Systems and methods for treating hypertrophic cardiomyopathy

Abstract

Some aspects disclosed herein relate to systems and methods for treating hypertrophic cardiomyopathy (HCM) in a subject. In some embodiments, the systems and methods described herein include monitoring cardiac function and, optionally, detecting abnormalities in the cardiac function, possibly after administering an initial HCM treatment. In some cases, the initial HCM treatment includes administering a myosin inhibitor and / or septal resection. In some embodiments, the cardiac function correlates with right ventricular function and / or pulmonary artery pressure. In further embodiments, the systems and methods described herein include identifying a treatment for the subject to reduce the risk of worsening cardiac function. In some cases, identifying the treatment includes predicting the effectiveness of the treatment on cardiac function. Furthermore, in some embodiments, a recommendation for adjusting the treatment is provided based on the observed effect on cardiac function.

Description

[Technical Field]

[0001] REFERENCE TO RELATED APPLICATIONS This application claims the benefit of and priority to U.S. Patent Application No. 63 / 380,500, filed October 21, 2022, entitled "Systems and Methods for Treating Hypertrophic Cardiomyopathy," which is incorporated herein by reference in its entirety. [Background technology]

[0002] Hypertrophic cardiomyopathy (HCM) is a cardiac disease characterized by thickening of the heart muscle, most commonly in the septum between the right and left ventricles. This can lead to stiffening of the heart wall and changes in the mitral valve, which can impede normal blood flow from the heart. Therefore, in some embodiments, the heart may increase in rate to supply sufficient blood to the subject, or contract more forcefully to overcome increased pressure caused by reduced blood flow. Traditional treatments for HCM include medications that reduce the force and rate of cardiac contraction, thereby improving overall cardiac function. Typical medications include the administration of myosin inhibitors, beta-blockers, calcium channel blockers, antiarrhythmics, and anticoagulants. In some cases, invasive treatments, such as ventricular septal resection, septal ablation, and implantable cardioverter-defibrillators, are used to treat obstructive HCM. Summary of the Invention

[0003] In some aspects disclosed herein, a method for treating hypertrophic cardiomyopathy in a subject is provided. The method includes: detecting an abnormality in the cardiac function of the subject, the abnormality corresponding 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 effectiveness of a first treatment for alleviating the abnormality; optionally administering the first treatment or a second treatment to the subject based on the predicted effectiveness of the first treatment; and after administering the first treatment or the second treatment to the subject, monitoring the abnormality and optionally detecting any negative effects.

[0004] In some embodiments, the first and / or second therapies include one or more therapies including myosin inhibition, therapy aimed at reducing pulmonary vascular resistance, one or more beta-blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, one or more hemodiluting drugs, or any combination thereof. In some embodiments, the negative effect includes an increase in abnormal cardiac function in (a).

[0005] 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 related to the first therapy. In some embodiments, the one or more correlations are based on one or more health parameters received from a population and include, for each individual in the population, either: i) a negative effect on corresponding cardiac function due to administration of the first therapy; ii) a positive effect on corresponding cardiac function due to administration of the first therapy; or iii) no effect on corresponding cardiac function due to administration of the first therapy, whereby at least one of the one or more health parameters from corresponding individuals in the population correlates with an effect on cardiac function when combined with administration of the first therapy. In some embodiments, applying the one or more correlations to the data includes using a machine learning algorithm.

[0006] In some embodiments, detecting 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. In some embodiments, detecting and / or monitoring the abnormality is performed in a healthcare setting, an outpatient setting, or both. In some embodiments, detecting and / or monitoring the abnormality includes obtaining an ECG, wherein the ECG includes a single-lead ECG, a two-lead ECG, a six-lead ECG, or a 12-lead ECG.

[0007] In some embodiments, the method further includes adjusting the first or second therapy based on detecting a corresponding negative effect or no effect on the cardiac function abnormality. In some embodiments, adjusting the first or second therapy includes administering a third therapy and / or reducing the amount (dosage) of the first or second therapy. In some embodiments, reducing the amount of the first or second therapy includes reducing the administration frequency and / or the dosage of the first or second therapy. In some embodiments, the method further includes administering an initial HCM therapy to the subject before detecting the cardiac function abnormality. In some embodiments, the initial HCM therapy includes administering a myosin inhibitor to the subject.

[0008] In some embodiments, the hypertrophic cardiomyopathy includes obstructive hypertrophic cardiomyopathy (oHCM). In some embodiments, the method further includes at least partially removing an obstruction associated with oHCM before detecting the abnormality in cardiac function. In some embodiments, at least partially removing the obstruction includes performing a ventricular septal resection.

[0009] In one aspect, a non-transitory computer-readable medium for treating hypertrophic cardiomyopathy in a subject is disclosed herein, the non-transitory computer-readable medium comprising instructions that, when executed by a processor, perform the following operations: detect an abnormality in the subject's cardiac function (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 effectiveness of a first treatment for alleviating the abnormality; determine a treatment, including administering a first or second treatment to the subject based on the predicted effectiveness of the first treatment; and monitor the abnormality after administering the first or second treatment to the subject, and optionally detect any adverse effects.

[0010] In some embodiments, the first and / or second therapies include one or more therapies including myosin inhibition, therapy aimed at reducing pulmonary vascular resistance, one or more beta-blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, one or more hemodiluting drugs, or any combination thereof. In some embodiments, the negative effect includes an increase in abnormal cardiac function in (a).

[0011] In some embodiments, predicting the efficacy of a first treatment involves obtaining one or more health parameters of a subject and applying the one or more health parameters to one or more correlations related to the first treatment. In some embodiments, the one or more correlations are based on one or more health parameters received from a population and include, for each individual in the population, either i) a negative effect on corresponding cardiac function due to administration of the first treatment, ii) a positive result on corresponding cardiac function due to administration of the first treatment, or iii) no effect on corresponding cardiac function due to administration of the first treatment, whereby at least one of the one or more health parameters from corresponding individuals in the population is correlated with an effect on cardiac function when combined with administration of the first treatment. In some embodiments, applying the one or more correlations to the data includes using a machine learning algorithm.

[0012] In certain embodiments, detecting and / or monitoring the abnormality comprises obtaining one or more cardiac parameters of the subject, including using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof. In certain embodiments, detecting and / or monitoring the abnormality is performed in a medical setting, an ambulatory setting, or both. In some aspects, detecting and / or monitoring the abnormality comprises obtaining an electrocardiogram (ECG), including a single-lead ECG, a two-lead ECG, a six-lead ECG, or a 12-lead ECG.

[0013] In some embodiments, the method further includes determining an adjustment of the first or second therapy based on detecting a corresponding negative effect or no effect on the abnormal cardiac function. In some embodiments, adjusting the first or second therapy includes determining to administer a third therapy and / or determining a dose reduction of the first or second therapy. In some embodiments, reducing the dose of the first or second therapy includes reducing the administration frequency and / or reducing the dosage of the first or second therapy.

[0014] In some embodiments, the subject is administered an early HCM treatment before detecting the abnormality in cardiac function. In some embodiments, the early HCM treatment comprises administering a myosin inhibitor to the subject. In some embodiments, the hypertrophic cardiomyopathy comprises obstructive hypertrophic cardiomyopathy (oHCM). In some embodiments, the blockage associated with oHCM is at least partially removed before detecting the abnormality in cardiac function. In one embodiment, at least partial removal of the obstruction is accomplished through a ventricular septal resection.

[0015] In one aspect, a system is disclosed for treating hypertrophic cardiomyopathy in a subject, the system including one or more processors and one or more memories, the memories storing instructions that, when executed by the one or more processors, perform the following operations: detect an abnormality in the subject's cardiac function (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 effectiveness of a first treatment for alleviating the abnormality; optionally determine a treatment, including administering a first or second treatment to the subject, based on the predicted effectiveness of the first treatment; and monitor the abnormality and, optionally, detect any adverse effects after administering the first or second treatment to the subject.

[0016] In certain embodiments, the first and / or second treatments include one or more treatments including myosin inhibition, treatment aimed at reducing pulmonary vascular resistance, one or more beta-blockers, one or more calcium channel blockers, one or more antiarrhythmic agents, one or more hemothilunary agents, or any combination thereof.

[0017] In some embodiments, the negative effect comprises an increase in abnormal cardiac function in (a). In some embodiments, predicting the effectiveness of the first treatment comprises obtaining one or more health parameters of the subject and applying the one or more health parameters to one or more correlations related to the first treatment. In some embodiments, the one or more correlations are based on one or more health parameters received from a population and include, for each individual in the population, either i) a negative effect on the corresponding cardiac function from administering the first treatment, ii) a positive result on the corresponding cardiac function from administering the first treatment, or iii) no effect on the corresponding cardiac function from administering the first treatment, whereby at least one of the one or more health parameters from corresponding individuals in the population is correlated with an effect on cardiac function in combination with administering the first treatment. In an embodiment, applying one or more correlations of the data includes using a machine learning algorithm.

[0018] In some embodiments, detecting and / or monitoring the abnormality includes obtaining one or more cardiac parameters of the subject, including using an implantable pulmonary artery monitor, obtaining an echocardiogram (ultrasound), obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof. In some embodiments, detecting and / or monitoring the abnormality is performed in a healthcare setting, an ambulatory setting, or both. In some embodiments, detecting and / or monitoring the abnormality includes obtaining an ECG, including a single-lead ECG, a two-lead ECG, a six-lead ECG, or a 12-lead ECG.

[0019] In some embodiments, the method further comprises determining an adjustment of the first or second therapy based on detecting a corresponding negative effect or no effect on the abnormal cardiac function. In some embodiments, adjusting the first or second therapy comprises determining to administer a third therapy and / or determining a dose reduction of the first or second therapy. In some embodiments, reducing the dose of the first or second therapy comprises reducing the administration frequency and / or reducing the dosage of the first or second therapy.

