of selected patients with heart failure.

[0085] In some embodiments, the clinical parameters include physiological data such as heart rate, blood glucose, blood pressure, respiratory rate, body temperature, blood volume, oxygen saturation, etc. In some embodiments, such physiological data is obtained via a smart device such as a wearable device or other device configured to operatively communicate with the clinical data module 306.

[0086] In some embodiments, the clinical parameters include the subject's exercise outcome, the subject's activity (e.g., fitness activity or other movement), speed, sleep data, etc. In some embodiments, such clinical parameters are obtained via a smart device such as a wearable device or other device configured to operatively communicate with the clinical data module 306. For example, in some embodiments, an accelerometer is used to provide such clinical parameters.

[0087] In some embodiments, exercise testing is important in evaluating the cardiorespiratory condition. In some embodiments, the exercise test measurements include maximal exercise power (MET / Watt) and distance (e.g., six-minute walk). In some embodiments, maximal exercise testing on a treadmill or stationary bicycle is combined with ECG monitoring and is typically combined with imaging to evaluate cardiac function and / or ischemia. In some embodiments, the distance test is performed on a defined route with or without ECG monitoring.

[0088] Connected consumer wearable technology has been well established in its ability to measure multiple dynamic activity parameters including total steps, speed, and duration. Such data can be applicable to provide clinical insights similar to supervised exercise protocols, especially when integrating ECG and other dynamic data.

[0089] In some embodiments, any of such clinical parameters can be input by the subject and / or the healthcare provider.

[0090] In some embodiments, the clinical parameters include other data that can be input by the subject and / or healthcare provider or transmitted from the healthcare provider. For example, such other data can include symptoms reported by the subject, past medical history, family history, genetics, or any combination thereof.

[0091] In some embodiments, symptom assessment is an important factor in cardiovascular care. In some embodiments, symptoms are primarily recorded during patient visits, and the patient / family can notify the healthcare provider of temporary changes / lack of changes. In some cases, symptom logs are generally not used outside of a specific test protocol or clinical trial setting. As described herein, in some embodiments, the cardiac performance tool 206 is configured to receive inputs of symptoms from the subject, healthcare provider, or other individual.

[0092] Connected consumer wearable technologies are well-suited to prompting individuals to enter symptoms using both structured and open fields. These inputs can be integrated with other dynamic inputs to provide a longitudinal record of symptoms with associated clinical parameters.

[0093] Decision Engine and Decision Engine Data

[0094] In some embodiments, the decision engine module 308 applies one or more algorithms to predict the efficacy of a therapy in reducing abnormalities in cardiac performance (e.g., determining the effect of a therapy on cardiac performance abnormalities), and / or to determine the cardiac performance state. In some embodiments, such one or more algorithms utilize one or more of the subject health parameters (as described herein) to predict such efficacy and / or cardiac performance state. As described herein, such subject health parameters can be obtained via ECG data, clinical biomarkers, imaging data, and / or clinical data parameters (as described herein). In some embodiments, one or more algorithms can correspond to determining the efficacy of a specific therapy in reducing abnormalities in cardiac performance. As described herein, in some embodiments, therapies include pharmaceutical therapies such as administration of myosin inhibitors, PDE5 inhibitors, one or more beta blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, and / or one or more blood thinners. In some embodiments, one or more algorithms can correspond to determining the cardiac performance state, including detecting abnormalities in cardiac performance.

[0095] In some embodiments, one or more decision engines apply algorithms (e.g., algorithms embodied in a trained model) to associate various combinations of a subject's health parameters with a given therapy and / or a cardiac performance state. In some embodiments, at least one of the one or more algorithms may include a machine learning algorithm incorporating artificial intelligence (AI) to help improve the accuracy of the prediction of efficacy, and / or for determining a cardiac performance state. For example, with respect to determining treatment efficacy, in some embodiments, the artificial intelligence is applied to trained model data (which may be included in decision engine data) and / or data including subject health parameters from a plurality of individuals (e.g., a population of individuals) having one or more treatment effects, in order to identify correlations between the subject health parameters and treatment effects, and thereby train the model. In some embodiments, the treatment effect corresponds to a decrease in cardiac performance (e.g., a negative effect), an improvement in cardiac performance (e.g., a positive effect), or no change in cardiac performance (e.g., no effect).

[0096] In some embodiments, any of the decision engines described herein is any one of a regression model (e.g., linear regression, logistic regression, or polynomial regression), a decision tree, a random forest, a gradient boosting machine learning model, a support vector machine, a naive Bayes model, k-means clustering, or a neural network (e.g., a feedforward network, a convolutional neural network (CNN), a deep neural network (DNN), an autoencoder neural network, a generative adversarial network, or a recurrent network (e.g., a long short-term memory network (LSTM), a bidirectional recurrent network, a deep bidirectional recurrent network)) or any combination thereof. In a particular embodiment, any of the decision engines described herein is a logistic regression model. In a particular embodiment, any of the decision engines described herein is a random forest classifier. In a particular embodiment, any of the decision engines described herein is a gradient boosting model.

[0097] In some embodiments, any of the decision engines described herein (e.g., a trained model) can be trained using machine learning-implemented methods, which can be any one of, for example, linear regression algorithms, logistic regression algorithms, decision tree algorithms, support vector machine classification, naive Bayes classification, K-nearest neighbor classification, random forest algorithms, deep learning algorithms, gradient boosting algorithms, and dimensionality reduction techniques such as manifold learning, principal component analysis, factor analysis, autoencoder regularization, and independent component analysis, or combinations thereof. In a particular embodiment, the machine learning-implemented method is a logistic regression algorithm. In a particular embodiment, the machine learning-implemented method is a random forest algorithm. In a particular embodiment, the machine learning-implemented method is a gradient boosting algorithm, such as XGboost. In some embodiments, any of the trained models described herein are trained using supervised learning algorithms, unsupervised learning algorithms, semi-supervised learning algorithms (e.g., partial supervision), weak supervision, transfer, multi-task learning, or any combination thereof.

