Adherence to positive airway pressure therapy in patients with Down syndrome: assessing cloud-based monitoring data
摘要
Obstructive sleep apnea is common in Down syndrome (DS) with many patients prescribed positive airway pressure (PAP) therapy. This study evaluates PAP adherence and identifies factors influencing adherence.
Methods:Retrospective analysis of electronic health records and cloud-based PAP therapy data from patients with DS at Rady Children’s Hospital, San Diego, CA. Cloud data were evaluated cross-sectionally at 30- and 90-night post-clinic visit and longitudinally in patients with ≥ 2 90-night data downloads. Outcomes included adherence (percentage of nights with ≥ 4 hours of use) and usage (percentage of nights with any PAP usage). The impact of demographic and PAP therapy factors (eg, mask leak) on these outcomes was also assessed.
Results:Forty-seven patients with DS with cloud-based PAP therapy data over a 90-night period and 46 over a 30-night period were analyzed. The mean age was 17.7 ± 4.6 years (21 females). Median adherence was significantly higher at 30 nights (56.7%, interquartile range: 0.0, 90.8%) than at 90 nights (34.4%, interquartile range: 0.0, 86.7%) (P < .05). Median usage did not differ between the 30-night and 90-night periods. Demographic characteristics and PAP therapy parameters were not associated with adherence or usage. Among the longitudinal cohort (n = 32), median adherence was 69.7% (interquartile range: 19.2, 90.0%), and median usage was 78.2% (interquartile range: 45.2, 95.7%). Compared to an age- and sex-matched cohort without DS, patients with DS demonstrated higher PAP adherence (P < .05).
Conclusions:Cross-sectional and longitudinal analyses reveal that many patients with DS successfully adhere to PAP therapy, challenging the misconception that they struggle with adherence and proving they may be as successful, if not more, than non-DS patients.
Citation:Bhattacharjee R, Warner M, Nokes B, et al. Adherence to positive airway pressure therapy in patients with Down syndrome: assessing cloud-based monitoring data. J Clin Sleep Med. 2025;21(4):675–681.