LAUSR

Abstract Funding Acknowledgements Type of funding sources: None. Background The HeartLogic algorithm combines multiple implantable defibrillator (ICD) sensor data and has proved to be a sensitive and timely predictor of impending heart failure (HF) decompensation. Purpose We determined if remotely monitored data from this algorithm can be used to identify patients at high risk of mortality. Methods The HeartLogic feature was activated in 568 ICD patients from 26 centers. Results During a median follow-up of 26 months [25th–75th percentile: 16-37], 1200 HeartLogic alerts were recorded in 370 (65%) patients. Overall, the time IN the alert state was 13% of the total observation period (151 out of 1159 years) and 20% of the follow-up period of the 370 patients with alerts. During follow-up, 55 patients died (37 in the group with alerts). Experiencing any alert episode was associated with a substantially increased risk of death [hazard ratio (HR): 2.08, 95% confidence interval (CI): 1.16–3.73, P = 0.039]. Additionally, a time IN alert ≥20% was associated with death (HR: 4.07, 95%CI: 2.19-7.54, p<0.001, Figure), even after multivariate correction for age, atrial fibrillation on implantation, chronic kidney disease, ischemic cardiomyopathy (HR: 3.26, 95%CI:1.87-5.70, p<0.001). The rate of death was 0.25/patient-year (95%CI: 0.17-0.34) with the HeartLogic IN the alert state and 0.02/patient-year (95%CI: 0.01-0.03) OUT of the alert state, with an incidence rate ratio of 13.72 (95%CI: 7.62-25.60, p<0.001). Conclusions The HeartLogic algorithm provides an index that can be used to identify patients at higher risk of all-cause mortality. The index status identifies periods of significantly increased risk of death. Kaplan-Meier curves for time to death