[0098] In some embodiments, any of the trained models described herein has one or more parameters, such as hyperparameters or model parameters. Hyperparameters are typically established before training. Examples of hyperparameters include learning rate, depth or leaves of a decision tree, number of hidden layers in a deep neural network, number of clusters in k-means clustering, penalty in a regression model, and regularization parameters associated with a cost function. Model parameters are typically adjusted during training. Examples of model parameters include weights associated with nodes in a neural network layer, support vectors in a support vector machine, node values in a decision tree, and coefficients in a regression model. The model parameters of the risk prediction model are trained (e.g., adjusted) using the trained data to improve the prediction ability of the risk prediction model.

[0099] In some embodiments, any of the trained models described herein are trained via the trained data located in the trained model data (which can be included in the decision engine module 318). In some embodiments, such trained model data includes subject health parameters obtained from a plurality of individuals, as well as the corresponding therapies administered to the individuals and the effects on cardiac performance abnormalities. In some embodiments, such trained model data includes subject health parameters from a plurality of individuals, such as one or more of the cardiac parameters described herein, and the corresponding cardiac performance states.

[0100] In various embodiments, the training data used to train any of the trained models described herein includes reference ground truths that indicate whether a therapy is associated with a negative effect, a positive effect, or no effect on cardiac performance abnormalities (also referred to hereinafter as "negative" or "-", "positive" or "+", or "neutral"). In various embodiments, the reference ground truths in the training data are binary values, such as "1" or "0". For example, a therapy diagnosed as having a positive effect on cardiac performance abnormalities may be identified in the training data using the value "1", while a therapy diagnosed as having a negative effect on cardiac performance abnormalities may be identified in the training data using the value "0". In various embodiments, any of the trained models described herein is trained using the training data to minimize a loss function, such that any of the trained models described herein can better predict an outcome (e.g., a future diagnosis of a cardiac condition) based on an input (e.g., extracted features of a subject's health parameters). In some embodiments, the loss function is constructed for any of least absolute shrinkage and selection operator (LASSO) regression, ridge regression, or elastic net regression. In some embodiments, any of the trained models described herein is a random forest model and is trained to minimize one of the Gini impurity or entropy measures for feature typing, such that any of the trained models described herein can more accurately predict the effect of a therapy on cardiac performance abnormalities.

[0101] In various embodiments, the training data can be obtained and / or derived from publicly available databases. In some embodiments, the training data can be obtained and collected independently of publicly available databases. Such training data can be a custom dataset. As described herein, in some embodiments, the training data includes subject health parameters (as described herein) from multiple individuals (e.g., a cohort of individuals), wherein the efficacy of at least one therapy on cardiac performance abnormalities is identified for each individual based on the corresponding subject health parameters of the individual.

[0102] In some embodiments, the correlations associating a subject's health parameters with one or more therapies, such as obtained via system 200, are stored in the decision engine data storage 318. In some embodiments, the decision engine data storage includes ranges and / or combinations associating a subject's health data (e.g., cardiac parameters, ECG data, imaging, clinical biomarkers, subject health parameters) with the efficacy of specific therapies for cardiac performance abnormalities and / or cardiac performance states. For example, in some cases, certain subject health parameters associate a therapy with a negative effect on cardiac performance such that a decision engine that combines the subject health parameters received from the subject with the stored correlations provides a risk metric related to the likelihood and / or severity of the occurrence of the negative effect. In some embodiments, the decision engine data storage 312 is updated via communication with an external database and / or based on subject health parameters received from a subject.

[0103] Therapy Risk Prediction

[0104] In some embodiments, as described herein, the therapy risk prediction module 310 is configured to predict the efficacy of a therapy for cardiac performance abnormalities. In some embodiments, the therapy risk prediction module 310 is configured to apply one or more decision engines with one or more of the subject's health parameters (as described herein) to identify the therapy as i) having a negative effect on cardiac performance abnormalities and optionally a risk level associated with the likelihood and / or severity of deterioration of the cardiac performance abnormalities, ii) having no effect on cardiac performance abnormalities, or iii) having a positive effect on cardiac performance abnormalities. As described herein, in some embodiments, one or more decision engines associate subject health parameter data from multiple individuals with the corresponding effects of therapies on cardiac performance abnormalities to generate the correlations.

[0105] In some embodiments, the therapy risk prediction module 310 is configured to communicate the determined efficacy of the therapy to the subject and / or a healthcare provider (as described herein) to assist in the decision of whether to administer the therapy to the subject.

[0106] Monitoring and Management of Heart Health Status

[0107] In some embodiments, the monitoring and management (MM) module 312 is configured to monitor the cardiac performance state of a subject via periodic measurement and input of health parameters. In some embodiments, such monitoring occurs at step 504 and / or 510 of Figure 1 For example, in some embodiments, the MM module 312 in communication with a device configured to provide subject health parameters, such as an ECG device or an accelerometer, is configured to obtain data from such device and apply one or more decision engines (as described herein) to determine the cardiac performance state.

[0108] In some embodiments, the MM module 312 first establishes a baseline assessment of the subject's cardiac performance state based on the health parameters initially received by the system 200. In some embodiments, the baseline assessment establishes a benchmark for future monitoring of the subject's cardiac performance state (e.g., Figure 1 step 510), thereby showing regression or progression of the cardiac performance state (e.g., an abnormality that is worsening).

