Veterans Administration Medical Center and University of Minnesota, Minneapolis, Minnesota 55417, USA.
J Card Fail. 2011 Jan;17(1):11-6. doi: 10.1016/j.cardfail.2010.08.001.
Remote monitoring of heart failure (HF) patients may help in the early detection of acute HF decompensation before the onset of symptoms. Appropriate early intervention in these patients may reduce HF-related hospitalizations and costs.
The MUSIC (Multi-Sensor Monitoring in Congestive Heart Failure) study comprises 2 multicenter nonrandomized phases (MUSIC-Development and MUSIC-Validation) designed to develop and validate an algorithm for the prediction of acute HF decompensation using multiple physiologic signals obtained from an external, adherent, multisensor system capable of intermittent transmission of physiologic signals. Data obtained from MUSIC-Development will be used to develop the algorithm to predict HF decompensation. The algorithm will be validated in MUSIC-Validation with the objectives of ≥ 60% sensitivity for correctly predicting an acute HF event, a false-positive patient status signal rate of ≤ 1.0 per patient-year, and a safety endpoint of ≤ 5% of patients experiencing significant adverse skin conditions related to the prolonged wearing of the adherent device. A total of 542 patients in New York Heart Association functional class III-IV HF, with ejection fraction ≤ 40% and a recent HF admission, are enrolled in MUSIC-Development (n = 180) and MUSIC-Validation (n = 362). All patients are remotely monitored for 90 days using the Corventis multisensor system that transmits bioimpedance, electrocardiogram, and accelerometer data.
The MUSIC study has completed patient enrollment and follow-up in both phases. Once algorithm development is complete from the MUSIC-Development phase, the sequestered data set from the MUSIC-Validation phase will be used for algorithm validation.
对心力衰竭（HF）患者进行远程监测有助于在出现症状之前及早发现急性 HF 失代偿。对这些患者进行适当的早期干预可能会减少 HF 相关的住院和费用。
MUSIC（充血性心力衰竭的多传感器监测）研究包括 2 个多中心非随机阶段（MUSIC-Development 和 MUSIC-Validation），旨在开发和验证一种使用外部附着式多传感器系统获得的多个生理信号预测急性 HF 失代偿的算法，该系统能够间歇性传输生理信号。从 MUSIC-Development 获得的数据将用于开发预测 HF 失代偿的算法。该算法将在 MUSIC-Validation 中进行验证，目标是：正确预测急性 HF 事件的敏感性≥60%；每位患者每年假阳性患者状态信号率≤1.0；经历与附着式设备长时间佩戴相关的严重不良皮肤状况的患者安全性终点≤5%。共有 542 名纽约心脏协会（NYHA）功能分级 III-IV 级 HF、射血分数≤40%和最近 HF 入院的患者入组 MUSIC-Development（n=180）和 MUSIC-Validation（n=362）。所有患者均使用 Corventis 多传感器系统远程监测 90 天，该系统传输生物阻抗、心电图和加速度计数据。
MUSIC 研究已完成两个阶段的患者入组和随访。一旦从 MUSIC-Development 阶段完成算法开发，将使用 MUSIC-Validation 阶段的隔离数据集对算法进行验证。