[0020] In certain embodiments, the subject is administered an initial HCM treatment prior to detecting abnormal cardiac function, hi certain embodiments, the initial HCM treatment comprises administering a myosin inhibitor to the subject.

[0021] In some embodiments, the hypertrophic cardiomyopathy includes obstructive hypertrophic cardiomyopathy (oHCM). In some embodiments, prior to detecting abnormal cardiac function, the obstruction associated with oHCM is at least partially removed. In some embodiments, the at least partial removal of the obstruction is achieved through ventricular septal resection. [Brief explanation of the drawings]

[0022] Some of these and other features, aspects, and advantages will be better understood with reference to the following description and accompanying drawings.

[0023] [Figure 1] 1 shows a flowchart of an example method for treating HCM according to embodiments described herein.

[0024] [Figure 2] 1 illustrates an example system flow chart for treating HCM according to embodiments described herein.

[0025] [Figure 3A] 1 shows a block diagram of a cardiac function tool (cardiac function assessment tool) according to an embodiment.

[0026] [Figure 3B]1 illustrates a block diagram of an example of clinical data parameters according to an embodiment.

[0027] [Figure 4] 1 illustrates an example of a computer system according to an embodiment.

[0028] [Figure 5] 1 shows an example of data showing elevated pulmonary artery pressure (PAP) associated with HCM.

[0029] [Figure 6] An example of data showing that increases in pulmonary artery pressure are not necessarily dependent on changes in the LVOT (left ventricular outflow tract) pressure gradient is shown.

[0030] [Figure 7] 1 shows an example of data showing pulmonary artery pressure before, immediately after, and after septal debulking therapy.

[0031] [Figure 8] 1 shows an example flow chart illustrating the causes and effects of certain cardiac condition changes. DETAILED DESCRIPTION OF THE INVENTION

[0032] I. Definition Terms used in the claims and specifications are defined below unless otherwise specified.

[0033] 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, including male or female.

[0034] The terms "treatment," "therapy," or "therapy" are used interchangeably.

[0035] It should be noted that as used in the specification, the singular forms "a," "an," and "the" include the plural forms unless the context clearly dictates otherwise.

[0036] The terms "ambulatory," "ambulatory measurement," "ambulatory monitoring," "ambulatory monitoring parameter," and the like refer to obtaining health data (e.g., a subject's health parameter as described herein) outside of a hospital or other medical facility (e.g., a medical clinic). For example, ambulatory monitoring may refer to monitoring health data (e.g., electrocardiogram, blood pressure, weight, etc.) at home.

[0037] The phrase "and / or" as used in the specification and claims should be understood to mean "either or both" of the associated elements, meaning that the elements are present together in some cases and separately in other cases. Multiple elements listed with "and / or" are similarly interpreted to mean "one or more of the associated elements." Other elements, related or unrelated, not specified in the list may optionally be present. Thus, as a non-limiting example, when the phrase "A and / or B" is used with open language such as "comprising," it can refer, in one embodiment, to A only (which may include elements other than B), in another embodiment to B only (which may include elements other than A), and in yet another embodiment to both A and B (which may include other elements).

[0038] As used herein and in the claims, "or" should 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" should be interpreted inclusively, i.e., to mean the inclusion of at least one or more of the elements, and optionally additional items not listed. It does not refer to the inclusion of exactly one element unless there is a term clearly stating the opposite, such as "only one of" or "exactly one of," or a term such as "consisting of" as used in the claims. In general, the term "or" should not be interpreted as indicating exclusive alternatives (i.e., "one or the other, but not both") unless preceded by an exclusive term, such as "either," "one of," "only one of," or "exactly one of."

[0039] II. Overview of HCM Treatment Described herein, in some embodiments, are systems and methods for treating hypertrophic cardiomyopathy (HCM) in a subject. In some embodiments, the systems and methods described herein are provided in combination with an initial HCM treatment. In some embodiments, the systems and methods described herein include monitoring cardiac function and detecting abnormalities in the cardiac function. In some embodiments, the cardiac function correlates with right ventricular function and / or pulmonary artery pressure (PAP), which is related to the oxygenation of deoxygenated blood through the lungs and subsequent pumping of oxygenated blood to the body via the left atrium / left ventricle. In some embodiments, the systems and methods described herein include identifying a therapy to reduce the subject's risk of worsening cardiac function, which may result in a reduction in the cardiac output to pump sufficient blood to the subject's body or the development of systolic dysfunction and associated heart failure. In some embodiments, identifying such a therapy includes predicting the effectiveness of the therapy with respect to cardiac function. In some embodiments, the systems and methods described herein include providing recommendations regarding adjustments to the therapy based on observed effects on cardiac function (e.g., effects on right ventricular function, PAP, etc.).

[0040] In some embodiments described herein, systems and methods for treating hypertrophic cardiomyopathy (HCM) are based on monitoring cardiac function and detecting abnormalities therein. As used herein, "abnormal cardiac function" refers to a condition in which a parameter related to cardiac function is substandard. For example, right ventricular function (RVF) is responsible for transporting deoxygenated blood to the lungs for oxygenation and delivering oxygenated blood to the entire body via the left atrium and left ventricle. Therefore, when pressure resistance increases or blood flow decreases due to HCM, the right ventricle must increase its contractile force and / or pumping power to overcome pressure resistance and maintain blood flow throughout the body. This increase in right ventricular contractile force and pumping power correlates with a decrease in right ventricular function (RVF), resulting in decreased or abnormal cardiac function. In this example, the increased right ventricular contractile force can cause a known cardiac disorder known as right ventricular hypertrophy, which can lead to reduced cardiac output and systolic dysfunction.

[0041] In some embodiments, cardiac function is determined based on one or more cardiac parameters measured from the subject. For example, cardiac function 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 enlargement or increased muscle mass), right ventricular strain, and right ventricular size. In some embodiments, abnormal measurements of one or more of these cardiac parameters correlate with abnormal and impaired cardiac function. For example, a normal PAP at rest is approximately 8-20 mmHg, and a high PAP above this range correlates with abnormal cardiac function and may be a sign of pulmonary hypertension. In some cases, normal pulmonary vascular resistance is less than approximately 2 Wood Units (WU), and an increase in resistance above 3 WU correlates with abnormal cardiac function. Right ventricular pressure includes either or both right ventricular systolic pressure and right ventricular diastolic pressure. In some cases, normal right ventricular systolic pressure is approximately 15-30 mmHg, and normal right ventricular diastolic pressure is approximately 1-7 mmHg; increases in pressure beyond these ranges correlate with abnormal cardiac function. Normal right ventricular size is approximately 49-101 ml / m², and left ventricular size is approximately 44-80 ml / m². Therefore, if the right ventricle size exceeds the normal range or is disproportionate to the left ventricle, this correlates with abnormal cardiac function.

[0042] In some embodiments, the greater the deviation of the measured cardiac parameter from the normal range, the greater the abnormality or deterioration of cardiac function, and the greater the risk of inadequate cardiac output or systolic dysfunction.

[0043] In some embodiments, cardiac function is evaluated based on a combination of one or more cardiac parameters.For example, an increase in PAP indicates that the heart needs to increase the right ventricular force to maintain a certain cardiac output.Therefore, even if an increase in right ventricular force is correlated with abnormal cardiac function, a low right ventricular force despite an increase in PAP may be a more serious abnormality than a high right ventricular force, because a low right ventricular force may lead to a decrease in cardiac output.

[0044] Figure 1 shows a flowchart of an example of a method for treating HCM in a subject described herein. In some embodiments, the treatment comprises one or more phases, which may be administered alone or in combination with other phases to provide treatment for HCM.

[0045] In some embodiments, the first phase of treatment 501 includes administering an initial treatment 502 for HCM. The initial HCM treatment administered to the subject may be any treatment known in the art. For example, in some embodiments, the subject undergoes septal resection (for obstructive HCM) or septal ablation as an initial treatment for HCM. Other initial HCM treatments, either in combination with septal resection or septal ablation or alone, include medications such as administering myosin inhibitors, beta-blockers, calcium channel blockers, antiarrhythmics, and / or anticoagulants.

[0046] In some embodiments, the second phase of treatment 503 includes monitoring the subject's cardiac function and identifying the subject as at risk for a condition such as reduced cardiac output and / or impaired systolic function based on cardiac function abnormalities 504. The cardiac function abnormalities are detected based on measurements of one or more cardiac parameters. The measurements may be performed using an implantable pulmonary artery monitor, an echocardiogram, an electrocardiogram (ECG), one or more biomarkers indicative of cardiac function status, or a combination thereof. "Cardiac function status" here refers to the state of cardiac function measured by one or more cardiac parameters, including both abnormal and normal cardiac function. The cardiac parameters may be measured in a medical facility (e.g., a clinic, hospital, or laboratory) or in a non-clinical setting, such as a home environment, using an electrocardiogram (ECG) or other device. As described in the system overview, such home monitoring, in conjunction with a system configured to monitor cardiac function, enables communication with a healthcare professional (e.g., a physician) and provides recommendations based on the cardiac function measurements. The recommendations may be generated automatically by the system or provided through a healthcare professional.

[0047] Thus, in some embodiments, the second phase of treatment 503 includes assessing the correlation between cardiac function and the initial HCM treatment (e.g., step 502). For example, after administering the initial HCM treatment (e.g., administering a myosin inhibitor) to a subject, their cardiac function is monitored to determine whether the abnormality continues or worsens (i.e., whether cardiac function worsens).

