Combining home monitoring temporal trends from implanted defibrillators and baseline patient risk profile to predict heart failure hospitalizations (SELENE HF)
- 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
- 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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