Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization The LINK-HF Multicenter Study
Continuous Wearable Monitoring Analytics Predict Heart Failure Hospitalization The LINK-HF Multicenter Study
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DOI：
10.1161/circheartfailure.119.006513
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发表时间：
2020-03-01
影响因子：
9.7
通讯作者：
Pham, Michael
中科院分区：
文献类型：
--
作者：
Stehlik, Josef;Schmalfuss, Carsten;Pham, Michael
背景：植入式心脏传感器已显示出减少心力衰竭（HF）再住院的希望，但无创方法的疗效尚未确定。本研究的目的是确定无创远程监测在预测心力衰竭再住院方面的准确性。方法：LINK-HF 研究（用于预测心力衰竭急性发作的多传感器无创远程监测）检查了个性化分析平台的性能，该分析平台使用连续数据流来预测心力衰竭入院后的再住院情况。使用放置在胸部的一次性多传感器贴片记录生理数据，对研究对象进行长达 3 个月的监测。数据通过智能手机不断上传到云分析平台。机器学习被用来设计一种预后算法来检测心力衰竭恶化。临床事件已正式裁决。结果：纳入了 100 名年龄 68.4 +/- 10.2 岁的受试者（98% 为男性）。出院后，分析平台得出预期生理值的个性化基线模型。基线模型估计的生命体征与实际监测值之间的差异用于触发临床警报。发生了 35 起计划外非创伤住院事件，其中包括 24 起恶化的心力衰竭事件。该平台能够检测心力衰竭恶化住院的先兆，敏感性为 76% 至 88%，特异性为 85%。初始警报和再次入院之间的中位时间为 6.5 (4.2-13.7) 天。结论：来自可穿戴传感器的多变量生理遥测可以提供即将再次住院的准确早期检测，其预测精度与植入设备相当。应进一步测试这种低成本无创缓解再住院方法的临床疗效和普遍性。注册：URL：。唯一标识符：NCT03037710。
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: . Unique Identifier: NCT03037710.