[0048] For example, in some embodiments, septal resection (e.g., initial HCM treatment) is performed to remove the obstruction (obstructive HCM) and at least reduce the pressure gradient across the left ventricular outflow tract (LVOT). However, some subjects may experience elevated PAP (pulmonary artery pressure) for a period of time, e.g., up to a year, after surgery. In such cases, increased right ventricular contractility is necessary to maintain adequate cardiac output (systemic blood flow) and prevent or reduce the risk of systolic dysfunction. See Figures 5-7. Figure 5 shows representative data demonstrating that elevated PAP is common in patients with HCM (Reference: Circ Heart Fail. 2017 Apr; 10(4): e003689; doi: 10.1161 / CIRCHEARTFAILURE.116.003689). Figure 6 shows that an increase in PAP does not necessarily correlate with LVOT gradient pressure (i.e., fluctuations in LVOT gradient pressure may not have an effect on PAP), and Figure 7 shows representative data showing a case in which PAP remained elevated even after septal debulking therapy (Reference: Eur Heart J, Volume 35, Issue 30, 7 August 2014, Pages 2032-2039, https: / / doi.org / 10.1093 / eurheartj / eht537).

[0049] In another embodiment, as part of the initial treatment of hypertrophic cardiomyopathy (HCM), medications such as myosin inhibitors are administered to reduce the left ventricular outflow tract (LVOT) pressure gradient. This can result in elevated pulmonary artery pressure (PAP) for a period of time, as described above for septal resection. However, in some cases, myosin inhibitors can also reduce the force of cardiac contraction, particularly that of the right ventricle (RV). Therefore, even if a reduction in the LVOT pressure gradient (caused by HCM) is achieved through the administration of a myosin inhibitor, the reduced force of right ventricle contraction can impair the right ventricle's ability to overcome a still-high PAP, potentially affecting its ability to maintain cardiac output (i.e., systemic blood flow) and / or potentially causing systolic dysfunction. For example, as shown in Figure 8, an example of the present invention provides a cause and effect flowchart of the interrelationships between various cardiac components (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 shown in Figure 8, administration of a myosin inhibitor can reduce the stroke volume of the right ventricle, which can decrease cardiac output from the right ventricle.

[0050] Such cardiac function monitoring may alert the subject or healthcare provider to any unintended deterioration in cardiac function after initial HCM treatment. Treatments to improve cardiac function may be administered, including pharmacological or surgical procedures. Examples include myosin inhibitors, beta-blockers, calcium channel blockers, antiarrhythmics, and hemodiluting agents. If a myosin inhibitor is administered as initial treatment and cardiac abnormalities due to reduced right ventricular contractility persist or worsen, treatments that reduce pulmonary vascular resistance, such as PDE5 inhibitors, can be administered concomitantly to offset the effects of reduced right ventricular contractility.

[0051] In some embodiments, the effect of a treatment administered to a subject is predicted in advance (step 508) to reduce the risk of the treatment having an adverse effect on cardiac function. The prediction is made using one or more subject health parameters, which may include cardiac parameters. For example, if a subject's PAP is determined to be high, it may be predicted that administration of a myosin inhibitor will increase the risk of reduced cardiac output or systolic dysfunction.

[0052] As described above, the effect of a treatment is assessed based on one or more subject health parameters (including cardiac parameters). The predicted effect of a treatment is determined based on correlations between health parameters and treatment effects. Such correlations are constructed based on data obtained from multiple subjects (populations). For each subject, the data includes the health parameters, the treatment administered, and the resulting effect on cardiac function. The effect may include a decrease (negative effect), improvement (positive effect), or no change (no effect) in cardiac function. These correlations may be derived using machine learning algorithms. Thus, in some embodiments, the systems described herein may be used to predict the effect (effect) of a treatment on a subject's cardiac function, and machine learning algorithms may be utilized.

[0053] With reference to step 508 of FIG. 1 , in some embodiments, if a negative effect is identified for a treatment, other treatments are evaluated. Furthermore, the systems described herein can be used to enable a medical professional (e.g., a physician or other healthcare provider) to suggest treatments to evaluate and predict their effect on cardiac function. The system may also automatically determine which treatments to evaluate based on the determined cardiac function and health parameters of the subject. The treatments implemented in step 506 are based on treatments predicted to have a positive effect, or at least predicted to be ineffective.

[0054] In another embodiment, the subject has not yet received an initial HCM treatment described herein, and step 504 is first performed to identify abnormalities in cardiac function, after which a treatment is optionally identified (508) and administered (506). Such treatments may include any treatments identified in the initial HCM treatment (e.g., septal resection, myosin inhibitors, etc.). In some embodiments, the treatment may include one or more of a variety of treatments, such as myosin inhibitors, PDE5 inhibitors, beta-blockers, etc.

[0055] In some embodiments, a third phase 505 of the HCM treatment described herein includes monitoring 510 cardiac function after the treatment administered in the second phase 503 (e.g., step 506). The monitoring can be performed by means such as those described in step 504, i.e., through measurement of one or more cardiac parameters. Measurements can include an implantable pulmonary artery monitor, an echocardiogram, an electrocardiogram (ECG), biomarkers indicative of cardiac function status, or a combination thereof. These measurements can be performed in a medical facility (e.g., a clinic, hospital, laboratory) or in a non-clinical setting, such as at home.

[0056] In some embodiments, the systems described herein allow for home monitoring to measure cardiac parameters periodically over a period of time without the need for a medical visit, and such monitoring can inform the subject or healthcare provider of the state of cardiac function and continuously monitor for progression or improvement of abnormalities.

[0057] In some embodiments, monitoring 510 identifies 512 a negative effect on cardiac function based on an increasing rate of abnormality (e.g., one or more cardiac parameters, alone or in combination, increasing outside of normal ranges). A treatment adjustment 514 is then suggested by the system or the subject / healthcare professional. A treatment adjustment may involve continuing the current treatment but changing the dosage or frequency of administration. For example, if a myosin inhibitor is being administered, the dosage may be reduced or the frequency of administration may be decreased.

[0058] In some embodiments, adjusting treatment may involve changing dosage or frequency and / or providing additional therapy, for example, if a patient is receiving a myosin inhibitor and right ventricular contractility is reduced while PAP remains elevated, then administration of an agent that reduces pulmonary vascular resistance, such as a PDE5 inhibitor, may be recommended.

[0059] In some embodiments, adjusting treatment involves discontinuing an existing treatment and recommending the administration of a new treatment, or discontinuing treatment for a period of time or entirely.

[0060] In some embodiments, one or more additional therapies, whether in combination with an existing therapy or not, are first recommended or implemented by predicting the effectiveness of the therapies in advance (step 508). If a negative effect on cardiac function is detected or if no positive effect is observed, the method resumes by re-performing steps 504 et seq.

[0061] In some embodiments, if no negative effect on cardiac function is detected or if a positive effect is confirmed, monitoring 510 continues.

[0062] III. System Overview In one embodiment, one or more steps of the methods described herein may be performed using the systems described herein. For example, in one embodiment, stages 2 and 3 of the HCM treatment method shown in FIG. 1 may be performed at least in part using the systems described herein.

[0063] 2 shows a schematic diagram of an example system 200 for detecting, monitoring, and managing cardiac abnormalities in a subject 202. In one embodiment, the system 200 receives subject health parameter measurements 204 obtained from one or more devices and provides them to a cardiac function tool (CPT) 206 to generate a cardiac function output. The cardiac function output may include a cardiac function status 208, a prediction of the effect of treatment on the cardiac function status, and a recommended treatment to mitigate the cardiac abnormality.

[0064] In one embodiment, system 200 provides an integrated management tool for detecting, monitoring, and managing cardiac abnormalities in a subject, as well as a means for communicating cardiac monitoring results, alerts, and / or recommendations to the subject and / or healthcare provider (e.g., a doctor, nurse, or other healthcare professional).

[0065] As described in connection with FIG. 2 , health parameter measurements 204 related to a subject 202 (e.g., ECG data from an ECG device) are received by a cardiac function tool 206, which then generates an output 208 related to the subject's cardiac function. In one embodiment, the cardiac function output is displayed on a display interface (e.g., a monitor, screen, smart device screen, etc.). In one embodiment, the health parameter measurements 204 are obtained via a device or entered into a computing device (which may include system 200) by an individual (e.g., the subject, a healthcare provider, the subject's family or friends, or other individual). In one embodiment, if the health parameter measurements are obtained via a device, the device and cardiac function tool 206 are used by different parties. For example, a first party (e.g., the subject 202, a medical professional, or other person) may operate an ECG device to obtain ECG data (i.e., health parameter measurements 204) from the subject 202, which is then provided to a second party (e.g., the subject 202, a medical professional, or other person different from the individual who operated the ECG device), who then uses the cardiac function tool 206 to determine the cardiac function output 208. However, in one embodiment, the device for measuring health parameters and the cardiac function tool 206 may be used by the same party. Similarly, when health parameter measurements are obtained by inputting data into a computing device, the inputting and operation of the cardiac function tool 206 may be performed by the same party or by different parties.

[0066] In one embodiment, the cardiac function tool is provided by one or more computing devices and may be embodied as a computer system (e.g., see FIG. 4 , item 400). Thus, in one embodiment, the methods and steps described in connection with cardiac function tool 206 may be performed in silico. For example, in one embodiment, the cardiac function tool may be configured to apply one or more health parameter measurements to one or more decision-making engines (e.g., trained models, decision trees, analytical expressions, etc.) to predict the effectiveness of a treatment for mitigating cardiac abnormalities in a subject. In one embodiment, each of the one or more decision-making engines applies an algorithm, such as a machine learning algorithm (described below), to the health parameter measurements.

