Abstract
BACKGROUND: Obstructive sleep apnea (OSA) is highly prevalent among patients with type 2 diabetes mellitus (T2DM) in China, but few patients with clinical symptoms of OSA are referred for diagnostic polysomnography (PSG). Thus, this study aimed to develop and validate an easy-to-use nomogram that predicts the severity of OSA in patients with T2DM. METHODS: This retrospective study included consecutive patients with T2DM admitted to the Endocrinology Department, Third Affiliated Hospital of Soochow University between January 1, 2016 and December 31, 2019. OSA was diagnosed with PSG. Participants were randomly assigned to a training cohort (70%) and a validation cohort (30%). Demographic, anthropometric, and biochemical data were collected. A least absolute shrinkage and selection operator (LASSO) regression model was used to reduce data dimensionality and identify factors for inclusion in the nomogram (training cohort). Nomogram validation was performed in the validation cohort. RESULTS: The study included 280 participants in the training group and 118 participants in the validation group. OSA prevalence was 58.5%. LASSO regression identified waist-to-hip ratio (WHR), smoking status, body mass index (BMI), serum uric acid (UA), the homeostasis model assessment insulin resistance index (HOMA-IR), and history of fatty liver disease as predictive factors for inclusion in the nomogram. Discrimination and calibration in the training group (C-index =0.88) and validation group (C-index =0.881) were good. The nomogram identified patients with T2DM at risk for OSA with an area under the curve of 0.851 [95% confidence interval (CI), 0.788-0.900]. CONCLUSIONS: Our nomogram could be used to facilitate individualized prediction of OSA risk in patients with T2DM and help prioritize patients for diagnostic PSG.