[0109] In some embodiments, as described herein, the MM module 312 is configured to monitor the subject's cardiac health state by periodically retrieving some of the health parameters (as described herein) at a prescribed frequency, in order to identify changes in the subject's cardiac performance state (e.g., changes in the severity of an abnormality) compared to the baseline assessment. In some embodiments, the prescribed frequency includes any temporal frequency prescribed by the subject, a healthcare professional, or other individual. For example, in some embodiments, the prescribed frequency includes obtaining one or more health parameters daily, every 2, 3, 4, 5, or 6 days, weekly, bi-weekly, monthly, every 4 to 20 weeks, etc. In some embodiments, the subject is provided with a reminder to enter a health clinic (e.g., a hospital, a medical clinic, etc. to obtain one or more subject health parameters such as an echocardiogram, a blood draw, etc.).

[0110] In some embodiments, the periodic monitoring performed by the MM module 312 enables a reduction in the risk of the subject developing systolic dysfunction and / or reduced cardiac output, as described herein.

[0111] Intervention Recommendations

[0112] In some embodiments, the intervention module 314 is configured to recommend an adjustment to an existing therapy, as described herein (e.g., see Figure 1 step 514). In some embodiments, the intervention module 314 is configured to obtain the cardiac performance state determined by the MM module 312 and determine a change from a previous cardiac performance state. In some embodiments, the MM module 312 is configured to automatically recommend a change in the administered therapy based on a negative effect on the cardiac performance state (e.g., an abnormality that is worsening in cardiac performance), or based on observing no positive effect on the cardiac performance state (e.g., the cardiac performance abnormality has not improved). In some embodiments, the intervention module 314 warns the subject and / or the healthcare provider (as described herein) about the change in the cardiac performance state and is configured to receive recommendations for therapy adjustment. In some embodiments, the intervention module 314 communicates the recommendation to the therapy risk prediction module to determine the efficacy of the recommended therapy, in order to warn the subject and / or the healthcare provider whether such therapy should be administered.

[0113] In some embodiments, adjusting the therapy includes maintaining the same therapy but changing one or more of the therapy dosage and frequency. For example, in the case of providing a myosin inhibitor, in some embodiments, adjusting the therapy includes providing a reduced amount of the myosin inhibitor and / or decreasing the frequency at which the myosin inhibitor is provided.

[0114] In some embodiments, adjusting the therapy includes changing one or more of the therapy dosage and frequency and / or providing one or more additional therapies. For example, in the case of providing a myosin inhibitor and finding that it reduces the contractility of the right ventricle while the PAP remains elevated, it is recommended to administer a PDE5 inhibitor or other therapy to the subject to reduce pulmonary vascular resistance.

[0115] In some embodiments, adjusting the therapy includes stopping the provision of an existing therapy and recommending the provision of one or more additional therapies, or alternatively, not providing any therapy (for a specified duration or completely stopping).

[0116] In some embodiments, providing one or more additional therapies, with or without an existing therapy (from Figure 1 step 506), includes first predicting the effect of the therapy before recommending or administering it to the subject (via Figure 1 step 508). In some embodiments, in the case of detecting a negative effect on cardiac performance, or in some cases, no positive effect is identified, the method restarts after step 504.

[0117] Communication

[0118] In some embodiments, the communication module 316 is configured to communicate with a healthcare provider, send warnings and / or cardiac health status, and / or relay recommendations, data (such as additional health parameters, including clinical biomarkers and / or images), and messages to the subject or the cardiac performance tool 206 (and associated modules).

[0119] In some embodiments, as described herein, the cardiac performance tool 206 is configured to display the subject's health parameters, cardiac performance status, and the efficacy of the therapy using a display interface (e.g., a monitor, a screen, etc.).

[0120] IV. Computer Implementation Scheme

[0121] In some embodiments, the methods described herein are executed on one or more computers, including methods of implementing one or more decision engines for determining the cardiac performance status and / or the efficacy of a therapy for cardiac performance abnormalities.

[0122] For example, the construction and deployment of any of the methods described herein can be implemented in hardware or software or a combination of both. In one embodiment, a machine-readable storage medium is provided, the medium including data storage material encoded with machine-readable data that, when used by a machine programmed with instructions, is capable of performing any of the methods described herein and / or displaying any of the data sets or results described herein (e.g., cardiac performance status, risk prediction). Some embodiments can be implemented in a computer program executable on a programmable computer, including a processor and a data storage system (including volatile and non-volatile memory and / or storage elements), and optionally including a graphics adapter, a pointing device, a network adapter, at least one input device, and / or at least one output device. A display can be coupled to the graphics adapter. Program code is applied to input data to perform the above functions and generate output information. The output information is applied to one or more output devices in a known manner. For example, the computer can be a personal computer, a microcomputer, or a conventionally designed workstation.

[0123] Each program can be implemented in a high-level programming or object-oriented programming language to communicate with the computer system. However, if desired, the program can be implemented in assembly or machine language. In any case, the language can be a compiled or interpreted language. Each such computer program is preferably stored on a general-purpose or special-purpose programmable computer-readable storage medium or device (e.g., ROM or disk) for configuring and operating the computer when the computer reads the storage medium or device to execute the program described herein. The system can also be considered to be implemented as a computer-readable storage medium configured with a computer program, where the storage medium so configured causes the computer to operate in a specific and predefined manner to perform the functions described herein.