[0067] 3A , a block diagram illustrating example computer logic components of cardiac function tool 206 in one embodiment is depicted. Here, cardiac function tool 206 includes an ECG data module 300, a clinical biomarker module 302, an image data module 304, a clinical data module 306, a decision engine module 308, a treatment risk prediction module 310, a monitoring and management module 312, an intervention module 314, a communication module 316, and a decision engine data storage 318. In some embodiments, cardiac function tool 206 may be configured to include additional or removed modules. For example, cardiac function tool 206 may be configured without image data module 304. In some embodiments, decision engine module 308 and / or decision engine data storage 318 may be located on different tools and / or computing devices.

[0068] As described herein, in some embodiments, cardiac function tool 206 is configured to determine cardiac function output 208 for subject 202, which may include determining a cardiac functional state and / or predicting the effectiveness of a treatment for mitigating a cardiac abnormality. In some embodiments, cardiac function tool 206 is configured to apply health parameters obtained for the subject to one or more decision engines to determine the cardiac functional state and / or the effectiveness of a treatment for mitigating a cardiac abnormality. In some embodiments, health parameters for the subject are obtained via ECG data module 300, clinical biomarker module 302, image data module 304, and / or clinical data module 306.

[0069] Subject Health Parameters In some embodiments, the subject health parameters described herein 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.

[0070] In some embodiments, the ECG data module 300 is configured to operatively interface with an ECG device to acquire ECG data from a subject. Thus, in some embodiments, the ECG data module is configured to receive ECG data from the ECG device and, if necessary, extract one or more parameters of the ECG data. The ECG device may be any device known for acquiring 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 2-lead ECG device. In some embodiments, the 12-lead ECG device is present in a medical facility, such as a healthcare provider office or clinic (including a hospital, doctor's office, medical clinic, or other facility where medical and / or health professionals work, including, for example, doctors, paramedics, nurses, first responders, psychologists, phlebotomists, medical physicists, licensed nurses, surgeons, dentists, and other healthcare professionals known to those skilled in the art). In some embodiments, the ECG device is configured for use outside of a healthcare provider office or clinic (as described herein). For example, it may be used by a subject in their own home (e.g., for ambulatory monitoring). In some embodiments, the ECG device, e.g., a single-lead or two-lead ECG device, is incorporated into a wearable device (e.g., a watch, smartwatch) or other mobile device and is configured to allow a subject to acquire ECG data while stationary and / or moving. As used herein, the terms "ambulatory monitoring" or "ambulatory measurement" refer to monitoring occurring and / or measurements being taken outside of a medical facility, such as a hospital or other medical facility (e.g., a clinic).

[0071] In some embodiments, an ECG device (e.g., a wearable device such as a smartwatch) may include built-in software (e.g., a software application, or "app") for storing acquired raw data (e.g., including but not limited to ECG waveforms). In some embodiments, the ECG device software is configured to operatively associate with ECG data module 300. In some embodiments, ECG data module 300 includes a software application that receives the raw ECG data.

[0072] In some embodiments, the ECG data module is configured to extract specific parameters from the ECG data to determine cardiac functional status and / or predict the effectiveness of a therapy. In some embodiments, the one or more specific ECG parameters extractable from the ECG data include a P wave parameter, a PR interval parameter, a QRS complex, a J point, an ST segment, a T wave, a corrected QT interval, a U wave, or any combination thereof. In some embodiments, the ECG data module is configured to correlate the one or more ECG parameters with one or more cardiac parameters to detect the cardiac functional status.

[0073] In some embodiments, the ECG data module 300 provides one or more ECG parameters to the treatment risk prediction module 310 and / or the monitoring and management module 312. In some embodiments, the treatment 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 by the ECG data module 300. In some embodiments, the treatment risk prediction module 310 is configured to correlate the one or more ECG parameters with one or more subject health parameters to determine the effectiveness of the treatment. In some embodiments, the monitoring and management module 312 is configured to correlate the one or more ECG parameters with one or more cardiac parameters to detect cardiac function status. For example, in some embodiments, the ECG parameters may be 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 treatment risk prediction module 310 and / or the monitoring and management module 312 use ECG data and / or data from one or more of the clinical biomarker module 302, the image data module 304, and the clinical data module 306 to correlate one or more cardiac parameters related to treatment response and / or cardiac functional status.

[0074] In some embodiments, the clinical biomarker module 302 is configured to acquire 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 protein measurements, one or more blood molecular measurements, urine proteins, and / or one or more other molecular measurements. Examples of blood protein measurements include B-type natriuretic peptide (BNP), N-terminal (NT)-proBNP (NT-proBNP), cardiac troponin, or combinations thereof. In some embodiments, the data for the one or more clinical biomarkers may include the amount, concentration, and / or level of the one or more clinical biomarkers.

[0075] Such clinical biomarkers may correlate with multiple factors related to cardiac health. For example, blood measurements of myocardial wall stress (NT-proBNP) and myocardial damage (troponin), lipid profiles and diabetes-related parameters (blood glucose and HbA1c), and inflammatory markers (high-sensitivity CRP (hs-CRP)) are widely established in cardiovascular assessment and management.

[0076] In some embodiments, data regarding one or more clinical biomarkers is obtained via a blood sample from a subject, which is further processed to identify one or more clinical biomarkers. In some embodiments, the blood sample may be obtained by a healthcare provider (e.g., a medical and / or health professional as described herein) and / or the subject themselves, or by a non-medical or non-health professional. In some embodiments, the blood sample may be processed in a laboratory, hospital, medical clinic, health center, or a combination thereof. In some embodiments, the blood sample may be processed using a point-of-care device, which allows the subject to process the blood sample at a location other than a healthcare facility (e.g., at home). In some embodiments, the results of blood sample processing identify one or more clinical biomarkers (and associated data, such as amounts, concentrations, and / or levels), and the results are entered into or transmitted to a computing device in operative communication with the clinical biomarker module 302, thereby enabling the clinical biomarker module 302 to receive the one or more clinical biomarkers. In some embodiments, the results of blood sample processing are input or transmitted directly to the clinical biomarker module 302 .

[0077] In some embodiments, the image data module 304 is configured to acquire and optionally store images associated with the subject. In some embodiments, the images include MRI scans (e.g., cardiac MRI scans) and / or echocardiogram scans. In some embodiments, the images are transmitted from a healthcare provider (e.g., a medical or other healthcare clinic) via a computing device in operative communication with the image data module 304. In some embodiments, the subject or other non-healthcare professional may upload or transmit the images, which are then received by the image data module 304.

[0078] In some embodiments, the image data module 304 is configured to extract one or more echocardiographic parameters. For example, example echocardiographic parameters related to the left ventricle include outflow tract obstruction, ejection fraction, fractional shortening, mass index, maximum wall thickness, septal thickness, and / or strain. Example echocardiographic parameters related to the mitral valve include systolic anterior motion and / or regurgitation. Example echocardiographic parameters related to the left atrium include maximum and / or minimum diameter, volume index, ejection fraction, function, and / or strain. Example echocardiographic parameters related to the right ventricle 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).

[0079] In some embodiments, the clinical data module 306 is configured to acquire and, if necessary, store one or more clinical parameters. FIG. 3B illustrates an example of clinical parameters. For example, in some embodiments, the clinical parameters include the subject's age, sex, weight, body mass index (BMI), height, etc. In some embodiments, clinical parameters such as weight are transmitted to the clinical data module 306 via a smart device, such as a weighing scale. Weight is an important factor in cardiovascular management as a chronic and acute risk indicator. Chronic weight is often measured in routine clinical practice because it can be an indirect indicator of cardiovascular health. On the other hand, sudden weight gain, primarily due to fluid gain, may be a sign of worsening heart failure. For certain patients, daily weight monitoring at home may be used as a means of managing heart failure.

[0080] In some embodiments, the clinical parameters include physiological data (e.g., heart rate, blood glucose level, blood pressure, respiratory rate, body temperature, blood volume, blood oxygen saturation, etc.) Such physiological data may be obtained via a smart device, such as a wearable device, configured to operatively communicate with the clinical data module 306.

[0081] In some embodiments, the clinical parameters include the subject's exercise results, physical activity (e.g., fitness activities or other movements), speed, sleep data, etc. These clinical parameters may also be obtained through a smart device, such as a wearable device, configured to operatively communicate with the clinical data module 306. For example, in some embodiments, an acceleration sensor is used to provide these clinical parameters.

[0082] In some embodiments, exercise testing is important in assessing cardiopulmonary status. In certain embodiments, exercise testing measures include maximal exercise power output (METs or watts) and distance (e.g., a 6-minute walk). Maximal exercise testing using a treadmill or stationary bicycle, combined with ECG (electrocardiogram) monitoring and, if necessary, imaging studies, is used to assess cardiac function and ischemia. Distance measurements may be performed over a defined course under supervision, with or without ECG monitoring.

[0083] Connected consumer wearable technologies are well established in their ability to measure multiple mobility parameters such as step count, speed, and exercise duration. These data, when integrated with other mobility data such as ECG, can be adapted to provide clinical implications similar to supervised exercise protocols in clinical trials.

[0084] In some embodiments, any of these clinical parameters may be input by the subject and / or a healthcare provider.

[0085] In some embodiments, clinical parameters include other data entered by the subject or a healthcare provider or transmitted by a healthcare provider. For example, such data may include subject-reported symptoms, past medical history, family history, genetic information, or a combination thereof.

[0086] In some embodiments, symptom assessment is an important factor in cardiovascular management. In some embodiments, symptoms are primarily recorded at the time of consultation, with the patient or family member informing the healthcare provider of any changes or lack of change on an ongoing basis. Symptom recording is not common outside of specific testing protocols or clinical trials. As described herein, in some embodiments, the cardiac function assessment tool 206 is configured to receive symptom input by the subject, a healthcare provider, or other individual.

