Abstract
OBJECTIVE: To develop and validate a clinical prediction model for olfactory dysfunction in patients with obstructive sleep apnea-hypopnea syndrome (OSAHS), evaluating the combined predictive value of polysomnography (PSG) parameters and clinical symptoms. METHODS: We retrospectively analyzed 546 OSAHS patients, including 420 from the Affiliated Hospital of North Sichuan Medical College were [randomly divided into training (n = 294) and internal validation (n = 126) sets], and 126 from the Sixth People's Hospital of Chengdu (external validation set). All patients underwent overnight PSG for sleep parameter assessment and Sniffin' Sticks tests for olfactory evaluation. Predictors were selected using LASSO regression with subsequent logistic regression modeling, followed by nomogram construction. Model performance was assessed through ROC analysis, calibration curves and DCA curves. RESULTS: Among 546 enrolled patients, with OSAHS were included in this study. The overall olfactory dysfunction incidence was 38.64% (211/546). Multivariable analysis identified seven independent predictors: gender, age, AHI, N3%, REM%, TS90%, and MoCA. The predictive efficacy AUC of the training set model was 0.832 (95% CI: 0.784-0.880); good calibration (slope = 0.89, Hosmer-Lemeshow P = 0.41); and clinical utility across threshold probabilities of 0.06-0.97. CONCLUSION: Our prediction model constructed based on gender, age, AHI, N3%, REM%, TS90%, and MoCA can effectively identify OSAHS patients at high risk for olfactory dysfunction. With robust discrimination and calibration, this tool provides a clinically useful, non-invasive method for early risk stratification and intervention planning.