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

Abstract

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

Methods and results: 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.

Conclusion: 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.

Keywords: Cardiac resynchronization therapy; Heart failure; Hospitalization; Implantable cardioverter-defibrillator; Predictors; Remote monitoring.

Figure 1

Consort diagram. Patients who were lost to follow-up or died contributed to the analyses up to the date of last available information. Adj., adjudicated as; HF, heart failure; hosp. or hospital., hospitalization; IVI, outpatient intravenous intervention; MR, minimum monitoring rate expressed as percentage of days with remote monitoring coverage in the last 90 days; R, random allocation stratified by primary endpoint and device type; RM, remote monitoring.

Figure 2

The modified receiver operating characteristic curve of algorithm sensitivity to primary endpoints vs. false and unexplained alert rates per patient-year (ppy). The curves were computed for the derivation cohort (left panel) and the validation cohort (right panel), using 3 consecutive days with index above nominal threshold to raise an alert, and an offset of −1.0 for the recovery threshold.

Figure 3

(A) Temporal trends of the predicting index. The daily average values of the predicting index are plotted in patients with primary endpoint events (n = 60, red line) vs. patients without primary endpoint events (n = 858, blue line). Data are aligned relative to the date of heart failure hospitalization (primary endpoint group) and up to 60 days before the end of follow-up (no primary endpoint group). Owing to the Seattle Heart Failure Model baseline component, index values are constantly numerically higher in the primary endpoint group. But statistical significance is not reached and baseline stratification is not sufficient for a reliable prediction unless the index value crosses certain nominal threshold. The apparent alerting time of about 20 days (shorter than the 42 days found in the validation analysis) is the result of averaging index values over all detected and undetected 60 events. (B) The relative contribution of all seven components to the index value, averaged for the last 7 days before 60 primary endpoint events. AHRE, atrial high rate episodes; HR, heart rate; HRV, heart rate variability; PVC, premature ventricular contractions; SHFM, Seattle Heart Failure Model; TI, thoracic impedance.

Figure 4

Trends of Home Monitoring variables and the predicting index in an 82-year-old man (implanted with a CRT-D, baseline SHFM = 0.23) before a worsening HF hospitalization (11-day hospital stay, treatment with loop and potassium-sparing diuretics). With the algorithm, an alert would have been raised 42 days before hospital admission, mainly driven by increasing 24-h HR and ventricular extrasystoles, instability of nocturnal HR, decreasing daily activity and thoracic impedance, visible 4–5 weeks before the alert and 8–9 weeks before the admission. The alert would have allowed proactive care and possibly prevent the exacerbation of HF and consequent hospitalization. Yellow line: day of alert, time = 0 (nominal threshold 4.5, recovery threshold offset −1.0); red line: day of HF hospitalization; light blue square: alerting state of index. AHRE, atrial high rate episodes; CRT-D, cardiac resynchronization therapy defibrillator; HR, heart rate; HRV, heart rate variability; PVC, premature ventricular contractions; SHFM, Seattle Heart Failure Model.