George E. Wahlen VA Medical Center, Salt Lake City, UT (J.S., J.N.-N., H.H.).
University of Utah School of Medicine, Salt Lake City, UT (J.S., J.N.-N., P.W.).
Circ Heart Fail. 2020 Mar;13(3):e006513. doi: 10.1161/CIRCHEARTFAILURE.119.006513. Epub 2020 Feb 25.
Implantable cardiac sensors have shown promise in reducing rehospitalization for heart failure (HF), but the efficacy of noninvasive approaches has not been determined. The objective of this study was to determine the accuracy of noninvasive remote monitoring in predicting HF rehospitalization.
The LINK-HF study (Multisensor Non-invasive Remote Monitoring for Prediction of Heart Failure Exacerbation) examined the performance of a personalized analytical platform using continuous data streams to predict rehospitalization after HF admission. Study subjects were monitored for up to 3 months using a disposable multisensor patch placed on the chest that recorded physiological data. Data were uploaded continuously via smartphone to a cloud analytics platform. Machine learning was used to design a prognostic algorithm to detect HF exacerbation. Clinical events were formally adjudicated.
One hundred subjects aged 68.4±10.2 years (98% male) were enrolled. After discharge, the analytical platform derived a personalized baseline model of expected physiological values. Differences between baseline model estimated vital signs and actual monitored values were used to trigger a clinical alert. There were 35 unplanned nontrauma hospitalization events, including 24 worsening HF events. The platform was able to detect precursors of hospitalization for HF exacerbation with 76% to 88% sensitivity and 85% specificity. Median time between initial alert and readmission was 6.5 (4.2-13.7) days.
Multivariate physiological telemetry from a wearable sensor can provide accurate early detection of impending rehospitalization with a predictive accuracy comparable to implanted devices. The clinical efficacy and generalizability of this low-cost noninvasive approach to rehospitalization mitigation should be further tested. Registration: URL: https://www.clinicaltrials.gov. Unique Identifier: NCT03037710.
植入式心脏传感器已显示出降低心力衰竭（HF）再住院率的潜力，但尚未确定非侵入性方法的疗效。本研究的目的是确定非侵入性远程监测预测 HF 再住院的准确性。
LINK-HF 研究（多传感器非侵入性远程监测预测心力衰竭恶化）检查了使用连续数据流预测 HF 入院后再住院的个性化分析平台的性能。研究对象使用放置在胸部的一次性多传感器贴片进行监测，最长可达 3 个月，该贴片记录生理数据。数据通过智能手机连续上传到云分析平台。机器学习用于设计一种预测算法来检测 HF 恶化。临床事件由正式裁决。
100 名年龄 68.4±10.2 岁（98%为男性）的患者入组。出院后，分析平台得出了预期生理值的个性化基线模型。基线模型估计的生命体征与实际监测值之间的差异用于触发临床警报。有 35 例非计划性非创伤性住院事件，包括 24 例 HF 恶化事件。该平台能够以 76%至 88%的灵敏度和 85%的特异性检测到 HF 恶化住院的前兆。首次警报与再入院之间的中位时间为 6.5（4.2-13.7）天。
可穿戴传感器的多变量生理遥测技术可以提供准确的早期检测，预测精度可与植入式设备相媲美。这种低成本非侵入性方法对再入院的缓解的临床疗效和普遍性应进一步测试。注册：网址：https://www.clinicaltrials.gov。唯一标识符：NCT03037710。
