Heart Failure Monitoring With Eko Electronic Stethoscopes

Study Overview
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Brief Summary
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This proof-of-concept study will evaluate the feasibility of the Eko DUO to measure signals relevant to heart failure (HF) decompensation, as well as the feasibility to develop an algorithm to model hemodynamic filling pressures in HF patients under active decompensation in a cardiac intensive care unit (CICU) or coronary care unit (CCU). To also assess the performance of the Eko CORE to detect signals of an HF event, this study will also take heart and lung sound measurements with the Eko CORE.

Detailed Description
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Heart failure (HF) affects an estimated 6.2 million Americans over the age of 20 and has a high cost burden worldwide. Many health plans, including CMS, have focused on interventions that monitor patients for early detection of HF decompensation. Earlier interventions can help care teams prevent avoidable hospitalizations. Health systems have developed outpatient at-home monitoring solutions to predict and prevent HF readmissions with mixed results. Despite equivocal results, telephone monitoring with vital sign and weight scale monitors are the de facto standard for outpatient HF remote monitoring. Invasive hemodynamic sensors have enabled HF care teams to better predict and prevent HF decompensation events and thus rehospitalizations based on fluid status assessment. Unfortunately, these solutions are invasive, costly, typically indicated for late stage heart failure, and have limited availability due to equivocal cost-effectiveness projections. More recently, researchers have investigated non-invasive sensors that may correlate with fluid status in HF patients. A study in Taiwan demonstrated that outpatient therapy guided by an inpatient device with ECG and sound sensors reduced post-discharge HF utilization by 31% when compared to a control group using symptoms to guide therapy. The LINK-HF study demonstrated that a wearable patch with ECG and sound sensors could predict HF readmissions with sensitivity of 76% to 88%, specificity of 85%, and a median lead time of 6.5 days. Despite promising results, these devices have significant disadvantages. The inpatient device used in the Taiwanese study was not able to be adapted into a portable form factor for outpatient use, which would make it a poor candidate for at-home remote monitoring. Conventional wearable devices also face usability challenges; they can be rigid, uncomfortable, and highly visible, which can interfere with patient functional living and decrease monitoring compliance. There is an unmet clinical need for a non-invasive, affordable device that can estimate a HF patient's hemodynamic fluid status and potentially guide a remote care team to decrease risk for readmission from the comfort of the patient's home. Eko has developed the DUO, an FDA-approved portable, hand-held, connected medical device with ECG and sound sensors. Data from this device can be wirelessly streamed to a mobile phone or tablet, which is transmitted to a HIPAA-compliant internet cloud infrastructure. This data can then be analyzed by algorithms to detect disease states. Because patients in active HF decompensation are known to have an audible third heart sound, characteristic ECG findings, and adverse time interval durations between sound and ECG signals, the Eko DUO device may be uniquely positioned to detect these types of changing signals, and predict if patients are at-risk for a HF event, well in advance of the event itself. Eko has also developed the Eko CORE, an FDA-approved electronic stethoscope with sound sensors only. The CORE has otherwise identical functionality to the DUO in regard to data streaming and transmission. The Eko CORE may be able to detect an audible third heart sound as well, and therefore has the potential to aid clinicians in predicting future HF events. In addition, because heart failure and fluid overload are reflected in the lungs as crackles (and occasionally effusions), the lung examination is and has always been a cornerstone of the overall physical examination of the patient in decompensated HF. By using the CORE to capture lung sounds in patients with decompensated HF, and comparing not only the presence or absence of crackles, but also how they change in correlation with the patient's response to treatment, we will be able to explore the utility of the Eko CORE in helping treat these patients. This will be a proof-of-concept study to evaluate the feasibility of the Eko DUO to measure signals relevant to HF decompensation, as well as the feasibility to develop an algorithm to model hemodynamic filling pressures in HF patients under active decompensation in a cardiac intensive care unit (CICU) or coronary care unit (CCU). To also assess the performance of the Eko CORE to detect signals of an HF event, this study will also take heart and lung sound measurements with the Eko CORE.

Official Title
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Heart Failure Monitoring With Eko Electronic Stethoscopes

Conditions
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Heart Failure, Crackles, Heart Failure With Preserved Ejection Fraction

Intervention / Treatment
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* Device: Use of Eko CORE and Eko DUO electronic stethoscopes


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Adults aged 18 years and older Patient or patient's legal healthcare proxy consents to participation Admitted to an CCU with a diagnosis of acute decompensated heart failure Presence of indwelling right heart catheter Receiving at least once daily hemodynamic measurements Willing to have heart and lung sounds recorded with two different electronic stethoscopes Exclusion Criteria: Patient or proxy is unwilling or unable to give written informed consent Patient is enrolled in another study that may interfere with the observations from this study Acute pericarditis Healing chest wall wounds, such as in post-sternotomy patients Pacemaker (single ventricle or biventricular) with active pacing Mechanical ventricular support (such as ECMO, LVAD, RVAD, BiVAD, Impella, intra-aortic balloon pumps) Acute heart failure secondary to: a. Severe stenotic or regurgitant valvular disease; b. Acute myocardial infarction

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Study Plan
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How is the study designed?
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Arms and Interventions

| Intervention/Treatment |
| --- |
|Device: Use of Eko CORE and Eko DUO electronic stethoscopes|Auscultation of heart and lung sounds using electronic stethoscopes|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Feasibility of use of Eko DUO by protocol compliance | Assess the feasibility of use of the Eko DUO, a non-invasive cutaneous device with simultaneous ECG and PCG recording, on ADHF patients in an CICU/CCU setting based on ease of protocol compliance. | Within two minutes of device use | 
| Feasibility of use of Eko CORE | Assess the feasibility of use of the Eko CORE, a non-invasive cutaneous device with PCG recording, on ADHF patients in an CICU/CCU setting based on ease of protocol compliance. | Within two minutes of device use | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Intra-Subject Reproducibility of Normalized EMAT | Assess intra-subject reproducibility of derived variables of interest, such as normalized EMAT. | Within two minutes of device use | 
| Intra-Subject Reproducibility of HRV | Assess intra-subject reproducibility of derived variables of interest, such as HRV | Within two minutes of device use | 
| Intra-Subject Reproducibility of S3 Heart Sound | Assess intra-subject reproducibility of derived variables of interest, such as S3 heart sound | Within two minutes of device use | 
| Algorithm Model Development of Hemodynamic Status based on time since invasive right heart catheter insertion | Develop a model composed of derived variables that correlates with fluid hemodynamic status between the time a patient's invasive right heart catheter is inserted for ADHF treatment, until the time the patient's right heart catheter is removed. | Within two minutes of device use | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Machine learning, Remote monitoring