Cardiology Division, "Giovan Battista Grassi" Hospital, Rome, Italy.
"Unità Operativa di Elettrofisiologia, Studio e Terapia delle Aritmie", Monaldi Hospital, Naples, Italy.
Clin Cardiol. 2020 Jul;43(7):691-697. doi: 10.1002/clc.23366. Epub 2020 Apr 18.
The HeartLogic algorithm measures data from multiple implantable cardioverter-defibrillator-based sensors and combines them into a single index. The associated alert has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation.
We describe a multicenter experience of remote HF management by means of HeartLogic and appraise the value of an alert-based follow-up strategy.
The alert was activated in 104 patients. All patients were followed up according to a standardized protocol that included remote data reviews and patient phone contacts every month and at the time of alerts. In-office examinations were performed every 6 months or when deemed necessary.
During a median follow-up of 13 (10-16) months, the overall number of HF hospitalizations was 16 (rate 0.15 hospitalizations/patient-year) and 100 alerts were reported in 53 patients. Sixty alerts were judged clinically meaningful, and were associated with multiple HF-related conditions. In 48 of the 60 alerts, the clinician was not previously aware of the condition. Of these 48 alerts, 43 triggered clinical actions. The rate of alerts judged nonclinically meaningful was 0.37/patient-year, and the rate of hospitalizations not associated with an alert was 0.05/patient-year. Centers performed remote follow-up assessments of 1113 scheduled monthly transmissions (10.3/patient-year) and 100 alerts (0.93/patient-year). Monthly remote data review allowed to detect 11 (1%) HF events requiring clinical actions (vs 43% actionable alerts, P < .001).
HeartLogic allowed relevant HF-related clinical conditions to be identified remotely and enabled effective clinical actions to be taken; the rates of unexplained alerts and undetected HF events were low. An alert-based management strategy seemed more efficient than a scheduled monthly remote follow-up scheme.
HeartLogic 算法可测量来自多个植入式心律转复除颤器的传感器的数据，并将其组合成一个单一的指标。相关警报已被证明是心力衰竭（HF）失代偿即将发生的敏感和及时的预测指标。
我们描述了一种通过 HeartLogic 进行远程 HF 管理的多中心经验，并评估了基于警报的随访策略的价值。
在 104 名患者中激活了警报。所有患者均根据标准化协议进行随访，包括每月进行远程数据审查和患者电话联系，以及在警报时进行。每 6 个月或必要时进行门诊检查。
在中位数为 13（10-16）个月的随访期间，HF 住院的总次数为 16（发生率为 0.15 次/患者年），53 名患者中报告了 100 次警报。60 次警报被认为具有临床意义，并与多种 HF 相关病症相关。在 60 次警报中，有 48 次临床医生之前并不了解该病症。在这 48 次警报中，有 43 次触发了临床行动。被认为无临床意义的警报发生率为 0.37/患者年，与警报无关的住院率为 0.05/患者年。各中心对 1113 次预定的每月传输（10.3/患者年）和 100 次警报（0.93/患者年）进行了远程随访评估。每月远程数据审查可发现 11 例（1%）需要临床行动的 HF 事件（与 43%的可采取行动警报相比，P < .001）。
HeartLogic 可远程识别与 HF 相关的临床病症，并采取有效的临床行动；未解释的警报和未检测到的 HF 事件的发生率较低。基于警报的管理策略似乎比预定的每月远程随访方案更有效。
