Obstructive Sleep Apnea(OSA) is a chronic condition that affects 1 billion people worldwide. Apnea Hypopnea Index(AHI) is the clinical gold standard to measure the severity of OSA. This study highlights limitations in the apnea-hypopnea index as a predictor for obstructive sleep apnea (OSA) outcomes. Instead, a physiology-guided machine learning (ML) approach was developed using features from ventilatory, hypoxic, and arousal domains, based on polysomnography data from the Sleep Heart Health Study (SHHS). The ML model demonstrated superior predictive performance for all-cause mortality (AUROC-0.93) and daytime sleepiness (AUROC-0.81) compared to AHI. Explainable AI techniques, such as SHAP analysis, provided insights into feature importance, offering a clinically interpretable and scalable tool for OSA outcome prediction.

The authors have declared no competing interest.

This study was funded by R21HL165320 and R01HL171813

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The dataset used in this study was obtained exclusively from the Sleep Heart Health Study (SHHS) hosted on the National Sleep Research Resource (NSRR) platform. This dataset is publicly available and can be accessed at https://sleepdata.org/.

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The dataset used in this study was obtained exclusively from the Sleep Heart Health Study (SHHS) hosted on the National Sleep Research Resource (NSRR) platform. This dataset is publicly available and can be accessed at https://sleepdata.org/.

https://sleepdata.org/datasets/shhs