In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

In this study, we will look back at past medical records to test how well two computer-based tools can help spot two types of heart disease: hypertrophic cardiomyopathy (HCM) and a form of cardiac amyloidosis called transthyretin amyloidosis (ATTR). One tool analyzes heart ultrasound images (echocardiograms) using artificial intelligence to identify signs of these conditions. The other tool looks at patterns in electronic health records—like diagnoses, test results, and medications—to flag patients who may have HCM or ATTR. Our goal is to see how accurate and useful these tools are in finding patients who may need further evaluation or care.

To describe current real-world utilization of mcfDNA testing for CV infections using a multi-center retrospective registry. We will develop multi-center REDCap database of mcfDNA use in valvular IE and/or CIED lead infections and summarize patient demographics and clinical characteristics of IE and CIED lead infection cases. We will assess common scenarios/indications for which mcfDNA is sent and timing of the test. Clinically relevant microbiological yield of mcfDNA in IE and/or CIED lead infections will be described.

To identify clinical predictors where mcfDNA outperforms CMT. We will assess clinical characteristics of patients with IE and CIED lead infection in whom mcfDNA has higher microbiologic yield compared to CMT. We will develop a prediction model/scoring system to identify subgroup of patients in whom mcfDNA should be sent early (after 48 hours of negative CMT).

To analyze clinical impact of mcfDNA testing in patients with valvular IE and/or CIED lead infections. We will classify cases as having Positive vs. Negative vs. Neutral impact using pre-specified definitions and assess predictor of positive clinical impact.

The purpose of the study is to evaluate the safety and how well the medication sotatercept works versus placebo in treating Heart Failure with a Preserved Ejection Fraction. The study will also look at information obtained from the tests performed as part of the study to see if subjects have improvement in symptoms of heart failure. Participation in this study will last approximately 26 months. During the study period subjects will be asked to attend regular study visits with the research coordinator. These visits will include such activities as blood tests, questionnaires, physical evaluation by a study doctor, a right heart catheterization with exercise, echocardiogram, and 6 minute hall walks. There will be 35 visits as part of participation in this clinical trial.

Participants will be randomized to either the treatment group (and receive the medication) or the control group (and not receive the medication). Subjects will have a 2:1 chance of receiving the study medication during their participation in the trial. The treatment assignment is determined by randomization, where a computer selects at random which treatment group you will be in (like drawing straws). Neither the subject, nor the blinded personnel will know which group subjects are in. Neither the subject nor the study doctor will decide what group subjects are assigned. Participants from the placebo group in CADENCE who enter HARMONIZE at Visit 9a will be randomized 1:1 to one of the active treatment groups. Participants from an active treatment group in CADENCE entering HARMONIZE after Visit 9a will be allocated to continue in the same treatment group (ie, sotatercept dose level) as in CADENCE.