Remote monitoring algorithms predict heart failure events, may reduce hospitalizationsRemote monitoring may help predict heart failure events and reduce hospital stays

This narrative review synthesizes evidence on remote monitoring (RM) of cardiac implantable electronic devices (CIEDs) for heart failure (HF) management. The authors focus on multiparametric algorithms—HeartLogic, TriageHF, and HeartInsight—that integrate hemodynamic and arrhythmic parameters to predict HF events with good sensitivity. These algorithms may potentially reduce hospitalizations and improve outcomes, though the review does not provide pooled effect sizes or quantitative data.

The review highlights the role of artificial intelligence in enhancing RM capabilities but notes several limitations. Heterogeneous protocols across studies, data latency issues, inadequate reimbursement structures, and inconsistent RM implementation hinder widespread adoption. The authors do not report safety data, funding sources, or conflicts of interest.

Practice relevance is tempered by these gaps. While RM shows promise for early HF event detection, standardized workflows and reimbursement models are needed before routine clinical use. Clinicians should interpret findings cautiously given the lack of comparative data and the narrative nature of the evidence.