[0087] Connected consumer wearable technologies are suitable for prompting individuals to input symptoms in both structured and open fields. These inputs can be integrated with other exogenous inputs to provide a longitudinal record of symptoms and associated clinical parameters.

[0088] Decision Engine and Decision Engine Data In some embodiments, the decision engine module 308 applies one or more algorithms to predict the efficacy of a treatment for alleviating the cardiac dysfunction (e.g., to determine the effect of the treatment on the cardiac dysfunction) and / or to determine the cardiac functional status. In some embodiments, the one or more algorithms utilize subject health parameters (as described herein) to predict the efficacy and / or cardiac functional status. As previously described, the subject health parameters may be obtained via ECG data, clinical biomarkers, image data, and / or clinical data parameters. In some embodiments, the one or more algorithms may correspond to determining the efficacy of a particular treatment for alleviating the cardiac dysfunction. As described herein, in some embodiments, the treatment includes medications administering a myosin inhibitor, a PDE5 inhibitor, one or more beta-blockers, one or more calcium channel blockers, one or more cardiac rhythm medications, and / or one or more blood thinners. In some embodiments, the one or more algorithms may correspond to determining the cardiac functional status, i.e., detecting the cardiac dysfunction.

[0089] In some embodiments, one or more decision engines apply algorithms (e.g., algorithms embodied in trained models) to correlate various combinations of subject health parameters of a subject with a particular treatment and / or cardiac function status. In some embodiments, at least one of the one or more algorithms includes a machine learning algorithm incorporating artificial intelligence (AI), which may contribute to improving the accuracy of the efficacy prediction and / or determining the cardiac function status. For example, with respect to determining treatment efficacy, in some embodiments, artificial intelligence is applied to training model data (which may be included in decision engine data) and / or data including subject health parameters of a plurality of individuals (e.g., a population) having one or more treatment effects, thereby identifying correlations between subject health parameters and treatment effects and training a model. In some embodiments, treatment effects correspond to a decrease in cardiac function (e.g., a negative effect), an improvement in cardiac function (e.g., a positive effect), or no change in cardiac function (e.g., no effect).

[0090] In some embodiments, any decision engine described herein is 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 feed-forward network, a convolutional neural network (CNN), a deep neural network (DNN), an autoencoder network, a generative adversarial network (GAN), 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 particular embodiments, the decision engine is a logistic regression model. In particular embodiments, the decision engine is a random forest classifier. In particular embodiments, the decision engine is a gradient boosting model.

[0091] In some embodiments, any decision engine (e.g., a trained model) described herein may be trained using a machine learning implementation technique, such as a linear regression algorithm, a logistic regression algorithm, a decision tree algorithm, a support vector machine classification, a naive Bayes classification, a K-Nearest Neighbor classification, a random forest algorithm, a deep learning algorithm, a gradient boosting algorithm, and dimensionality reduction techniques such as manifold learning, principal component analysis (PCA), factor analysis, autoencoder regularization, independent component analysis, or a combination thereof. In particular embodiments, the machine learning implementation technique is a logistic regression algorithm. In particular embodiments, the machine learning implementation technique is a random forest algorithm. In particular embodiments, the machine learning implementation technique is a gradient boosting algorithm such as XGboost. In some embodiments, any trained model described herein is trained using a supervised learning algorithm, an unsupervised learning algorithm, a semi-supervised learning algorithm (e.g., partial supervision learning), weak supervision learning, transfer learning, multi-task learning, or any combination thereof.

[0092] In some embodiments, any trained model described herein has one or more parameters (e.g., hyperparameters or model parameters). Hyperparameters are generally set before training. Examples of hyperparameters include a learning rate, the depth or number of leaves of a decision tree, the number of hidden layers in a deep neural network, the number of clusters in k-means clustering, a penalty in a regression model, and a regularization parameter associated with a cost function. Model parameters are generally 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 a risk prediction model are trained (e.g., adjusted) using training data to improve the predictive ability of the risk prediction model.

[0093] In some embodiments, any trained model described herein is trained using training data present in training model data (which may be included in decision engine module 318). In some embodiments, the training model data includes subject health parameters obtained from multiple individuals and corresponding treatments administered to the individuals and their effects on cardiac function abnormalities. In some embodiments, the training model data includes subject health parameters (e.g., one or more cardiac parameters described herein) and corresponding cardiac function states of multiple individuals.

[0094] In various embodiments, the training data used to train any trained model described herein includes reference ground truths indicating that treatments are associated with a negative effect, a positive effect, or no effect on cardiac dysfunction (hereinafter also referred to as "negative (-)," "positive (+)," or "neutral"). In various embodiments, the reference ground truths in the training data are binary values ​​such as "1" or "0." For example, treatments diagnosed with a positive effect on cardiac dysfunction may be identified as "1" in the training data, and treatments diagnosed with a negative effect on cardiac dysfunction may be identified as "0." In various embodiments, any trained model described herein is trained to minimize a loss function to more accurately predict outcomes (e.g., future diagnosis of cardiac disease) based on inputs (e.g., extracted features of subject health parameters). In some embodiments, the loss function is constructed as least absolute shrinkage and selection operator (LASSO) regression, ridge regression, or ElasticNet regression. In some embodiments, any of the trained models described herein is a random forest model, trained to minimize either the Gini impurity or entropy measure for feature segmentation, thereby enabling more accurate prediction of treatment effects for cardiac dysfunction.

[0095] In various embodiments, the training data may be obtained and / or derived from a public database. In some embodiments, the training data may be obtained and collected independently of a public database. The training data may be a custom data set. As described herein, in some embodiments, the training data includes subject health parameters (as described herein) for a plurality of individuals (e.g., a population), and for each individual, the effectiveness of at least one therapy for the cardiac dysfunction is identified based on the individual's subject health parameters.

[0096] In some embodiments, correlations between subject health parameters and one or more treatments are obtained via system 200 and stored in decision engine data storage 318. In some embodiments, the decision engine data storage includes ranges and / or combinations of subject health data (e.g., cardiac parameters, ECG data, images, clinical biomarkers, subject health parameters) correlating with particular treatment efficacy and / or cardiac function status. For example, in some cases, a particular subject health parameter may be correlated with a treatment having a negative effect on cardiac function, allowing a decision engine incorporating the subject health parameters received from the subject to measure the risk associated with the likelihood and / or severity of the negative effect. In some embodiments, the decision engine data storage 318 may be updated via communication with an external database or may be updated based on subject health parameters received from the subject.

[0097] Treatment risk prediction

[0098] In some embodiments, as described herein, the treatment risk prediction module 310 is configured to predict the effectiveness of a treatment for the cardiac dysfunction. In some embodiments, the treatment risk prediction module 310 is configured to apply one or more subject health parameters (as described herein) and one or more decision engines to identify either i) the treatment has a negative effect on the cardiac dysfunction, optionally with a risk level correlated to the likelihood of worsening and / or severity of the dysfunction, ii) the treatment has no effect on the cardiac dysfunction, or iii) the treatment has a positive effect on the cardiac dysfunction. As described herein, in some embodiments, the one or more decision engines generate the correlation by correlating subject health parameter data from multiple individuals and the corresponding effects of the treatment on the cardiac dysfunction.

[0099] In some embodiments, the treatment risk prediction module 310 is configured to communicate a decision regarding the effectiveness of a treatment to the subject and / or a healthcare provider (as described herein), thereby assisting the subject in deciding whether or not to administer the treatment.

[0100] Cardiac health monitoring and management In some embodiments, the monitoring and management (MM) module 312 is configured to monitor a subject's cardiac functional status through periodic measurement and input of health parameters. In some embodiments, such monitoring is performed in steps 504 and / or 510 of Figure 1. For example, in some embodiments, the MM module 312 communicates with and obtains data from a device configured to provide subject health parameters, such as an ECG device or an accelerometer, and applies one or more decision engines (as described herein) to determine the cardiac functional status.

[0101] In some embodiments, the MM module 312 establishes a baseline assessment of the subject's cardiac functional status based on the health parameters initially received by the system 200. In some embodiments, this baseline assessment establishes a benchmark for the subject's cardiac functional status for future monitoring (e.g., step 510 of FIG. 1 ), thereby indicating regression or progression of the cardiac functional status (e.g., worsening cardiac abnormality).

[0102] In some embodiments, as described herein, the MM module 312 is configured to monitor a subject's cardiac health by periodically acquiring certain health parameters (as described herein) at a predetermined frequency to identify changes in cardiac function status (e.g., changes in the severity of abnormalities) compared to a baseline assessment. In some embodiments, the predetermined frequency includes any temporal frequency set by the subject, a healthcare professional, or other individual. For example, in some embodiments, the predetermined frequency includes acquiring health parameters daily, every 2 days, every 3 days, every 4 days, every 5 days, every 6 days, weekly, every other week, monthly, every 4-20 weeks, etc. In some embodiments, a prompt may be provided to the subject to visit a health clinic (e.g., a hospital, a medical clinic, etc.) to acquire one or more subject health parameters (e.g., an echocardiogram, a blood draw, etc.).

[0103] In some embodiments, regular monitoring by the MM module 312 reduces the risk that the subject will develop contractile dysfunction and / or reduced cardiac output (as described herein).

