Predictive value of remote monitoring heart failure alerts for hospitalizations and device therapy activation: insights from an island-based heart failure remote management program

Remote monitoring (RM) of heart failure (HF) patients with implantable cardiac devices allows early detection of clinical deterioration through automated multiparametric alerts. However, the predictive value of these alerts for future hospitalizations or arrhythmic therapies (ATP/shocks) in daily clinical practice remains uncertain, particularly in geographically isolated island populations where telemonitoring supports continuity of care.

To evaluate whether the number of device-generated heart failure alerts predicts HF hospitalization and subsequent device therapy activation in a real-world cohort of remotely monitored HF patients from an island-based healthcare system.

We retrospectively analysed 265 HF patients with active RM and at least 12 months of follow-up. For each patient, the total number of heart failure alerts (Heart_failure_algorithm) was recorded and correlated with HF hospitalizations and device therapies (ATP, shocks, total interventions). Patients were divided into low-alert (<2 alerts/year) and high-alert (≥2 alerts/year) groups. Statistical tests included Mann–Whitney U and Spearman correlation; logistic regression adjusted for age, sex, LVEF, and device type was used to determine independent predictors.

Patients with a high alert burden (n=91, 34%) showed more HF hospitalizations (1.8 ± 0.9 vs 0.6 ± 0.4, p < 0.001), more device therapies (4.2 ± 5.1 vs 1.9 ± 3.4, p = 0.009), and shorter median time to first hospitalization (4.3 vs 7.9 months, p = 0.02). Alert count correlated with hospitalization frequency (r = 0.61, p < 0.001) and total therapies (r = 0.39, p = 0.01). In multivariate logistic regression, ≥2 HF alerts independently predicted hospitalization (OR 2.94, 95% CI 1.53–5.22, p = 0.002) and therapy activation (OR 1.76, 95% CI 1.01–3.10, p = 0.046).

A higher number of device-generated heart failure alerts independently predicts both hospitalization and increased device therapy activation in patients under remote monitoring. Findings from this island-based population highlight the value of integrating RM alert frequency into personalized risk stratification and early intervention strategies in heart failure management, especially in geographically remote healthcare systems.