Artificial Intelligence-guided Total Opacity Scores and Obstructive Sleep Apnea in Adults with COVID-19 Pneumonia

Abstract

Objective: We previously demonstrated that artificial intelligence (AI)-directed chest computed tomography (CT)-based total opacity scores (TOS) are associated with high-risk obstructive sleep apnea (OSA) based on the Berlin Questionnaire. In the current study, we examined the association between TOS severity and OSA severity based on polysomnography (PSG) recordings among participants with a history of Coronavirus disease-2019 (COVID-19) infection.

Material and methods: This was a post-hoc analysis of 56 patients who underwent CT imaging after being diagnosed with COVID-19 pneumonia as well as overnight PSG for a validation study with a median of 406 days after the initial COVID-19 onset. The AI software quantified the overall opacity scores, which included consolidation and ground-glass opacity regions on CT scans. TOS was defined as the volume of high-opacity regions divided by the volume of the entire lung, and severe TOS was defined as the score ≥15. OSA was defined as an apnea-hypopnea index (AHI) of at least 15 events/h.

Results: In total, 21 participants had OSA and 35 had no OSA. The median TOS was 10.5 [interquartile range (IQR) 1.6-21.2] in the OSA group and 2.8 (IQR 1.4-9.0) in the non-OSA group (P = 0.047). In a multivariate logistic regression analysis, OSA, AHI, and oxygen desaturation index were associated with severe TOS (P < 0.05 for all, respectively) adjusted for age, sex, body mass index, and hypertension.

Conclusion: AI-directed CT-based TOS severity in patients with COVID-19 pneumonia was associated with OSA severity based on PSG recordings. These results support our previous findings suggesting an association between questionnaire-based high-risk OSA and worse outcomes in COVID-19 pneumonia.