A higher frequency of HeartLogic alerts per year independently predicted increased all-cause mortality (HR 2.07; 95% CI 1.40-3.04; p<0.001) in heart failure patients.
Cohort (n=356)
No
Does a higher HeartLogic alert burden predict all-cause and cardiac mortality in heart failure patients with CIEDs?
A higher frequency of HeartLogic alerts independently predicts long-term all-cause and cardiac mortality in heart failure patients, identifying a high-risk cohort that may benefit from targeted interventions.
Hazard Ratio: 2.07 (95% CI 1.4–3.04)
p-value: p=<0.001
Abstract Background HeartLogic (HL) is a multiparameter heart failure (HF) risk algorithm integrated into Boston Scientific cardiac implantable electronic devices (CIEDs). By combining physiological measures, HL provides early detection of HF deterioration with a median lead time of 34 days. While HL alerts are known to predict short-term decompensation and HF events, their role in long-term mortality risk remains unclear. Purpose To evaluate the association of heart failure alerts with all-cause and cardiac mortality in HF patients undergoing remote monitoring (RM). Methods A retrospective cohort study of 356 patients with HL-capable CIEDs undergoing RM at a tertiary centre. A HL alert was triggered by a HL index (HLi) ≥ 16 and resolved when HLi ≤ 6. HL alert burden was quantified as the total number of HL alert episodes and the time spent in alert per year. Patients were stratified into quartiles based on number of HL alerts per year for Kaplan-Meier survival analysis. Cox proportional hazards models and Fine-Gray competing risks regression were used to assess the relationship between HL alert burden and mortality as a continuous predictor. Results Among the 356 patients (mean age 68.6 ± 11.3 years; 76.7% male), monitored over a median follow-up of 1.44 years (IQR: 0.6-2.9 years), there were 23 deaths (9 cardiac; 14 non-cardiac). Compared with survivors, patients who died had significantly higher HL alerts-per-year (3.01 vs. 1.17, p 0.001) and spent more days-in-alert per-year (117.6 vs. 47.4, p 0.001). However, in Cox proportional hazards models, only the number of HL alerts per year emerged as a robust, independent predictor of both all-cause (HR 2.07, 95% CI 1.40-3.04, p 0.001) and cardiac mortality (HR 2.19, 95% CI 1.35-3.56, p = 0.0015). By contrast, days-in-alert per-year did not independently predict mortality (p0.05) once adjusted for confounders. Fine-Gray competing-risks regression confirmed frequent HL alerts-per-year strongly predicted cardiac death (subdistribution HR 2.15, 95% CI 1.35-3.43, p = 0.0012). Total days-in-alert per-year did not contribute to additional risk. Conclusion A higher frequency of HL alerts is a strong predictor of both all-cause and cardiac mortality in HF patients. These findings indicate that HL alert burden may help identify patients at the highest risk who could benefit from early, targeted HF interventions. Further studies are needed to determine whether HL-guided interventions can reduce mortality in high-risk HF populations.
Assad et al. (Sat,) conducted a cohort in Heart failure (n=356). HeartLogic alert burden (number of alerts per year) was evaluated on All-cause mortality (HR 2.07, 95% CI 1.40-3.04, p=<0.001). A higher frequency of HeartLogic alerts per year independently predicted increased all-cause mortality (HR 2.07; 95% CI 1.40-3.04; p<0.001) in heart failure patients.