Pilot study: AI-enhanced COMISA diagnosis in a community hospital using objective sleep and HRV metrics in Hong Kong

Abstract

<bold>Introduction:</bold> This study explored the feasibility of leveraging the AI-powered wearable (Belun Ring [BR]) to facilitate comorbid insomnia and obstructive sleep apnea (COMISA) diagnosis and personalized treatment. <bold>Methods:</bold> Subjects were co-screened by clinical psychologist and pulmonologist, and were categorized into the OSA group if PSG-AHI ≥ 15 events/h, insomnia if Insomnia Severity Index (ISI) ≥ 15 or diagnosed based on the International Classification of Sleep Disorders, third edition (ICSD-3), or COMISA if meeting both conditions. All subjects completed a 3-night (≥ 100 mins/night) sleep assessment using the BR. Kruskal-Wallis tests and PCA analyses were conducted. <bold>Results:</bold> 31 subjects had insomnia, 13 had OSA, and 10 had COMISA. There were marked discrepancies between subjective and AI-generated sleep statistics (concordance rate= 0.07). COMISA exhibited the highest wake after sleep onset (median, mins) compared to insomnia and OSA (76.5 vs. 48.1 vs. 21.2, P<0.001). COMISA and insomnia had upregulated wakefulness (median, count) compared to OSA (14.9 vs. 15.2 vs. 8.3, P<0.05). COMISA exhibited the lowest SDNN (median, ms) and RMSSD (median, ms) compared to insomnia and OSA (SDNN: 49.0 vs. 63.8 vs. 92.9, P<0.01; RMSSD: 44.9 vs. 53.8 vs. 79.8, P<0.01). COMISA had dropped PRR50 (median, %) compared to OSA (6.6 vs. 24.8, P<0.05). Normalized LF and LF/HF did not differ across the three groups. The novel workflow reduced waiting time for consultation from 104 to 45 weeks. <bold>Conclusion:</bold> This novel study elucidated the potential how AI can assist to identify COMISA patients even in monodisciplinary settings for better patient care.

Ähnliche Arbeiten