CIED-derived sleep apnea burden and risk of heart failure hospitalization: a registry-based analysis

Background

Sleep apnea is a highly prevalent yet frequently underdiagnosed comorbidity in heart failure (HF). Cardiac implantable electronic devices (CIEDs) offer a pragmatic approach for continuous screening without additional procedures or costs. We retrospectively analyzed registry-based data to evaluate the relationship between the CIED-derived Apnea–Hypopnea Index (AHI) and hospitalizations for HF.

Methods

A registry-based cohort of patients with HF and HeartLogic-enabled CIEDs was analyzed. Patients were followed remotely using daily biometric sensor data, including AHI. Univariable and multivariable Cox regression models with the Anderson-Gill extension were employed with AHI as a time-varying covariate to assess its association with HF hospitalizations. The multivariable model was adjusted for age, sex, BMI, diabetes, ischemic heart disease, atrial fibrillation, NYHA class, and baseline LVEF. Mean AHI used to stratify subjects according to AHI>30 was derived in the 30-day period with first available measures. Recurrent events were included, and a cluster-robust variance estimator was applied to obtain robust standard errors. In addition, AHI trends were evaluated over the 14 days preceding HF hospitalization and 28 days thereafter.

Results

A total of 1,000 patients with HF and available AHI measurements were included. During a median follow-up of 3.2 years [IQR 2.2–4.8], 98 patients (9.8%) experienced 142 HF hospitalizations. The cohort was stratified by AHI ≤30 and >30 (severe). Patients with AHI >30 were older (mean ± SD: 59.9±12.5 vs. 57.3±13.5 years, p = 0.002) and more frequently male (83.2% vs. 66.3%, p < 0.002). Baseline characteristics are summarized in Table 1. Patients with AHI >30 exhibited a higher comorbidity burden and more advanced NYHA class. A clinical diagnosis of sleep apnea was recorded in only 6.1% vs. 10.8% of patients (p = 0.014).

In the two weeks preceding hospitalization, weekly mean AHI was significantly higher in hospitalized compared with non-hospitalized patients (Figure 1). Cox regression analysis demonstrated that higher AHI was independently associated with an increased risk of HF hospitalization (rate ration (RR) 1.21 per 10-unit increase; 95% CI 1.04–1.41; p = 0.012).

Conclusions

Sleep apnea estimated by CIED-derived AHI was highly prevalent yet frequently underdiagnosed in HF. Higher AHI values preceded HF hospitalizations and were independently associated with increased risk. These findings highlight the potential clinical value of device-based sleep apnea monitoring, warranting further validation.AHI and risk of HF hospitalisationbaseline characteristics