Background:The HeartLogic algorithm combines multiple implantable cardioverter-defibrillator sensors to identify patients at risk of heart failure (HF) events. We sought to evaluate the risk stratification ability of this algorithm in clinical practice. We also analyzed the alert management strategies adopted in the study group and their association with the occurrence of HF events.Methods:The HeartLogic feature was activated in 366 implantable cardioverter-defibrillator and cardiac resynchronization therapy implantable cardioverter-defibrillator patients at 22 centers. The median follow-up was 11 months [25th–75th percentile: 6–16]. The HeartLogic algorithm calculates a daily HF index and identifies periods IN alert state on the basis of a configurable threshold.Results:The HeartLogic index crossed the threshold value 273 times (0.76 alerts/patient-year) in 150 patients. The time IN alert state was 11% of the total observation period. Patients experienced 36 HF hospitalizations, and 8 patients died of HF during the observation period. Thirty-five events were associated with the IN alert state (0.92 events/patient-year versus 0.03 events/patient-year in the OUT of alert state). The hazard ratio in the IN/OUT of alert state comparison was (hazard ratio, 24.53 [95% CI, 8.55–70.38], P<0.001), after adjustment for baseline clinical confounders. Alerts followed by clinical actions were associated with less HF events (hazard ratio, 0.37 [95% CI, 0.14–0.99], P=0.047). No differences in event rates were observed between in-office and remote alert management.Conclusions:This multiparametric algorithm identifies patients during periods of significantly increased risk of HF events. The rate of HF events seemed lower when clinical actions were undertaken in response to alerts. Extra in-office visits did not seem to be required to effectively manage HeartLogic alerts.Registration:URL: https://www.clinicaltrials.gov; Unique identifier: NCT02275637.

中文翻译：

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用于心力衰竭风险分层和管理的多参数植入式心脏复律除颤器算法：临床实践分析

背景：HeartLogic 算法结合了多个植入式心律转复除颤器传感器来识别有心力衰竭 (HF) 事件风险的患者。我们试图评估该算法在临床实践中的风险分层能力。我们还分析了研究组采用的警报管理策略及其与 HF 事件发生的关联。方法：在 22 个中心的 366 名植入式心律转复除颤器和心脏再同步治疗植入式心律转复除颤器患者中激活 HeartLogic 功能。中位随访时间为 11 个月 [第 25-75 个百分位数：6-16]。HeartLogic 算法计算每日 HF 指数并根据可配置的阈值识别处于警报状态的时期。结果：HeartLogic 指数超过阈值 273 次 (0. 76 次警报/患者年）在 150 名患者中。处于警报状态的时间占总观察期的 11%。在观察期间，患者经历了 36 次 HF 住院治疗，8 名患者死于 HF。35 个事件与 IN 警报状态相关（0.92 个事件/患者年对比 0.03 个事件/患者年处于 OUT 警报状态）。警报状态比较的 IN/OUT 中的风险比为（风险比，24.53 [95% CI，8.55–70.38]，P <0.001)，在校正基线临床混杂因素后。紧随临床行动的警报与较少的 HF 事件相关（风险比，0.37 [95% CI，0.14–0.99]，P = 0.047）。在办公室内和远程警报管理之间没有观察到事件发生率的差异。结论：这种多参数算法可以识别心衰事件风险显着增加期间的患者。当针对警报采取临床行动时，HF 事件的发生率似乎较低。似乎不需要额外的办公室访问来有效管理 HeartLogic 警报。注册：URL：https://www.clinicaltrials.gov；唯一标识符：NCT02275637。