Why the study?
To evaluate the long-term performance of HeartLogic in patients with ICDs and CRT-Ds, and determine whether adding device-detected severe sleep apnea enhances diagnostic performance.
Does combining the HeartLogic algorithm with device-detected severe sleep apnea improve the prediction of heart failure hospitalization or death in patients with ICDs and CRT-Ds?
Does combining the HeartLogic algorithm with device-detected severe sleep apnea improve the prediction of heart failure hospitalization or death in patients with ICDs and CRT-Ds?
The HeartLogic algorithm provides high sensitivity for detecting impending heart failure events, and while adding severe sleep apnea criteria significantly reduces unexplained alerts, it does so at the expense of reduced sensitivity.
Background The HeartLogic algorithm integrates multiple implantable cardioverter-defibrillator (ICD) sensor signals to predict impending heart failure (HF) decompensation. In its validation study, HeartLogic demonstrated a sensitivity of 70%, an unexplained alert rate of 1.47 per patient-year, and a median lead time of 34 days before an HF event. Sleep apnea (SA) is known to exacerbate HF progression. Purpose This study evaluated the long-term performance of HeartLogic in a large cohort of patients implanted with ICDs and cardiac resynchronization therapy defibrillators (CRT-D). Additionally, we investigated whether the concomitant presence of a HeartLogic alert and a high respiratory disturbance index (RDI) could enhance the diagnostic performance of the system. Methods Data on HeartLogic Index and RDI were collected across the study cohort. The IN-alert HF state was defined as a multi-sensor HeartLogic Index >16. As these devices also measure respiratory patterns, a high RDI was defined as ≥30 episodes per hour (indicating severe sleep apnea) Results The HeartLogic feature was activated in 602 ICD recipients (393 with CRT-D; 78% male; mean age 68 ± 11 years; left ventricular ejection fraction 32 ± 9%) across 19 centers. The median follow-up was 45 months [IQR: 28–68]. During follow-up, 92 hospitalizations and 53 deaths occurred. Of these, 51 events were attributed to HF, and 374 outpatient visits involved intensification of HF therapy. A total of 2,384 HeartLogic alerts were recorded (1.09 per patient-year), with patients spending 13% of the total observation time in an alert state. The algorithm demonstrated a sensitivity of 80% [95% CI: 69–91] for detecting HF hospitalization or death, with a median lead time of 49 days [IQR: 21–85]. When including outpatient visits, sensitivity was 66% [95% CI: 62–71], with a median lead time of 20 days [IQR: 5–51]. The unexplained alert rate was 0.94 per patient-year [95% CI: 0.90–0.98]. The ICD-detected RDI was ≥30 episodes/h during 44% of the total observation period. Combining a HeartLogic alert with RDI ≥30 episodes/h (weekly median value at alert onset) yielded a sensitivity of 54% [95% CI: 38–71] for detecting HF hospitalization or death (p = 0.020 vs. HeartLogic alone) while significantly reducing the unexplained alert rate to 0.38 per patient-year [95% CI: 0.35–0.41] (p < 0.001). Conclusions This analysis confirms the high sensitivity and low unexplained alert rate of the HeartLogic algorithm for detecting HF-related clinical events. Concurrent verification of HeartLogic alerts and severe SA markedly decreases the rate of unexplained alerts but at the expense of reduced sensitivity.
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Mazza et al. (2026) studied this question.
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