CIED algorithm-based remote monitoring reduced the heart failure hospitalization rate compared to standard care alone (3.88 vs 7.95 per 100 patient-years; IRR 0.49; 95% CI 0.26-0.91; P=0.025).
Cohort (n=567)
No
Does CIED algorithm-based remote monitoring reduce heart failure hospitalizations and improve survival in ambulatory heart failure patients?
Integrating CIED-based multisensory remote monitoring algorithms into standard care significantly reduces heart failure hospitalizations, emergency department visits, and all-cause mortality in ambulatory heart failure patients.
Effect estimate: IRR 0.49 (95% CI 0.26-0.91)
Absolute Event Rate: 3.88% vs 7.95%
p-value: p=0.025
Abstract Background Cardiac implantable electronic device (CIED)-based multisensory algorithms aim to detect early signs of fluid retention in heart failure (HF) patients, providing a window for timely (pharmacological) intervention. While previous studies demonstrate that these algorithms can be safely integrated into clinical practice, their impact on reducing HF hospitalizations and improving survival remains uncertain. Purpose This study assesses the impact of CIED-based remote monitoring, alongside standard care, on HF-related hospital admissions, emergency department (ED) visits and survival in a contemporary ambulatory HF population. Methods In this prospective cohort study, consecutive ambulatory HF patients with a CIED and under follow-up in our referral center between 01-05-2023 and 01-02-2025 were included. Carriers of CIEDs with an activated multisensory algorithm were followed-up according to the previously described CIED Algorithm-Based HF Remote Monitoring on top Standard of Care protocol. The CIED algorithms compute an index value from continuously monitored multisensory data (e.g., thoracic impedance, heart rate variability, patient activity, atrial burden). This stratifies patients and identifies those at increased risk of fluid retention, alerting the caregiver via a remote monitoring transmission. HF patients with a CIED and standard of care were the control group. The primary endpoint was the HF hospitalization rate. Secondary endpoints included time to first HF admission, HF-related ED visits, and all-cause mortality. Results The HF algorithm group comprised 191 patients while 376 patients were included in the control group. Median age was 70 years, 76% were male, 58% had cardiac resynchronization therapy (CRT), and 52% had ischemic HF. Additionally, 55% of patients were classified as NYHA class II, and 92% had a moderately to severely impaired left ventricular ejection fraction. Total follow up entailed 938 patient years (PY). Analyses was adjusted for baseline differences. The HF hospitalization rate was lower in the algorithm group with 3.88 vs 7.95 admissions per 100 PY (Incidence Rate Ratio (IRR): 0.49, 95% CI 0.26 – 0.91, P=0.025). The algorithm group had a 54% lower hazard of first HF hospitalization compared to the control group (Hazard Ratio (HR): 0.46, 95% CI 0.21–0.96, P=0.04). HF-related ED visits were lower in the algorithm group (IRR: 0.32, 95% CI 0.11 – 0.92, P=0.034). Survival was also higher in the algorithm group, with 4.2% deaths compared to 10.4% deaths (HR: 0.43, 95% CI 0.20–0.92, P=0.03). Conclusion Integrating CIED-based remote monitoring algorithms into an ambulatory HF care pathway, in addition to standard care, is associated with a lower rate of HF admissions and ED visits, and improved survival. These findings highlight the potential benefits of CIED algorithm-based remote monitoring in enhancing patient outcomes.Results Baseline
Aslan et al. (Sat,) conducted a cohort in Heart failure (n=567). CIED algorithm-based remote monitoring vs. Standard of care was evaluated on Heart failure hospitalization rate (IRR 0.49, 95% CI 0.26-0.91, p=0.025). CIED algorithm-based remote monitoring reduced the heart failure hospitalization rate compared to standard care alone (3.88 vs 7.95 per 100 patient-years; IRR 0.49; 95% CI 0.26-0.91; P=0.025).