Design and performance of a multisensor heart failure monitoring algorithm: Results from the multisensor monitoring in Congestive Heart Failure (MUSIC) study

Background: Remote monitoring of heart failure (HF) patients may help in the early detection of acute decompensation before the onset of symptoms, providing the opportunity for early intervention to reduce HF-related hospitalizations, improve outcomes, and lower costs. Methods and Results: MUSIC is a multicenter nonrandomized study designed to develop and validate an algorithm for prediction of impending acute HF decompensation with the use of physiologic signals obtained from an external device adhered to the chest. A total of 543 HF patients (206 development, 337 validation) with ejection fraction ≤40% and a recent HF admission were enrolled. Patients were remotely monitored for 90 days using a multisensor device. Accounting for device failure and patient withdrawal, 314 patients (114 development, 200 validation) were included in the analysis. Development patient data were used to develop a multiparameter HF detection algorithm. Algorithm performance in the development cohort had 65% sensitivity, 90% specificity, and a false positive rate of 0.7 per patient-year for detection of HF events. In the validation cohort, algorithm performance met the prespecified end points with 63% sensitivity, 92% specificity, and a false positive rate of 0.9 per patient-year. The overall rate of significant adverse skin response was 0.4%. Conclusion: Using an external multisensor monitoring system, an HF decompensation prediction algorithm was developed that met the prespecified performance end point. Further studies are required to determine whether the use of this system will improve patient outcomes.