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临床试验/NCT05080504
NCT05080504已完成不适用

Heart Failure Monitoring With Eko Electronic Stethoscopes (CardioMEMS)

Eko Devices, Inc.2 个研究点 分布在 1 个国家目标入组 17 人开始时间: 2021年2月1日最近更新:
适应症

试验速览

阶段
不适用
状态
已完成
入组人数
17
试验地点
2
主要终点
Correlation between AI/ML model output and the ground-truth of CardioMEMS PA pressure measurements.

研究概览

简要总结

This study will enroll heart failure (HF) patients who are under active management with an implanted pulmonary artery pressure sensor (CardioMEMS). Subjects will be provided an electronic stethoscope (the Eko DUO) to take at-home heart sound, lung sound, and ECG recordings in conjunction with regimented CardioMEMS measurements. These two datasets will be used to confirm whether an AI/ML model to track HF status can be developed.

详细描述

Heart failure (HF) affects an estimated 6.2 million Americans over the age of 20 and carries a very high healthcare system burden worldwide. Annual costs for HF management in the United States were estimated at $30.7 billion in 2012 and are projected to increase to $68.7 billion by 2030. The primary cost driver for HF management is a high rate of acute decompensation and subsequent hospitalization. The mean per-patient cost of an HF-related hospitalization is estimated to be $14,631.

Among the conditions that the Centers of Medicare and Medicaid Services (CMS) monitors for their Hospital Readmission Reduction Program, HF has the highest median readmission rate at days 1-29 (23%) and days 1-60 (11.4%) postdischarge. The cost burden of HF readmission is $2.7 billion in 2013. A meta-analysis from 2012 estimated that 23.1% of HF readmissions are avoidable, although individual studies ranged from 5% to 79%. 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.

Invasive hemodynamic sensor devices have enabled HF care teams to better predict and prevent HF decompensation events, and thus prevent rehospitalizations. One such device is the CardioMEMS pulmonary artery (PA) sensor (Abbott Inc., Atlanta, GA, USA). The CardioMEMS is implanted in a branch of the left PA, allowing for daily measurements of PA pressures. PA pressures are used as a surrogate marker of filling pressure, and rising filling pressures, in turn, are a marker that precedes the exacerbation of HF. The CHAMPIONS trial demonstrated that remote diuretic management using CardioMEMS reduced HF all-cause hospitalizations by 43% and mortality by 57%. Unfortunately, CardioMEMS as an HF solution is invasive, costly (average sales price of $17,750), indicated for a restricted patient population (NYHA class III HF who have been hospitalized within the last year), and has limited reimbursement coverage due to equivocal cost-effectiveness projections.

This has stimulated a search for less expensive, 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 a sensitivity of 76% to 88%, a specificity of 85%, and a median lead time of 6.5 days.

Despite these initially promising results, however, these devices have significant disadvantages. The inpatient device used in the Taiwanese study could not be adapted into a portable form factor for outpatient use. Wearable devices can be rigid, uncomfortable, and highly visible, all of which can interfere with patient function and decrease monitoring compliance.

研究设计

研究类型
Observational
观察模型
Cohort
时间视角
Prospective

入排标准

年龄范围
18 Years 至 —(Adult, Older Adult)
性别
All
接受健康志愿者

入选标准

  • Aged 18 years and older
  • Patient or healthcare proxy willing to give written informed consent to participate
  • Presence of an implanted CardioMEMS device or imminent implantation of a CardioMEMS device
  • Expressed willingness to take DUO recordings immediately before or after taking their CardioMEMS measurements, on the same schedule prescribed by their physician
  • Functioning iOS or Android smartphone or tablet that can download and run the companion Eko application
  • Access to WiFi or cellular data connection at home

排除标准

  • Patient or healthcare 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 (e.g., sternotomy or thoracotomy)

结局指标

主要结局

Correlation between AI/ML model output and the ground-truth of CardioMEMS PA pressure measurements.

时间窗: 6 months

The primary objective of this proof of concept study is to demonstrate whether Eko data scientists can create an artificial intelligence machine learning (AI/ML) model of pulmonary artery (PA) pressures by analyzing sound and electrical (ECG) signals of heart activity captured by the non-invasive, FDA-cleared, Eko DUO electronic stethoscope.

次要结局

  • Composite of the incidence of poor-quality ECG or PCG data and tabulation of patient compliance with the data measurement schedule(6 months)
  • Intra-subject reproducibility of measured variables(6 months)

研究者

申办方类型
Industry
责任方
Sponsor

研究点 (2)

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