[0124] The signature pattern and its database can be provided in a variety of media to facilitate its use. "Media" refers to an article containing the signature pattern information of the embodiment. The database of some embodiments can be recorded on a computer-readable medium, such as any medium directly readable and accessible by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy disks, hard disk storage media, and magnetic tapes; optical storage media, such as CD-ROMs; electrical storage media, such as RAM and ROM; and hybrids of these categories, such as magnetic / optical storage media. Those skilled in the art can readily understand how to use any of the currently known computer-readable media to create an article containing a record of the current database information. "Recording" refers to the process of storing information on a computer-readable medium using any such method known in the art. Any convenient data storage structure can be selected based on the means for accessing the stored information. Various data processor programs and formats can be used for storage, such as word processing text files, database formats, etc.

[0125] In some embodiments, the methods described herein, including methods for determining a cardiac health state, are executed on one or more computers in a distributed computing system environment (e.g., in a cloud computing environment). In this specification, "cloud computing" is defined as a model for enabling on-demand network access to a shared pool of configurable computing resources. Cloud computing can be used to provide on-demand access to a shared pool of configurable computing resources. The shared pool of configurable computing resources can be provisioned rapidly via virtualization and released with low management effort or service provider interaction, and then scaled accordingly. The cloud computing model can consist of various characteristics such as on-demand self-service, broad network access, resource pooling, rapid elasticity, measured service, etc. The cloud computing model can also exhibit various service models such as software as a service ("SaaS"), platform as a service ("PaaS"), and infrastructure as a service ("IaaS"). The cloud computing model can also be deployed using different deployment models such as private cloud, community cloud, public cloud, hybrid cloud, etc. In this specification and the claims, a "cloud computing environment" is an environment that employs cloud computing.

[0126] Figure 4 Illustrative computer for implementing Figure 1 Example computer 400 shown in FIG. -3. Computer 400 includes at least one processor 402 coupled to a chipset 404. Chipset 404 includes a memory controller hub 420 and an input / output (I / O) controller hub 422. Memory 406 and a graphics adapter 412 are coupled to the memory controller hub 420, and a display 418 is coupled to the graphics adapter 412. A storage device 408, an input device 414, and a network adapter 416 are coupled to the I / O controller hub 422. Other embodiments of computer 400 have different architectures.

[0127] Storage device 408 is a non-transitory computer-readable storage medium such as a hard disk drive, a compact disc read-only memory (CD-ROM), a DVD, or a solid-state memory device. Memory 406 holds instructions and data used by processor 402. Input interface 414 is a touchscreen interface, a mouse, a trackball, or other type of pointing device, a keyboard, or some combination thereof, and is used to input data into computer 400. In some embodiments, computer 400 can be configured to receive input (e.g., commands) from input interface 414 via gestures from a user. Network adapter 416 couples computer 400 to one or more computer networks.

[0128] The graphics adapter 412 displays images and other information on the display 418. In various embodiments, the display 418 is configured such that a user (e.g., a subject, a healthcare professional, a non-healthcare professional) can enter a user selection on the display 418 to, for example, initiate a system for determining a cardiac health status. In one embodiment, the display 418 may include a touch interface. In various embodiments, the display 418 may show cardiac performance status, predicted therapy efficacy, and warnings associated with cardiac performance abnormalities.

[0129] The computer 400 is adapted to execute computer program modules for providing the functions described herein. As used herein, the term "module" refers to computer program logic for providing a specified function. Thus, a module can be implemented in hardware, firmware, and / or software. In one embodiment, the program modules are stored on the storage device 408, loaded into the memory 406, and executed by the processor 402.

[0130] by Figure 1 The type of computer 400 used by an entity - 3 can vary according to the embodiment and the processing capabilities required by the entity. For example, the cardiac performance tool 206 can run on a single computer 400 or on multiple computers 400 that communicate with each other via a network such as a server farm. The computer 400 may lack some of the above components, such as the graphics adapter 412 and the display 418.

[0131] V. System

[0132] Systems for implementing one or more decision engines for determining a cardiac performance status and / or determining the efficacy of a therapy for a cardiac performance abnormality are further disclosed herein. In various embodiments, such systems can include at least the cardiac performance tool 206 described above in Figure 2 In some embodiments, the cardiac performance tool 206 is embodied as a computer system, such as a computer system having the Figure 4 example computer 400 described in

[0133] In some embodiments, the system includes one or more auxiliary devices, such as an ECG device, a dynamic monitoring device, an imaging device, an accelerometer, a weight scale, etc., or any combination thereof, as described herein. In some embodiments, the system includes both the cardiac health tool 206 (e.g., a computer system) and one or more of the auxiliary devices. In such embodiments, the cardiac performance tool 206 can be communicatively coupled to any combination of the auxiliary devices to receive data therefrom.

[0134] All publications, patents, patent applications, and other documents cited in this application are hereby incorporated by reference in their entirety for all purposes to the same extent as if each individual publication, patent, patent application, or other document were specifically and individually indicated to be incorporated by reference for all purposes.

[0135] VI. Numbered Embodiments

[0136] Example 1: A method for treating hypertrophic cardiomyopathy in a subject, the method comprising: detecting an abnormality in the cardiac performance of the subject, wherein the abnormality corresponds to one or more of abnormal pulmonary artery pressure, pulmonary hypertension, abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular strain, and right ventricular size; optionally predicting the efficacy of a first therapy for reducing the abnormality; optionally administering to the subject the first therapy or a second therapy based on the predicted efficacy of the first therapy; and monitoring the abnormality and optionally detecting negative effects after administering the first therapy or the second therapy to the subject.

