Combining home monitoring temporal trends from implanted defibrillators and baseline patient risk profile to predict heart failure hospitalizations: results from the SELENE HF study

We developed and validated an algorithm for prediction of heart failure (HF) hospitalizations using remote monitoring (RM) data transmitted by implanted defibrillators.

The SELENE HF study enrolled 918 patients (median age 69 years, 81% men, median ejection fraction 30%) with cardiac resynchronization therapy (44%), dual-chamber (38%), or single-chamber defibrillators with atrial diagnostics (18%). To develop a predictive algorithm, temporal trends of diurnal and nocturnal heart rates, ventricular extrasystoles, atrial tachyarrhythmia burden, heart rate variability, physical activity, and thoracic impedance obtained by daily automatic RM were combined with a baseline risk-stratifier (Seattle HF Model) into one index. The primary endpoint was the first post-implant adjudicated HF hospitalization. After a median follow-up of 22.5 months since enrolment, patients were randomly allocated to the algorithm derivation group (n = 457; 31 endpoints) or algorithm validation group (n = 461; 29 endpoints). In the derivation group, the index showed a C-statistics of 0.89 [95% confidence interval (CI): 0.83–0.95] with 2.73 odds ratio (CI 1.98–3.78) for first HF hospitalization per unitary increase of index value (P < 0.001). In the validation group, sensitivity of predicting primary endpoint was 65.5% (CI 45.7–82.1%), median alerting time 42 days (interquartile range 21–89), and false (or unexplained) alert rate 0.69 (CI 0.64–0.74) [or 0.63 (CI 0.58–0.68)] per patient-year. Without the baseline risk-stratifier, the sensitivity remained 65.5% and the false/unexplained alert rates increased by ≈10% to 0.76/0.71 per patient-year.

With the developed algorithm, two-thirds of first post-implant HF hospitalizations could be predicted timely with only 0.7 false alerts per patient-year.