What question did this study set out to answer?

This study aims to determine if flu-shot status, encounter no-show rates, and off-work orders can predict CPAP adherence in obstructive sleep apnea patients.

May 10, 2026

0610 Relationship Between Behavioral Adherence Patterns Derived from Electronic Health Records and Positive Airway Pressure Adherence

Key Points

This study aims to determine if flu-shot status, encounter no-show rates, and off-work orders can predict CPAP adherence in obstructive sleep apnea patients.
Analyzed electronic health records of adult OSA patients with new CPAP initiations from 2016 to 2025.
Compared baseline characteristics and adherence metrics between adherent and non-adherent groups.
Assessed statistical significance using Chi-Square and Student's t-tests.
CMS-adherents showed higher flu-shot rates (57.9% vs 55.6%; p<0.0001) and lower no-show rates (0.5% vs 0.8%; p<0.0001).
No significant differences in off-work orders between groups (0.22 vs 0.25; p=0.65).
EHR-derived predictions for CPAP adherence showed minimal meaningful contributions.

Abstract

Abstract Introduction Behavioral adherence insights can be derived from Electronic Health Record (EHR). We separately revealed EHR-derived medication adherence was higher in 90-day CPAP adherent versus non-adherent patients but with minimal effect size. In this study, we evaluate whether influenza vaccination rate (flu-shot), encounter no-show rates (no-show) and off-work orders and days (OWO) predict CPAP adherence. Methods We identified adult OSA patients with new CPAP initiations in Kaiser Permanente Southern California between 2016 and 2025. Baseline characteristics were compared between CMS-adherents (30-day period with ≥70% of days with ≥4 hours use during first 90-days) and non-adherents. Flu-shot status, encounter no-shows and OWO were derived from EHR (Epic Systems) and assessed during the one-year period prior to CPAP initiation. Statistical significance was assessed using Chi-Square (flu-shot) and Student’s t-tests (no-shows and OWO). Results Of 151,170 patients, CMS-adherent patients (45.7%) and non-adherent patients (54.3%) were similar in age (54.2±14.5 vs 52.1±15.2 years) and BMI (35.1±8.1 vs 34.8±8.3 kg/m2). Compared with non-adherents, CMS-adherents were more often male (65.5% vs 61.0%) and White (44.8% vs 34.6%), and less frequently Hispanic (33.1% vs 40.3%) or Black (7.3% vs 9.7%). CMS-adherents had higher AHI4% (32.1±25.8 vs 27.9±24.3) and substantially greater 90-day CPAP use (350.9±105.0 vs 116.2±113.8 min/night). Total encounters were also similar (10.4±11.0 vs 10.7±11.5). Lower no-show rates and higher flu-shot rates were seen in CMS-adherents vs non-adherents, but with very small effect sizes: no-show rate 0.5±3.3% vs 0.8±4.1% (p 0.0001); flu-shot rate 57.9 vs 55.6% (p 0.0001). OWO order frequency (0.22±1.09 vs 0.25±1.27; p=0.65) and OWO days were similar (3.0±31.4 vs 3.7±96.5; p=0.81). Conclusion Encounter no-show rates and flu-shot rates showed only very small differences between CMS-adherent vs non-adherent patients, while off-work rates did not differ. These EHR-derived metrics are unlikely to contribute meaningfully towards development of CPAP adherence prediction models. Support (if any) NIH NHLBI R01 HL161253-01A1

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