Artificial Intelligence-Enabled Detection and Management of Physiological Instability in Heart Failure: Results of the Randomized LINK-HF2 Trial.

Published in JACC. Heart failure. Abstract: Artificial intelligence (AI)-enabled wearable monitoring can detect individualized physiological deviations preceding heart failure (HF) decompensation, but whether these signals can be translated into improved outcomes remains uncertain. This study aims to evaluate the feasibility of integrating AI-generated notifications with a structured clinician response algorithm into postdischarge HF care and to explore their association with HF events following hospitalization. LINK-HF2 (Detection of Physiological Anomaly Using Artificial Intelligence and Prevention of Heart Failure Hospitalization) was a prospective, randomized, open-label, blinded-endpoint study conducted at 5 Veterans Affairs medical centers. Adults hospitalized for acute HF were enrolled at discharge and assigned to AI-guided monitoring with clinician notification or to standard care. All participants wore a continuous multisensor patch for up to 90 days. A personalized similarity-based model detected deviations in cardiorespiratory and activity signals and generated notifications. Implementation outcomes included feasibility, acceptability, and usability. The main exploratory clinical endpoint was HF rehospitalization. Secondary exploratory clinical endpoints included the composite outcome of all-cause unplanned rehospitalizations, emergency department visits, and all-cause mortality. All clinical endpoints were compared through Cox proportional hazards models over the follow-up period. Among 171 analyzed partici...