Background: Obstructive sleep apnea (OSA) is highly prevalent in bariatric surgery patients and can lead to potential perioperative risks, but some screening tools lack adequate performance in this population. Thus, we aimed to develop and validate a clinical nomogram for predicting OSA in bariatric surgery candidates.
Methods: We retrospectively collected the data of 482 bariatric surgery patients between September 2015 and January 2020. Patients were randomly classified into training cohort (n=338) and validation cohort (n=144). The Lasso regression was used to select potentially relevant features; then, multivariable logistic regression analysis was used to establish the nomogram. Discrimination, calibration and clinical usefulness of the nomogram were assessed using the C-index, calibration curve and decision curve analysis (DCA).
Results: The overall prevalence of OSA was 71.0% and higher in males (88.2%) compared to females (60.1%). Of these, 26.1% had mild OSA, 14.9% had moderate OSA, and 44.8% had severe OSA. The nomogram consisted of gender, habitual snoring, type 2 diabetes mellitus (T2DM), neck circumference, body mass index (BMI) and age. The nomogram provided favorable discrimination, with a C-indexes for the training and validation cohort of 0.856 (95% CI: 0.816-0.897) and 0.829 (95% CI: 0.76-0.895), respectively, and good calibration. The DCA displayed that the nomogram was clinically useful.
Conclusion: We established a concise and practical nomogram that could facilitate the preoperative individualized prediction of OSA before bariatric surgery, which may help clinicians select bariatric surgery patients with high-risk OSA for polysomnography (PSG).
Keywords: bariatric surgery; nomogram; obesity; obstructive sleep apnea; predictors.
© 2021 Chen et al.