[0104] Intervention Recommendations In some embodiments, the intervention module 314 is configured to recommend an adjustment to an existing therapy, as described herein (e.g., see step 514 of FIG. 1 ). In some embodiments, the intervention module 314 is configured to obtain the cardiac functional status determined by the MM module 312 and determine a change from a previous cardiac functional status. In some embodiments, the MM module 312 is configured to automatically recommend a change in therapy based on a negative effect on the cardiac functional status (e.g., worsening of the cardiac abnormality) or based on the lack of an observed positive effect on the cardiac functional status (e.g., lack of improvement in the cardiac abnormality). In some embodiments, the intervention module 314 is configured to alert the subject and / or a healthcare provider (as described herein) regarding the change in cardiac functional status and receive recommendations regarding therapy adjustments. In some embodiments, the intervention module 314 is configured to communicate the recommendation to a therapy risk prediction module to determine the effectiveness of the recommended therapy and alert the subject and / or healthcare provider whether or not to administer the therapy.

[0105] In some embodiments, adjusting therapy involves maintaining the same therapy but changing one or more of the dosage and frequency of therapy. For example, if a myosin inhibitor is being administered, adjusting therapy in some embodiments involves decreasing the dosage of the myosin inhibitor and / or decreasing the frequency of administration of the myosin inhibitor.

[0106] In some embodiments, adjusting therapy includes changing one or more of the dosage and frequency of therapy and / or providing one or more additional therapies. For example, if a myosin inhibitor is being administered to reduce right ventricular contractility while pulmonary artery pressure (PAP) remains elevated, a PDE5 inhibitor or other therapy may be recommended to reduce pulmonary vascular resistance in the subject.

[0107] In some embodiments, adjusting treatment involves discontinuing administration of an existing treatment and recommending one or more additional treatments, or alternatively, not administering treatment at all (either for a specified period of time or completely ceasing treatment).

[0108] In some embodiments, if one or more additional therapies are provided in combination with or without an existing treatment (following step 506 of FIG. 1 ), the effect of the treatment is predicted (following step 508 of FIG. 1 ) before recommending or administering to the subject. In some embodiments, if a negative effect on cardiac function is detected or if a positive effect is not confirmed, the method returns to step 504 and begins again.

[0109] communication In some embodiments, the communications module 316 is configured to communicate with a healthcare provider, send alerts and / or cardiac health status, and / or relay recommendations, data (e.g., additional health parameters including clinical biomarkers and / or images), and messages to the subject or to the cardiac function tool 206 (and associated modules).

[0110] In some embodiments, cardiac function tool 206 is configured to display a subject's health parameters, cardiac function status, and treatment effectiveness using a display interface (e.g., a monitor, screen, etc.), as described herein.

[0111] IV. Computer Implementation The methods described herein, particularly the implementation of one or more decision engines for determining the efficacy of treatment for cardiac status and / or cardiac abnormalities, are, in some embodiments, executed on one or more computers.

[0112] For example, any of the methods described herein may be implemented and deployed 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 encoding machine-readable data, which can be used to perform any of the methods described herein and / or display any of the data sets or results (e.g., cardiac performance status, risk prediction) described herein when programmed with instructions for using the data. In some embodiments, the methods are executed on a programmable computer having a processor and a data storage system (including volatile and non-volatile memory and / or storage elements), and may optionally include a graphics adapter, a pointing device, a network adapter, at least one input device, and / or at least one output device. A display may be connected to the graphics adapter. Program code is applied to the input data to perform the functions described above and generate output information. The output information is applied to one or more output devices in a known manner. The computer may be, for example, a personal computer, microcomputer, or workstation of conventional design.

[0113] Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, they can also be implemented in assembly or machine language, if desired. In either case, the language may be compiled or interpreted. 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 magnetic diskette), which, when read by a computer, configures and operates the computer to perform the procedures described herein. The system can also be considered to be implemented as a computer-readable storage medium configured with a computer program that causes the computer to operate in a specific, predefined manner to perform the functions described herein.

[0114] The signature patterns and their databases may be provided in a variety of media to facilitate their use. "Media" refers to a product containing the signature pattern information of the present embodiment. The database of some embodiments may be recorded on a computer-readable medium, i.e., any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media (e.g., floppy disks, hard disk storage media, magnetic tape), optical storage media (e.g., CD-ROMs), electrical storage media (e.g., RAM and ROM), and hybrid media (e.g., magnetic / optical storage media). Those skilled in the art will readily appreciate that any currently known computer-readable medium can be used to create a product that records the database information. "Recorded" refers to the process of storing information on a computer-readable medium using any method known in the art. Any convenient data storage structure may be selected based on the means used to access the stored information. A variety of data processing programs and formats, such as word processing text files and database formats, may be used for storage.

[0115] In some embodiments, the methods described herein, particularly the methods for determining cardiac health, are executed on one or more computers in a distributed computing system environment (e.g., a cloud computing environment). Here, "cloud computing" refers to a model that provides on-demand network access to a collection of shared, configurable computing resources. Cloud computing can be utilized to provide on-demand access to shared, configurable computing resources. Shared resources are rapidly provisioned through virtualization, released with little management effort or service provider intervention, and then scaled appropriately. A cloud computing model can have features such as on-demand self-service, widespread network access, resource pooling, rapid elasticity, and metered services. A cloud computing model can also offer various service models, such as Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Furthermore, a cloud computing model can employ various deployment models, such as private clouds, community clouds, public clouds, and hybrid clouds. In this specification and claims, a "cloud computing environment" refers to an environment in which cloud computing is utilized.

[0116] 4 illustrates an exemplary computer for implementing each of the entities illustrated in FIGS. 1-3. Computer 400 includes at least one processor 402 connected 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 connected to memory controller hub 420, and a display 418 is connected to graphics adapter 412. Storage device 408, input device 414, and network adapter 416 are connected to I / O controller hub 422. In other embodiments, computer 400 may have a different architecture.

[0117] The storage device 408 is a non-transitory computer-readable storage medium, such as a hard drive, a compact disc read-only memory (CD-ROM), a DVD, or a solid-state memory device. The memory 406 holds instructions and data used by the processor 402. The input interface 414 is a touchscreen interface, a mouse, a trackball or other pointing device, a keyboard, or a combination thereof, and is used to input data into the computer 400. In some embodiments, the computer 400 may be configured to receive input (e.g., commands) from the input interface 414 via user gestures. The network adapter 416 connects the computer 400 to one or more computer networks.

[0118] Graphics adapter 412 displays images and other information on display 418. In various embodiments, display 418 is configured to allow a user (e.g., a subject, a medical professional, a non-medical professional) to input user selections on display 418, such as to activate a system for determining cardiac health status. In one embodiment, display 418 may include a touch interface. In various embodiments, display 418 may display cardiac function status, predicted outcomes of treatment effectiveness, and alerts related to cardiac abnormalities.

[0119] The computer 400 is adapted to execute computer program modules to provide the functionality described herein. As used herein, a "module" refers to computer program logic used to provide a particular function. Thus, a module may be implemented in hardware, firmware, and / or software. In one embodiment, the program modules are stored in the storage device 408, loaded into the memory 406, and executed by the processor 402.

[0120] The type of computer 400 used by each entity shown in Figures 1-3 may vary depending on the embodiment and the processing power required for the entity. For example, the cardiac function tool 206 may run on a single computer 400, or on multiple computers 400 communicating with each other over a network (e.g., a server farm). The computer 400 may lack some of the components described above (e.g., graphics adapter 412, display 418).

[0121] V.System Further disclosed herein is a system for implementing one or more decision engines for determining cardiac function status and / or determining the effectiveness of treatment for cardiac function abnormalities. In various embodiments, such a system may include at least a cardiac function tool 206 shown in FIG. 2. In some embodiments, cardiac function tool 206 is embodied as a computer system having an exemplary computer 400 shown in FIG. 4.

[0122] In some embodiments, the system may include one or more auxiliary devices, such as an ECG device, an ambulatory monitoring device, an image acquisition device, an accelerometer, a weight scale, or the like, or any combination thereof, as described herein. In some embodiments, the system includes both a cardiac health tool 206 (e.g., a computer system) and one or more auxiliary devices. In such embodiments, the cardiac function tool 206 can be communicatively coupled to and receive data from any auxiliary device.

[0123] All publications, patents, patent applications, and other documents cited in this application are incorporated by reference herein in their entirety for all purposes as if each individual publication, patent, patent application, or document was individually incorporated by reference.

[0124] VI. Numbered Embodiments Embodiment 1: A method for treating hypertrophic cardiomyopathy in a subject, the method comprising: detecting a cardiac function abnormality in the subject, the abnormality corresponding 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 effectiveness of a first treatment in alleviating the abnormality; administering the first treatment or a second treatment to the subject, optionally based on the predicted effectiveness of the first treatment; and monitoring the abnormality after administering the first treatment or the second treatment to the subject, and optionally detecting any adverse effects.

[0125] Embodiment 2: The method of embodiment 1, wherein the first and / or second treatment is one or more treatments including myosin inhibition, pulmonary vascular resistance reducing therapy, one or more beta-blockers, one or more calcium channel blockers, one or more cardiac rhythm medications, and / or one or more blood thinners, or any combination thereof.

[0126] Embodiment 3: The method of embodiment 1 or 2, wherein the negative effect comprises an increase in cardiac dysfunction from (a).

[0127] Embodiment 4: The method of any one of embodiments 1 to 3, wherein predicting the efficacy of the first treatment comprises obtaining one or more health parameters of the subject; and applying the one or more health parameters to one or more correlations related to the first treatment.

[0128] Embodiment 5: The method of embodiment 4, wherein the one or more correlations are based on one or more health parameters received from the population; and for each individual of the population, identifying either i) a negative effect, ii) a positive result, or iii) no effect on the corresponding cardiac function from administering the first treatment, thereby correlating the one or more health parameters from corresponding individuals of the population with the effect on cardiac function when combined with administering the first treatment.

