Development and validation of a clinlabomics-based machine-learning model for noninvasive risk stratification of moderate-to-severe OSA.

PURPOSE: Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder strongly associated with adverse cardiometabolic and neurocognitive outcomes. Polysomnography (PSG), the diagnostic gold standard, is not a readily accessible test. Therefore, accurate risk stratification tools independent of PSG are critically needed to optimize clinical triage and timely intervention. METHODS: We retrospectively analyzed 408 patients who underwent PSG and routine clinical evaluation at the Second Xiangya Hospital (2018-2025). Seventy-one demographic, anthropometric, hematologic, and biochemical variables were screened. After imputation and collinearity adjustment, univariate logistic regression and LASSO regression identified eight key predictors. Nine machine learning (ML) algorithms were trained and internally validated (7:3 split). Model performance was assessed using discrimination, calibration, and decision curve analyses. Interpretability was evaluated with SHAP values. RESULTS: Among the 408 participants, 276 (67.6%) had moderate-to-severe OSA (AHI ≥ 15). Age, BMI, neck circumference, Mallampati classification, glucose, fibrinogen, AST/ALT ratio, and anion gap were independent predictors. Among ML models, XGBoost achieved the best performance (AUC: 0.852 training, 0.861 validation). Calibration and decision analyses confirmed clinical utility. SHAP identified anion gap, BMI, and FIB as dominant contributors. CONCLUSIONS: We developed and validated an interpretable XGBoost model using routinely available anthropometric and laboratory data for risk stratification of moderate-to-severe OSA. This model, implemented as an online tool, may enable clinical risk stratification and triage for high-risk individuals (particularly patients undergoing PSG evaluation), optimize PSG resource allocation, and support timely targeted intervention.