A Prediction Model for Obstructive Sleep Apnea in Bariatric Surgery Candidates with Obesity

Area of Science:

• Medical research
• Clinical prediction modeling
• Sleep medicine

Background:

• Obstructive sleep apnea (OSA) is a significant concern in bariatric surgery patients, increasing postoperative complication risks.
• Effective preoperative screening tools for OSA in this population are currently lacking.

Purpose of the Study:

• To develop and validate a predictive model for obstructive sleep apnea (OSA) risk.
• To aid in the preoperative evaluation of bariatric surgery candidates.

Main Methods:

• Retrospective collection of clinical data from 453 bariatric surgery candidates.
• Development of a regression model incorporating identified risk factors (NAFLD, age, CC, SpO2).
• Validation of the model using calibration curves, ROC curves, and decision curve analysis.

Main Results:

• Non-alcoholic fatty liver disease (NAFLD), age, chest circumference (CC), and average SpO2 were identified as independent risk factors for OSA.
• The predictive model demonstrated strong performance with an AUC of 0.88 in the training cohort.
• Calibration curves confirmed good model fit (P > 0.05) in both training and validation sets.

Conclusions:

• A novel prediction model incorporating NAFLD, age, CC, and mean SpO2 offers superior predictive performance for OSA in bariatric surgery candidates.
• This nomogram provides a convenient and cost-effective alternative to polysomnography (PSG) for preoperative screening.
• Further research is needed to explore the NAFLD-OSA relationship and externally validate the model.