[0137] Example 2: The method according to Example 1, wherein the first and / or second therapy comprises one or more types of therapies, the one or more types of therapies comprising myosin inhibition, therapies for reducing pulmonary vascular resistance, one or more beta blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, and / or one or more blood thinners or any combination thereof.

[0138] Example 3: The method according to Example 1 or 2, wherein the negative effect comprises an abnormal increase in the cardiac performance from (a).

[0139] Example 4: The method according to any one of Examples 1 to 3, wherein predicting the efficacy of the first therapy comprises: obtaining one or more health parameters of the subject; and applying the one or more health parameters to one or more correlations associated with the first therapy.

[0140] Example 5: The method according to Example 4, wherein the one or more correlations are based on: one or more health parameters received from a group of individuals; and for each individual in the group of individuals, i) a negative effect on the corresponding cardiac performance due to the first therapy being administered, ii) a positive outcome on the corresponding cardiac performance due to the first therapy being administered, or iii) no effect on the corresponding cardiac performance due to the first therapy being administered, such that when combined with the administration of the first therapy, at least one of the one or more health parameters (from the corresponding individual in the group of individuals) is associated with an effect on cardiac performance.

[0141] Example 6: The method according to Example 4 or 5, wherein the one or more correlations of the application data include using a machine learning algorithm.

[0142] Example 7: The method according to any one of Examples 1 to 6, wherein detecting the abnormality and / or monitoring the abnormality includes using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

[0143] Example 8: The method according to Example 7, wherein detecting the abnormality and / or monitoring the abnormality is performed in a medical environment, a dynamic environment, or both.

[0144] Example 9: The method according to Example 7 or 8, wherein detecting the abnormality and / or monitoring the abnormality includes obtaining an ECG, wherein the ECG includes a single-lead ECG, a 2-lead ECG, a 6-lead ECG, or a 12-lead ECG.

[0145] Example 10: The method according to any one of Examples 1 to 9, further comprising adjusting the first or second therapy based on detecting a corresponding negative effect or a corresponding lack of effect on the abnormality of the cardiac performance.

[0146] Example 11: The method according to Example 10, wherein adjusting the first or second therapy includes administering a third therapy and / or reducing the amount of the first or second therapy administered.

[0147] Example 12: The method according to Example 11, wherein reducing the amount of the first or second therapy administered includes reducing the frequency of the first or second therapy administered and / or reducing the dose of the first or second therapy administered.

[0148] Example 13: The method according to any one of Examples 1 to 12, further comprising administering an initial HCM treatment to the subject before detecting the cardiac performance abnormality.

[0149] Example 14: The method according to Example 13, wherein the initial HCM treatment includes administering a myosin inhibitor to the subject.

[0150] Example 15: The method according to any one of Examples 1 to 14, wherein the hypertrophic cardiomyopathy includes obstructive hypertrophic cardiomyopathy (oHCM).

[0151] Example 16: The method according to Example 15, further comprising at least partially removing the obstruction associated with the oHCM before detecting the cardiac performance abnormality.

[0152] Example 17: The method according to Example 16, wherein said at least partial removal of the obstruction comprises performing a septal myectomy.

[0153] Example 18: A non-transitory computer-readable medium for treating hypertrophic cardiomyopathy in a subject, the non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations comprising: detecting an abnormality in the cardiac performance of the subject, wherein the abnormality corresponds to one or more of an abnormal pulmonary artery pressure, pulmonary hypertension, abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular strain, and right ventricular size; optionally predicting the efficacy of a first therapy for reducing the abnormality; optionally determining a treatment comprising administration of the first therapy or a second therapy to the subject based on the predicted efficacy of the first therapy; and monitoring the abnormality and optionally detecting a negative effect after administration of the first therapy or the second therapy to the subject.

[0154] Example 19: The non-transitory computer-readable medium according to Example 18, wherein the first and / or second therapy comprises one or more types of therapy, the one or more types of therapy comprising myosin inhibition, a therapy for reducing pulmonary vascular resistance, one or more beta blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, and / or one or more blood thinners or any combination thereof.

[0155] Example 20: The non-transitory computer-readable medium according to Example 18 or 19, wherein the negative effect comprises an abnormal increase in the cardiac performance from (a).

[0156] Example 21: The non-transitory computer-readable medium according to any one of Examples 18 to 20, wherein predicting the efficacy of the first therapy comprises: obtaining one or more health parameters of the subject; and applying the one or more health parameters to one or more correlations associated with the first therapy.

[0157] Example 22: The non-transitory computer-readable medium according to Example 21, wherein the one or more correlations are based on: one or more health parameters received from a population of individuals; and for each individual in the population of individuals, i) a negative effect on the corresponding cardiac performance due to the first therapy being administered, ii) a positive outcome on the corresponding cardiac performance due to the first therapy being administered, or iii) no effect on the corresponding cardiac performance due to the first therapy being administered, such that at least one of the one or more health parameters (from the corresponding individual in the population of individuals) is associated with an effect on cardiac performance when combined with the administration of the first therapy.

[0158] Example 23: The non-transitory computer-readable medium according to Example 21 or 22, wherein one or more of the correlations of the application data include the use of a machine learning algorithm.

[0159] Example 24: The non-transitory computer-readable medium according to any one of Examples 18 to 23, wherein detecting the anomaly and / or monitoring the anomaly includes obtaining one or more cardiac parameters of the subject using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

[0160] Example 25: The non-transitory computer-readable medium according to Example 24, wherein detecting the anomaly and / or monitoring the anomaly is performed in a medical environment, a dynamic environment, or both.

