BackgroundTo predict worsening heart failure hospitalizations (WHFHs), the HeartInsight multiparametric algorithm calculates a Heart Failure (HF) Score based on temporal trends of physiologic parameters obtained through automatic daily remote monitoring of implantable cardioverter-defibrillators (ICDs). ObjectiveWe studied the association of the baseline HF Score (BHFScore), determined at algorithm activation, with long-term patient outcomes. MethodsData from nine clinical trials were pooled, including 1,841 ICD patients with a pre-implant ejection fraction ≤35%, NYHA class II/III, and no long-standing atrial fibrillation. Primary endpoint was a composite of death or WHFH. ResultsAfter a median follow-up of 631 days (interquartile range, 385-865), there were 243 WHFHs in 173 patients (9.4%) and 122 deaths (6.6%), 52 of which (42.6%) were cardiovascular. Primary endpoint occurred in 265 patients (14.4%). A multivariable time-to-first event analysis showed that a high BHFScore (>23, as determined by a time-dependent receiver operating characteristics curve analysis) was significantly associated with the occurrence of primary endpoint (adjusted hazard ratio [HR], 2.05; 95%-confidence interval [CI], 1.54–2.71; p<0.0001), all-cause death (HR, 2.37; CI, 1.56–3.58; p<0.0001), cardiovascular death (HR, 2.19; CI, 1.14–4.22; p=0.019), and WHFH (HR, 1.91; CI, 1.35–2.71; p=0.0003). In a hierarchical event analysis of all-cause death as the outcome with highest priority and WHFHs as repeated-event outcomes, the win-ratio was 2.47 (CI, 1.89–3.24; p<0.0001). ConclusionsBased on a retrospective analysis of clinical trial data with adjudicated events, baseline HF Score derived from device-monitored variables was able to stratify patients at higher long-term risk of death or WHFH.