Background: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.Methods: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.Results: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.Conclusions: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.

中文翻译：

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连续可穿戴监测分析可预测心力衰竭住院：LINK-HF多中心研究。

背景：植入式心脏传感器在减少因心力衰竭（HF）的再次住院治疗方面已显示出希望，但尚未确定非侵入性方法的功效。这项研究的目的是确定无创远程监测在预测心衰再住院中的准确性。数据流可预测HF入院后的住院治疗。使用放置在胸部的一次性多传感器贴片对研究对象进行长达3个月的监测，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。机器学习被用来设计一种预后的算法来检测HF恶化。结果：100例年龄为68.4±10.2岁的受试者（男性98％）入组。出院后，分析平台得出预期生理值的个性化基线模型。基线模型估计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例计划外的非创伤性住院事件，包括24例恶化的HF事件。该平台能够以76％至88％的敏感性和85％的特异性检测出因HF加重而住院的前体。初次警报到再次入院之间的中位时间为6.5（4.2-13.7）天。来自可穿戴式传感器的多元生理遥测技术可以提供与植入式设备相当的预测精度，从而可以对即将进行的住院治疗提供准确的早期检测。这种低成本的非侵入性缓解再住院方法的临床疗效和可推广性应进一步测试。注册：URL：https://www.clinicaltrials.gov。唯一标识符：NCT03037710。