[0129] Embodiment 6: The method of embodiment 4 or 5, wherein applying one or more correlations of the data comprises using a machine learning algorithm.

[0130] Embodiment 7: The method of any of embodiments 1-6, wherein detecting and / or monitoring cardiac function abnormalities is performed by using an implantable pulmonary artery monitor, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

[0131] Embodiment 8: The method of embodiment 7, wherein the detection and / or monitoring of cardiac function abnormalities is performed in a healthcare facility, in an outpatient setting, or both.

[0132] Embodiment 9: The method of embodiment 7 or 8, wherein detecting and / or monitoring cardiac function abnormalities comprises obtaining an ECG, wherein the ECG is one of a single-lead ECG, a two-lead ECG, a six-lead ECG, or a twelve-lead ECG.

[0133] Embodiment 10: The method described in any one of embodiments 1 to 9, further comprising adjusting the first treatment or the second treatment based on the detection of a corresponding negative effect or no corresponding effect on the cardiac function abnormality.

[0134] Embodiment 11: The method of embodiment 10, wherein adjusting the first or second treatment comprises administering a third treatment and / or reducing the amount of the first or second treatment.

[0135] Embodiment 12: The method of embodiment 11, wherein the quantitative reduction of the first or second treatment comprises reducing the frequency and / or dosage of the treatment.

[0136] Embodiment 13: The method of any of embodiments 1 to 12, further comprising administering an early HCM (hypertrophic cardiomyopathy) treatment to the subject prior to detecting abnormalities in cardiac function.

[0137] Embodiment 14: The method of embodiment 13, wherein the initial HCM treatment comprises administering a myosin inhibitor to the subject.

[0138] Embodiment 15: The method according to any one of embodiments 1 to 14, wherein the hypertrophic cardiomyopathy comprises obstructive hypertrophic cardiomyopathy (oHCM).

[0139] Embodiment 16: The method of embodiment 15, further comprising at least partially removing the obstruction associated with oHCM before detecting the abnormality in cardiac function.

[0140] Embodiment 17: The method of embodiment 16, wherein at least partial removal of the obstruction comprises performing a septal resection.

[0141] Embodiment 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 following processes to be performed: detecting an abnormality in cardiac function in the subject, the abnormality corresponding 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 effectiveness of a first treatment in alleviating the abnormality; determining a treatment, including administering the first treatment or a second treatment to the subject, based on the predicted effectiveness of the first treatment (optional); monitoring the abnormality after administration of the first treatment or the second treatment, and optionally detecting any negative effects.

[0142] Embodiment 19: The non-transitory computer-readable medium of embodiment 18, wherein the first therapy and / or the second therapy comprises one or more therapies consisting of myosin inhibition, a therapy that reduces pulmonary vascular resistance, one or more beta-blockers, one or more calcium channel blockers, one or more antiarrhythmic agents, one or more blood thinning agents, or any combination thereof.

[0143] Embodiment 20: A non-transitory computer-readable medium according to embodiment 18 or 19, wherein the negative effects include an increase in abnormal cardiac function due to (a).

[0144] Embodiment 21: A non-transitory computer-readable medium described in any of embodiments 18 to 20, wherein predicting the effectiveness of the first treatment comprises obtaining one or more health parameters of the subject; and applying the one or more health parameters to one or more correlations related to the first treatment.

[0145] Embodiment 22: A non-transitory computer-readable medium as described in embodiment 21, wherein the one or more correlations are based on one or more health parameters received from the population; and for each individual in the population, identifying either i) a negative effect on the corresponding cardiac function, ii) a positive result on the corresponding cardiac function, or iii) no effect on the corresponding cardiac function due to the administration of the first treatment, thereby correlating one or more health parameters from corresponding individuals in the population with the effect on cardiac function when combined with the administration of the first treatment.

[0146] Embodiment 23: A non-transitory computer-readable medium as described in embodiment 21 or 22, wherein applying one or more correlations of data comprises using a machine learning algorithm.

[0147] Embodiment 24: A non-transitory computer-readable medium according to any of embodiments 18 to 23, wherein the detection and / or monitoring of cardiac function abnormalities is performed using an implantable pulmonary artery monitor on the subject, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

[0148] Embodiment 25: A non-transitory computer-readable medium as described in embodiment 24, wherein the detection and / or monitoring of cardiac function abnormalities is performed in a medical facility, in an outpatient environment, or both.

[0149] Embodiment 26: A non-transitory computer-readable medium as described in embodiment 24 or 25, wherein detecting and / or monitoring cardiac function abnormalities includes obtaining an ECG, and the ECG is either a single-lead ECG, a two-lead ECG, a six-lead ECG or a twelve-lead ECG.

[0150] Embodiment 27: A non-transitory computer-readable medium described in any of embodiments 18 to 26, further comprising a step of determining adjustment of the first or second treatment based on detection of a corresponding negative effect or no effect on cardiac function abnormality.

[0151] Embodiment 28: A non-transitory computer-readable medium as described in embodiment 27, wherein adjusting the first or second treatment includes deciding to administer a third treatment and / or reducing the amount of the first or second treatment.

[0152] Embodiment 29: The non-transitory computer-readable medium of embodiment 28, wherein the quantitative reduction in the first or second therapy comprises reducing the frequency of administration and / or reducing the dose of the first or second therapy.

[0153] Embodiment 30: A non-transitory computer-readable medium according to any of embodiments 18 to 29, comprising administering an initial HCM treatment to the subject prior to detecting cardiac function abnormalities.

[0154] Embodiment 31: The non-transitory computer-readable medium of embodiment 30, wherein the initial HCM treatment comprises administering to the subject a myosin inhibitor.

[0155] Embodiment 32: A non-transitory computer-readable medium according to any one of embodiments 18 to 31, wherein the hypertrophic cardiomyopathy comprises obstructive hypertrophic cardiomyopathy (oHCM).

[0156] Embodiment 33: A non-transitory computer-readable medium as described in embodiment 32, comprising at least partially removing an obstruction associated with oHCM before detecting the cardiac function abnormality.

[0157] Embodiment 34: A non-transitory computer-readable medium as described in embodiment 33, wherein at least partial removal of the obstruction is achieved by septal myectomy.

[0158] Embodiment 35: A system for treating hypertrophic cardiomyopathy in a subject, the system comprising one or more processors and one or more memories, the memories having instructions stored therein that, when executed by the one or more processors, cause the system to perform the following operations: detecting a cardiac function abnormality in the subject, the abnormality corresponding 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 effectiveness of a first treatment in alleviating the abnormality; determining a treatment including administration of the first treatment or a second treatment, optionally based on the predicted effectiveness of the first treatment; and monitoring the abnormality after administration of the first treatment or the second treatment, and optionally detecting any adverse effects.

[0159] Embodiment 36: The system of embodiment 35, wherein the first and / or second therapy is one or more therapies including myosin inhibition, pulmonary vascular resistance reducing therapy, one or more beta-blockers, one or more calcium channel blockers, one or more cardiac rhythm medications, and / or one or more blood thinners, or any combination thereof.

[0160] Embodiment 37: The system of embodiment 35 or 36, wherein the negative effect comprises an increase in cardiac dysfunction from (a).

[0161] Embodiment 38: A system described in any of embodiments 35 to 37, wherein the step of predicting the effectiveness of the first treatment includes the steps of obtaining one or more health parameters of the subject; and applying the one or more health parameters to one or more correlations related to the first treatment.

[0162] Embodiment 39: A system as described in embodiment 38, wherein the one or more correlations are based on one or more health parameters received from the population; and for each individual in the population, identifying either i) a negative effect on the corresponding cardiac function, ii) a positive result on the corresponding cardiac function, or iii) no effect on the corresponding cardiac function due to the administration of the first treatment, thereby correlating one or more health parameters from corresponding individuals in the population with the effect on cardiac function when combined with the administration of the first treatment.

[0163] Embodiment 40: A system according to embodiment 38 or 39, wherein the step of applying one or more correlations of data comprises using a machine learning algorithm.

[0164] Embodiment 41: A system described in any of embodiments 35 to 40, wherein the detection and / or monitoring of cardiac function abnormalities is performed by using an implantable pulmonary artery monitor on the subject, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

[0165] Embodiment 42: A system as described in embodiment 41, wherein the detection and / or monitoring of cardiac function abnormalities is performed in a medical facility, in an outpatient environment, or both.

[0166] Embodiment 43: A system as described in embodiment 41 or 42, wherein detecting and / or monitoring cardiac function abnormalities includes obtaining an ECG, and the ECG is either a single-lead ECG, a two-lead ECG, a six-lead ECG or a twelve-lead ECG.

[0167] Embodiment 44: A system described in any of embodiments 35 to 43, further comprising a step of determining adjustment of the first or second treatment based on detection of a corresponding negative effect or no corresponding effect on cardiac function abnormality.

[0168] Embodiment 45: A system as described in embodiment 44, wherein adjusting the first or second treatment includes deciding to administer a third treatment and / or reducing the amount of the first or second treatment.

[0169] Embodiment 46: The system of embodiment 45, wherein the quantitative reduction of the first or second therapy comprises reducing the frequency of administration and / or reducing the dose of the first or second therapy.

[0170] Embodiment 47: A system according to any one of embodiments 35 to 46, comprising administering an initial HCM treatment to the subject before detecting cardiac function abnormalities.

[0171] Embodiment 48: The system of embodiment 47, wherein the initial HCM treatment comprises administering to the subject a myosin inhibitor.

[0172] Embodiment 49: The system of any of embodiments 35 to 48, wherein the hypertrophic cardiomyopathy comprises obstructive hypertrophic cardiomyopathy (oHCM).

