SELENE HF
Combining home monitoring temporal trends from implanted defibrillators and baseline patient risk profile to predict heart failure hospitalizations (SELENE HF)
D’Onofrio A et al. Europace 2021
doi: 10.1093/europace/euab170.
Study Design
- International, multi-center, prospective, observational, event-driven cohort study
- 34 centers in Italy and Spain
- 918 ICD and CRT-D patients with NYHA Class II-III, LVEF ≤ 35%
- To develop an algorithm to predict HF hospitalization based on seven Home Monitoring parameters and a baseline risk stratification (Seattle Heart Failure Model)
- Home Montoring parameters: Mean heart rate, mean heart rate at rest, premature ventricular contractions (PVC), atrial burden, heart rate variability (HRV), patient activity, and thoracic impedance (TI)
- Primary endpoint: First HF-related hospitalization
- Collection of ≥ 50 primary endpoint events
- Post-hoc randomization into 2 cohorts for algorithm development and validation
Key Result 1
The algorithm predicted heart failure hospitalizations early, with high sensitivity and with a low false alert rate.
Endpoint |
Sensitivity (%) |
Alerting time (days) |
False alert rate (ppy) |
First post-implant HF hospitalizations |
65.5 (45.7-82.1) |
42 (21-89) |
0.69 (0.64-0.74) |
Table 1: Main performance parameters of the predicting algorithm
(Numbers in brackets: confidence intervals, except for alerting time (IQR).
Clinical Relevance
- Heart failure is associated with poor prognosis and high hospitalization rates. Recurrent hospital admission due to heart failure results in a gradual worsening of the health status of patients and constitutes a considerable healthcare burden.
- Early prevention of HF decompensation is a key strategy to improve patient outcomes. An CIED-based algorithm that predicts impending heart failure hospitalization could help reduce hospitalization among high-risk heart failure patients.
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