[0161] Example 26: The non-transitory computer-readable medium according to Example 24 or 25, wherein detecting the anomaly and / or monitoring the anomaly includes obtaining an ECG, wherein the ECG includes a single-lead ECG, a 2-lead ECG, a 6-lead ECG, or a 12-lead ECG.

[0162] Example 27: The non-transitory computer-readable medium according to any one of Examples 18 to 26, further comprising determining an adjustment of the first or second therapy based on detecting a corresponding negative effect or a corresponding lack of effect on the cardiac performance due to the anomaly.

[0163] Example 28: The non-transitory computer-readable medium according to Example 27, wherein the adjustment of the first or second therapy includes determining the administration of a third therapy and / or reducing the amount of the first or second therapy administered.

[0164] Example 29: The non-transitory computer-readable medium according to Example 28, wherein reducing the amount of the first or second therapy administered includes reducing the frequency of the first or second therapy administered and / or reducing the dose of the first or second therapy administered.

[0165] Example 30: The non-transitory computer-readable medium according to any one of Examples 18 to 29, wherein an initial HCM treatment is administered to the subject before detecting the cardiac performance anomaly.

[0166] Example 31: The non-transitory computer-readable medium according to Example 30, wherein the initial HCM treatment includes administering a myosin inhibitor to the subject.

[0167] Example 32: The non - transitory computer - readable medium according to any one of Examples 18 to 31, wherein the hypertrophic cardiomyopathy includes obstructive hypertrophic cardiomyopathy (oHCM).

[0168] Example 33: The non - transitory computer - readable medium according to Example 32, wherein at least part of the obstruction associated with the oHCM is removed before detecting the abnormal cardiac performance.

[0169] Example 34: The non - transitory computer - readable medium according to Example 33, wherein the obstruction is at least partially removed via septal myectomy.

[0170] Example 35: A system for treating hypertrophic cardiomyopathy in a subject, the system comprising: one or more processors; and one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform operations including: detecting an abnormality in the cardiac performance of the subject, wherein the abnormality corresponds to one or more of abnormal pulmonary artery pressure, pulmonary hypertension, abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular strain, and right ventricular size; optionally predicting the efficacy of a first therapy for reducing the abnormality; optionally determining a treatment comprising the administration of the first therapy or a second therapy to the subject based on the predicted efficacy of the first therapy; and monitoring the abnormality and optionally detecting negative effects after administering the first therapy or the second therapy to the subject.

[0171] Example 36: The system according to Example 35, wherein the first and / or second therapy comprises one or more types of therapies, the one or more types of therapies including myosin inhibition, therapies for reducing pulmonary vascular resistance, one or more beta - blockers, one or more calcium channel blockers, one or more anti - arrhythmic drugs, and / or one or more blood thinners or any combination thereof.

[0172] Example 37: The system according to Example 35 or 36, wherein the negative effect comprises an abnormal increase in the cardiac performance from (a).

[0173] Example 38: The system according to any one of Examples 35 to 37, wherein predicting the efficacy of the first therapy comprises: obtaining one or more health parameters of the subject; and applying the one or more health parameters to one or more correlations associated with the first therapy.

[0174] Example 39: The system according to Example 38, wherein the one or more correlations are based on: one or more health parameters received from a group of individuals; and for each individual in the group of individuals, i) a negative effect on the corresponding cardiac performance due to the first therapy being administered, ii) a positive outcome on the corresponding cardiac performance due to the first therapy being administered, or iii) no effect on the corresponding cardiac performance due to the first therapy being administered, such that when combined with the administration of the first therapy, at least one of the one or more health parameters (from the corresponding individual in the group of individuals) is associated with an effect on cardiac performance.

[0175] Example 40: The system according to Example 38 or 39, wherein the one or more correlations of the application data include the use of a machine learning algorithm.

[0176] Example 41: The system according to any one of Examples 35 to 40, wherein detecting the abnormality and / or monitoring the abnormality includes obtaining one or more cardiac parameters of the subject using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

[0177] Example 42: The system according to Example 41, wherein detecting the abnormality and / or monitoring the abnormality is performed in a medical environment, a dynamic environment, or both.

[0178] Example 43: The system according to Example 41 or 42, wherein detecting the abnormality and / or monitoring the abnormality includes obtaining an ECG, wherein the ECG includes a single-lead ECG, a 2-lead ECG, a 6-lead ECG, or a 12-lead ECG.

[0179] Example 44: The system according to any one of Examples 35 to 43, further comprising determining an adjustment of the first or second therapy based on detecting a corresponding negative effect or corresponding no effect of the abnormality on the cardiac performance.

[0180] Example 45: The system according to Example 44, wherein the adjustment of the first or second therapy includes determining the administration of a third therapy and / or reducing the amount of the first or second therapy being administered.

[0181] Example 46: The system according to Example 45, wherein reducing the amount of the first or second therapy being administered includes reducing the frequency of the first or second therapy being administered and / or reducing the dose of the first or second therapy being administered.

[0182] Example 47: The system according to any one of Examples 35 to 46, wherein an initial HCM treatment is administered to the subject before detecting the cardiac performance abnormality.

[0183] Example 48: The system according to Example 47, wherein the initial HCM treatment comprises administering a myosin inhibitor to the subject.

[0184] Example 49: The system according to any one of Examples 35 to 48, wherein the hypertrophic cardiomyopathy comprises obstructive hypertrophic cardiomyopathy (oHCM).

[0185] Example 50: The system according to Example 49, wherein at least part of the obstruction associated with the oHCM is removed before detecting the cardiac performance abnormality.

[0186] Example 51: The system according to Example 50, wherein at least part of the obstruction is removed via septal myectomy.

