aceArtificial Intelligence-Enabled Detection and Management of Physiological Instability in Heart Failure: Results of the Randomized LINK-HF2 Trial
Background 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. Objectives 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. Methods LINK-HF2 (Det…
In veterans discharged after acute heart failure hospitalization, AI-guided monitoring with a clinician response algorithm was feasible and lowered the rate of AI-detected physiological instability versus standard care.
The same AI-guided monitoring did not show a reduction in heart failure rehospitalization or the composite of unplanned rehospitalization, ED visits, and death in exploratory analyses, and workflow integration and response uncertainty hindered implementation.
The study was a feasibility trial with a modest sample, and its clinical endpoints were exploratory, so it cannot establish whether AI-guided monitoring changes rehospitalization or mortality.
Evidence
- Peer-reviewedJACC: Heart Failure2026-10-09
How should this claim be treated?
Truvace Impact Record TRV-2026-1587, v1: “Artificial Intelligence-Enabled Detection and Management of Physiological Instability in Heart Failure: Results of the Randomized LINK-HF2 Trial.” Truvace, 2026-10-16. /record/TRV-2026-1587 (accessed at citation time). sha256 9271706e45ba6682…
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