[0173] Embodiment 50: The system of embodiment 49, wherein the system at least partially removes an obstruction associated with oHCM before detecting cardiac function abnormalities.

[0174] Embodiment 51: A system according to embodiment 50, wherein at least partial removal of the obstruction is performed by septal myectomy.

[0175] While various specific embodiments have been shown and described, the above description is not intended to be limiting. Upon reviewing the contents of this specification, those skilled in the art will recognize that various modifications can be made without departing from the spirit and scope of the present disclosure. Many variations will be apparent to those skilled in the art upon review of this specification.

Claims

1. 1. A method for treating hypertrophic cardiomyopathy in a subject, comprising: detecting a cardiac function abnormality in the subject, the abnormality corresponding 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 the first treatment in alleviating said abnormality; administering a first treatment or a second treatment to the subject, optionally based on the predicted effectiveness of the first treatment; and monitoring the abnormality after administration of the first or second treatment, and optionally detecting any adverse effects; A method comprising:

2. 10. The method of claim 1, wherein the first therapy and / or the second therapy is one or more therapies including myosin inhibition, pulmonary vascular resistance reducing therapy, one or more beta-blockers, one or more calcium channel blockers, one or more cardiac rhythm medications and / or one or more blood thinners, or any combination thereof.

3. 3. The method of claim 1 or 2, wherein the negative effects include an increase in the cardiac dysfunction.

4. 4. The method of claim 1, wherein predicting the efficacy of the first treatment comprises obtaining one or more health parameters of the subject; and applying the one or more health parameters to one or more correlations related to the first treatment.

5. 5. The method of claim 4, wherein the one or more correlations are based on one or more health parameters received from a population; and identifying, for each individual in the population, either i) a negative effect on the corresponding cardiac function, ii) a positive result on the corresponding cardiac function, or iii) no effect on the corresponding cardiac function due to administration of the first treatment, thereby correlating at least one health parameter from individuals in the population with the effect on cardiac function when combined with administration of the first treatment.

6. 6. The method of claim 4 or 5, wherein applying one or more correlations of the data comprises using a machine learning algorithm.

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

8. 8. The method of claim 7, wherein the detection and / or monitoring of cardiac abnormalities is performed in a medical facility, in an outpatient setting, or both.

9. 9. The method of claim 7 or 8, wherein detecting and / or monitoring cardiac abnormalities comprises obtaining an ECG, wherein the ECG is one of a single-lead ECG, a two-lead ECG, a six-lead ECG, or a twelve-lead ECG.

10. 10. The method of any one of claims 1 to 9, further comprising adjusting the first therapy or the second therapy based on the detection of a corresponding negative effect or no corresponding effect on cardiac function abnormality.

11. 11. The method of 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.

12. 12. The method of claim 11, wherein the quantitative reduction of the first or second therapy comprises reducing the frequency and / or dosage of the first or second therapy.

13. The method of any one of claims 1 to 12, further comprising administering an initial HCM treatment to the subject before detecting cardiac function abnormalities.

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

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

16. 16. The method of claim 15, further comprising at least partially removing an obstruction associated with the oHCM before detecting the cardiac function abnormality.

17. 17. The method of claim 16, wherein the step of at least partially removing the obstruction comprises performing a septal resection.

18. 1. 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 non-transitory computer readable medium to perform processes including: a process for detecting an abnormality in cardiac function in a subject, the abnormality corresponding to one or more of: abnormal pulmonary artery pressure, pulmonary hypertension, abnormal right ventricular pressure, right ventricular hypertrophy, right ventricular stress, and right ventricular size; Optionally, predicting the efficacy of the first treatment for the disorder; determining a treatment comprising administering said first treatment or a second treatment to a subject, optionally said decision being based on predicted efficacy of the first treatment; and After administering the first or second treatment to the subject, the abnormality is monitored, and optionally, any adverse effects are detected.

19. 20. The non-transitory computer-readable medium of claim 18, wherein the first therapy and / or the second therapy comprises one or more therapies including myosin inhibition, a therapy that reduces pulmonary vascular resistance, one or more beta-blockers, one or more calcium channel blockers, one or more antiarrhythmic drugs, one or more blood thinners, and / or any combination thereof.

20. 20. The non-transitory computer-readable medium of claim 18 or 19, wherein the negative effect comprises an increase in the cardiac dysfunction.

21. 21. The non-transitory computer readable medium of any of claims 18 to 20, wherein the process of predicting the efficacy of the first treatment comprises: obtaining one or more health parameters of the subject; Applying the health parameters to one or more correlations related to a first treatment.

22. 22. The non-transitory computer-readable medium of claim 21, wherein the one or more correlations are based on: A medium in which at least one health parameter from individuals in the population is correlated with an effect on cardiac function when combined with the administration of a first treatment, based on one or more health parameters obtained from a population; and, for each individual in the population, i) a negative effect on corresponding cardiac function due to the administration of a first treatment, ii) a positive effect on corresponding cardiac function due to the administration of the first treatment, or iii) no effect on corresponding cardiac function due to the administration of the first treatment.

23. 23. The non-transitory computer-readable medium of claim 21 or 22, wherein the process of applying one or more correlations uses a machine learning algorithm.

24. 24. The non-transitory computer-readable medium of any of claims 18 to 23, wherein the detecting and / or monitoring of the abnormality is performed by 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. 25. The non-transitory computer-readable medium of claim 24, wherein the anomaly detection and / or monitoring is performed in a medical institution environment, an ambulatory care environment, or both.

26. 26. The non-transitory computer-readable medium of claim 24 or 25, wherein detecting and / or monitoring cardiac abnormalities comprises obtaining an ECG, the ECG being one of a single-lead ECG, a two-lead ECG, a six-lead ECG, or a twelve-lead ECG.

27. 27. The non-transitory computer-readable medium of 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 no corresponding effect on the cardiac function abnormality.

28. 28. The non-transitory computer-readable medium of claim 27, wherein adjusting the first or second therapy includes deciding to administer a third therapy and / or reducing the amount of the first or second therapy.

29. 30. The non-transitory computer-readable medium of claim 28, wherein the quantitative reduction in the first or second therapy comprises a reduction in frequency of administration and / or a reduction in dosage of the first or second therapy.

30. 30. The non-transitory computer-readable medium of any one of claims 18 to 29, comprising administering an initial HCM therapy to a subject prior to detecting a cardiac function abnormality.

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

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

33. 33. The non-transitory computer-readable medium of claim 32, comprising at least partially removing an obstruction associated with oHCM before detecting the cardiac function abnormality.

34. 34. The non-transitory computer-readable medium of claim 33, wherein at least partially removing the obstruction is accomplished by a septal myectomy.

35. 1. A system for treating hypertrophic cardiomyopathy in a subject, comprising: one or more processors; and One or more memories having stored therein instructions that, when executed by one or more processors, cause the following operations to occur: detecting a cardiac function abnormality in a subject, the abnormality corresponding 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 the first treatment in alleviating the abnormality; determining a treatment, including administering a first treatment or a second treatment, said treatment optionally being based on the predicted effectiveness of the first treatment; and monitoring the abnormality after administration of the first treatment or the second treatment, and optionally detecting any adverse effects; A system including:

36. 36. The system of claim 35, wherein the first and / or second therapies are one or more therapies including myosin inhibition, pulmonary vascular resistance reducing therapy, one or more beta-blockers, one or more calcium channel blockers, one or more cardiac rhythm medications and / or one or more blood thinners, or any combination thereof.

37. 37. The system of claim 35 or 36, wherein the negative effect comprises an increase in cardiac dysfunction.

38. 38. The system of any one of claims 35 to 37, wherein the operation of predicting the effectiveness of the first treatment comprises obtaining one or more health parameters of the subject; and applying the one or more health parameters to one or more correlations related to the first treatment.

39. 39. The system of claim 38, wherein the one or more correlations are based on one or more health parameters received from the population; and for each individual in the population, identifying either i) a negative effect on the corresponding cardiac function, ii) a positive result on the corresponding cardiac function, or iii) no effect on the corresponding cardiac function due to the administration of the first treatment, thereby correlating at least one health parameter from individuals in the population with the effect on cardiac function when combined with the administration of the first treatment.

40. 40. The system of claim 38 or 39, wherein applying the one or more correlations comprises using a machine learning algorithm.

41. 41. The system of any one of claims 35 to 40, wherein detecting and / or monitoring cardiac function abnormalities is performed by using an implantable pulmonary artery monitor on the subject, obtaining an echocardiogram, obtaining an electrocardiogram (ECG), obtaining one or more biomarkers, or a combination thereof.

42. 42. The system of claim 41, wherein the detection and / or monitoring of cardiac abnormalities is performed in a healthcare facility, in an outpatient setting, or both.

43. 43. The system of claim 41 or 42, wherein detecting and / or monitoring cardiac abnormalities comprises obtaining an ECG, the ECG being one of a single-lead ECG, a two-lead ECG, a six-lead ECG or a twelve-lead ECG.

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

45. 45. The system of claim 44, wherein adjusting the first or second therapy includes deciding to administer a third therapy and / or reducing the amount of the first or second therapy.

46. 46. ​​The system of claim 45, wherein the quantitative reduction of the first or second therapy comprises reducing the frequency of administration and / or reducing the dosage of the first or second therapy.

47. 47. The system of any one of claims 35 to 46, comprising administering an initial HCM therapy to a subject prior to detecting abnormal cardiac function.

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

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

50. 50. The system of claim 49, wherein the system at least partially removes an obstruction associated with oHCM before detecting abnormal cardiac function.

51. 51. The system of claim 50, wherein at least partially removing the obstruction is accomplished by a septal myectomy.

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