[0187] Although various specific embodiments have been illustrated and described, the above specification is not restrictive. It should be understood that various changes can be made without departing from the spirit and scope of the present disclosure. After reading this specification, many variations will be obvious to those skilled in the art.

Claims

1. A method for treating a subject with hypertrophic cardiomyopathy, the method comprising: Detect an abnormality in the cardiac performance of the subject, wherein the abnormality corresponds to one or more of abnormal pulmonary artery pressure, pulmonary hypertension, abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular strain, and right ventricular size; Optionally, predict the efficacy of a first therapy for reducing the abnormality; Optionally, administer the first therapy or a second therapy to the subject based on the predicted efficacy of the first therapy; And Monitor the abnormality and optionally detect negative effects after administering the first therapy or the second therapy to the subject.

2. The method according to claim 1, wherein the first and / or second therapy comprises one or more types of therapy, the one or more types of therapy comprising myosin inhibition, therapy for reducing pulmonary vascular resistance, one or more beta blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, and / or one or more blood thinners or any combination thereof.

3. The method according to claim 1 or 2, wherein the negative effect comprises the abnormal increase in cardiac performance from (a).

4. The method according to any one of claims 1 to 3, wherein predicting the efficacy of the first therapy comprises: Obtain one or more health parameters of the subject; And apply the one or more health parameters to one or more correlations associated with the first therapy.

5. The method according to claim 4, wherein the one or more correlations are based on: one or more health parameters received from a group of individuals, and for each individual in the group of individuals, i) the negative effect on the corresponding cardiac performance due to the first therapy being administered, ii) the positive outcome on the corresponding cardiac performance due to the first therapy being administered, or iii) no effect on the corresponding cardiac performance due to the first therapy being administered, such that at least one of the one or more health parameters from the individuals in the group of individuals is associated with the effect on cardiac performance when combined with the administration of the first therapy.

6. The method according to claim 4 or 5, wherein the one or more correlations of the applied data comprise using a machine learning algorithm.

7. The method according to any one of claims 1 to 6, wherein detecting the abnormality and / or monitoring the abnormality comprises using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

8. The method according to claim 7, wherein detecting the abnormality and / or monitoring the abnormality is performed in a medical environment, a dynamic environment, or both.

9. The method according to claim 7 or 8, wherein detecting the abnormality and / or monitoring the abnormality comprises obtaining an ECG, wherein the ECG comprises a single-lead ECG, a 2-lead ECG, a 6-lead ECG, or a 12-lead ECG.

10. The method according to any one of claims 1 to 9, further comprising adjusting the first or second therapy based on detecting a corresponding negative effect or a corresponding lack of effect of the detected abnormality on the cardiac performance.

11. The method according to claim 10, wherein adjusting the first or second therapy comprises administering a third therapy, and / or reducing the amount of the first or second therapy administered.

12. The method according to claim 11, wherein reducing the amount of the first or second therapy administered comprises reducing the frequency of the first or second therapy administered, and / or reducing the dose of the first or second therapy administered.

13. The method according to any one of claims 1 to 12, wherein prior to detecting the cardiac performance abnormality, further comprising administering an initial HCM treatment to the subject.

14. The method according to claim 13, wherein the initial HCM treatment comprises administering a myosin inhibitor to the subject.

15. The method according to any one of claims 1 to 14, wherein the hypertrophic cardiomyopathy comprises obstructive hypertrophic cardiomyopathy (oHCM).

16. The method according to claim 15, wherein prior to detecting the cardiac performance abnormality, further comprising at least partially removing the obstruction associated with the oHCM.

17. The method according to claim 16, wherein the at least partially removing the obstruction comprises performing a septal myectomy.

18. A non-transitory computer-readable medium for treating hypertrophic cardiomyopathy in a subject, the non-transitory computer-readable medium comprising instructions that, when executed by a processor, cause the processor to perform operations comprising: Detecting an abnormality in the cardiac performance of the subject, wherein the abnormality corresponds to one or more of abnormal pulmonary artery pressure, pulmonary hypertension, abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular strain, and right ventricular size; Optionally predicting the efficacy of a first therapy for reducing the abnormality; Optionally, determine a treatment comprising the administration of the first therapy or the second therapy to the subject based on the predicted efficacy of the first therapy; And Monitor the abnormality and optionally detect negative effects after administering the first therapy or the second therapy to the subject.

19. The non-transitory computer-readable medium according to claim 18, wherein the first and / or second therapy comprises one or more types of therapy, the one or more types of therapy comprising myosin inhibition, therapy for reducing pulmonary vascular resistance, one or more beta blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, and / or one or more blood thinners or any combination thereof.

20. The non-transitory computer-readable medium according to claim 18 or 19, wherein the negative effect includes an abnormal increase in the cardiac performance from (a).

21. The non-transitory computer-readable medium according to any one of claims 18 to 20, wherein predicting the efficacy of the first therapy includes: Obtain one or more health parameters of the subject; And apply the one or more health parameters to one or more correlations associated with the first therapy.

22. The non-transitory computer-readable medium according to claim 21, wherein the one or more correlations are based on: one or more health parameters received from a group of individuals; and for each individual in the group of individuals, i) a negative effect on the corresponding cardiac performance due to the first therapy being administered, ii) a positive outcome on the corresponding cardiac performance due to the first therapy being administered, or iii) no effect on the corresponding cardiac performance due to the first therapy being administered, such that when combined with the administration of the first therapy, at least one of the one or more health parameters from an individual in the group of individuals is associated with an effect on cardiac performance.

23. The non-transitory computer-readable medium according to claim 21 or 22, wherein applying the one or more correlations of the data includes using a machine learning algorithm.

