Predicting obstructive sleep apnea among women candidates for bariatric surgery.

Background: More women than men pursue bariatric surgery for treatment of obesity. Untreated obstructive sleep apnea (OSA) in bariatric patients increases perioperative morbidity and mortality, and, therefore, most bariatric surgeons screen for OSA with polysomnography (PSG). We sought to develop a model for predicting OSA in women seeking bariatric surgery in order to use this diagnostic resource most efficiently.

Methods: We identified 296 women who had PSG in preparation for bariatric surgery. Regression and logistic regression analyses were used to assess the relationship between history and physical examination findings and OSA severity. After developing best statistical models, we constructed a summary index to identify patients exceeding clinical thresholds for mild (apnea-hypopnea index [AHI] ≥ 5) and moderate to severe disease (AHI ≥ 15).

Results: In our sample, most women (86%) had OSA, and more than half (53%) had moderate to severe disease. Multiple logistic regression showed that age, body mass index (BMI), neck circumference, hypertension, witnessed apneas, and snoring predicted AHI. Diabetes mellitus and daytime sleepiness measured with the Epworth Sleepiness Scale (ESS) were not significant predictors of OSA. Prediction models were statistically significant but had poor specificity for predicting OSA severity.

Conclusions: OSA is highly prevalent in symptomatic and asymptomatic women planning bariatric surgery for obesity. Best prediction models based on clinical characteristics did not predict disease severity under conditions superior to those in which they might be applied. In light of the perioperative risks associated with OSA in bariatric patients, all women considering bariatric surgery for obesity should be evaluated for OSA with PSG.