Stratifying the Risk of Cardiovascular Disease in Obstructive Sleep Apnea Using Machine Learning

Objectives/Hypothesis: Obstructive sleep apnea (OSA) is associated with higher risk of morbidity and mortality related to cardiovascular disease (CVD). Due to overlapping clinical risk factors, identifying high-risk patients with OSA who are likely to develop CVD remains challenging. We aimed to identify baseline clinical factors associated with the future development of CVD in patients with OSA. Study Design: Retrospective analysis of prospectively collected data. Methods: We performed a retrospective analysis of 967 adults aged 45 to 84 years and enrolled in the Multi-Ethnic Study of Atherosclerosis. Six machine learning models were created using baseline clinical factors initially identified by stepwise variable selection. The performance of these models for the prediction of additional risk of CVD in OSA was calculated. Additionally, these models were evaluated for interpretability using locally interpretable model-agnostic explanations. Results: Of the 967 adults without baseline OSA or CVD, 116 were diagnosed with OSA and CVD and 851 with OSA alone 10 years after enrollment. The best performing models included random forest (sensitivity 84%, specificity 99%, balanced accuracy 91%) and bootstrap aggregation (sensitivity 84%, specificity 100%, balanced accuracy 92%). The strongest predictors of OSA and CVD versus OSA alone were fasting glucose >91 mg/dL, diastolic pressure >73 mm Hg, and age >59 years. Conclusion: In the selected study population of adults without OSA or CVD at baseline, the strongest predictors of CVD in patients with OSA include fasting glucose, diastolic pressure, and age. These results may shape a strategy for cardiovascular risk stratification in patients with OSA and early intervention to mitigate CVD-related morbidity. Level of Evidence: 3 Laryngoscope, 132:234–241, 2022.