24. The non-transitory computer-readable medium according to any one of claims 18 to 23, wherein detecting the abnormality and / or monitoring the abnormality includes obtaining one or more cardiac parameters of the subject using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

25. The non-transitory computer-readable medium according to claim 24, wherein detecting the abnormality and / or monitoring the abnormality is performed in a medical environment, a dynamic environment, or both.

26. The non-transitory computer-readable medium according to claim 24 or 25, wherein detecting the abnormality and / or monitoring the abnormality includes obtaining an ECG, wherein the ECG includes a single-lead ECG, a 2-lead ECG, a 6-lead ECG, or a 12-lead ECG.

27. The non-transitory computer-readable medium according to any one of claims 18 to 26, further comprising determining an adjustment of the first or second therapy based on detecting a corresponding negative effect or corresponding no effect on the cardiac performance of the abnormality.

28. The non-transitory computer-readable medium according to claim 27, wherein the adjustment of the first or second therapy includes determining the administration of a third therapy and / or reducing the amount of the first or second therapy administered.

29. The non-transitory computer-readable medium according to claim 28, wherein reducing the amount of the first or second therapy administered comprises reducing the frequency of the first or second therapy administered, and / or reducing the dosage of the first or second therapy administered.

30. The non-transitory computer-readable medium according to any one of claims 18 to 29, wherein an initial HCM treatment is administered to the subject before detecting the cardiac performance abnormality.

31. The non-transitory computer-readable medium according to claim 30, wherein the initial HCM treatment comprises administering a myosin inhibitor to the subject.

32. The non-transitory computer-readable medium according to any one of claims 18 to 31, wherein the hypertrophic cardiomyopathy comprises obstructive hypertrophic cardiomyopathy (oHCM).

33. The non-transitory computer-readable medium according to claim 32, wherein before detecting the cardiac performance abnormality, at least part of the obstruction associated with the oHCM is removed.

34. The non-transitory computer-readable medium according to claim 33, wherein at least part of the obstruction is removed via septal myectomy.

35. A system for treating hypertrophic cardiomyopathy in a subject, the system comprising: One or more processors; And one or more memories storing instructions that, when executed by the one or more processors, cause the system to perform operations comprising: Detect an abnormality in the cardiac performance of the subject, wherein the abnormality corresponds to one or more of abnormal pulmonary artery pressure, pulmonary hypertension, abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular strain, and right ventricular size; Optionally, predict the efficacy of a first therapy for reducing the abnormality; Optionally, determine a treatment comprising the administration of the first therapy or a second therapy to the subject based on the predicted efficacy of the first therapy; And Monitor the abnormality and optionally detect negative effects after administering the first therapy or the second therapy to the subject.

36. The system according to claim 35, wherein the first and / or second therapy comprises one or more types of therapy, the one or more types of therapy comprising myosin inhibition, therapy for reducing pulmonary vascular resistance, one or more beta blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, and / or one or more blood thinners or any combination thereof.

37. The system according to claim 35 or 36, wherein the negative effect comprises an abnormal increase in the cardiac performance from (a).

38. The system according to any one of claims 35 to 37, wherein predicting the efficacy of the first therapy comprises: Obtain one or more health parameters of the subject; And apply the one or more health parameters to one or more correlations associated with the first therapy.

39. The system according to claim 38, wherein the one or more correlations are based on: one or more health parameters received from a group of individuals; and for each individual in the group of individuals, i) a negative effect on the corresponding cardiac performance due to the first therapy being administered, ii) a positive outcome on the corresponding cardiac performance due to the first therapy being administered, or iii) no effect on the corresponding cardiac performance due to the first therapy being administered, such that at least one of the one or more health parameters from an individual in the group of individuals is associated with an effect on cardiac performance when combined with the administration of the first therapy.

40. The system according to claim 38 or 39, wherein the one or more correlations of the application data include using a machine learning algorithm.

41. The system according to any one of claims 35 to 40, wherein detecting the abnormality and / or monitoring the abnormality includes obtaining one or more cardiac parameters of the subject using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

42. The system according to claim 41, wherein detecting the abnormality and / or monitoring the abnormality is performed in a medical environment, a dynamic environment, or both.

43. The system according to claim 41 or 42, wherein detecting the abnormality and / or monitoring the abnormality includes obtaining an ECG, wherein the ECG includes a single-lead ECG, a 2-lead ECG, a 6-lead ECG, or a 12-lead ECG.

44. The system according to any one of claims 35 to 43, further comprising determining an adjustment of the first or second therapy based on detecting a corresponding negative effect or corresponding no effect of the abnormality on the cardiac performance.

45. The system according to claim 44, wherein the adjustment of the first or second therapy includes determining the administration of a third therapy and / or reducing the amount of the first or second therapy being administered.

46. The system according to claim 45, wherein reducing the amount of the first or second therapy being administered includes reducing the frequency of the first or second therapy being administered and / or reducing the dose of the first or second therapy being administered.

47. The system according to any one of claims 35 to 46, wherein an initial HCM treatment is administered to the subject before detecting the cardiac performance abnormality.

48. The system according to claim 47, wherein the initial HCM treatment includes administering a myosin inhibitor to the subject.

49. The system according to any one of claims 35 to 48, wherein the hypertrophic cardiomyopathy includes obstructive hypertrophic cardiomyopathy (oHCM).

50. The system according to claim 49, wherein the obstruction associated with the oHCM is at least partially removed before detecting the abnormal cardiac performance.

51. The system according to claim 50, wherein the obstruction is at least partially removed via septal myectomy.

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