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Published on: November 8, 2013
Predicting One-Year CPAP Adherence and Use Trajectories Utilizing Baseline and Early Use Data
Introduction:
Maintaining long-term positive airway pressure (CPAP) adherence is a major challenge when treating obstructive sleep apnea (OSA). We evaluated factors associated with one-year CPAP adherence and developed models predicting Month-12 adherence and CPAP-use trajectories.
Methods:
CPAP-naive adults with OSA (apnea-hypopnea index [AHI]≥5) at Kaiser Permanente Southern California with 364-day observation period were analyzed (development/internal-validation cohort, n=14,906; temporal holdout, n=1,676). Logistic regression evaluated baseline associations with Month-12 adherence (mean≥4 hours/night during days 331-360). XGBoost models utilized baseline features (clinical, sleep-study, questionnaire data), early CPAP-only data (days 1-7), or combined to predict long-term CPAP use. A latent-class model identified trajectories over days 8-364. Predictive models were evaluated in both random and temporal holdouts.
Results:
About one-third (34.6%) of the primary cohort (49.7±13.8 years, 63.2% male) were adherent during Month-12. Higher AHI and longer self-reported sleep duration favored adherence while greater comorbidity burden, depression, and substance-use disorder were associated with lower adherence. In held-out testing, adherence prediction using baseline features yielded an AUROC of 0.678. Using CPAP-only data, AUROCs were higher from 0.787 with seven days of usage data to 0.816 and 0.854 with 14 and 30 days. Models combining CPAP and baseline features increased Day-7 AUROC to 0.810. We identified four one-year trajectories: Early-Decliners (42.9%), Gradual-Decliners (17.4%), Stable-Moderate (19.0%), Stable-High (20.7%). Models predicting patient trajectory revealed macro-average AUROCs of 0.619, 0.756, and 0.770 for baseline, CPAP-only, and combined models.
Conclusion:
Baseline pAbstractTextredictors may identify strategies for adherence support, while early CPAP data substantially improved predictive performance. These models may guide personalized follow-up but should not restrict CPAP access. External and implementation validation are needed.