Background: 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.
Methods: 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.
Results: 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.
Published by Elsevier Inc.