Predicting sleep apnea in bariatric surgery patients.
BACKGROUND: Because of the high prevalence and potentially serious complications of obstructive sleep apnea (OSA) in obese individuals, several prediction models have been developed to detect moderate-to-severe OSA in patients undergoing bariatric surgery. Using commonly collected variables (body mass index [BMI], age, observed sleep apnea, hemoglobin A1c, fasting plasma insulin, gender, and neck circumference), Dixon et al. developed a model with a sensitivity of 89% and specificity of 81% for patients undergoing laparoscopic adjustable gastric band surgery suspected to have OSA. The present study evaluated the prediction model of Dixon et al. in 310 gastric bypass patients (mean BMI 46.8 kg/m(2), age 41.6 years, 84.5% women), with no preselection for OSA symptoms in a bariatric surgery partnership. METHODS: The patients underwent overnight limited polysomnography to determine the presence and severity of OSA as measured using the apnea-hypopnea index. RESULTS: Of the 310 patients, 44.2% had moderate-to-severe OSA (apnea-hypopnea index ≥ 15/h). Most variables in the Dixon model were associated with a greater prevalence of OSA. The sensitivity (75%) and specificity (57%) for the model-based classification of OSA were considerably lower in the present sample than originally reported. An alternate prediction model identified 10 unique predictors of OSA. The presence of ≥ 5 of these predictors modestly improved the sensitivity (77%) and greatly improved the specificity (77%) in predicting an apnea-hypopnea index of ≥ 15/h. When applied to the validation sample, the sensitivity (76%) and specificity (72%) were essentially the same. CONCLUSION: Although the Dixon model and our model included overlapping predictors (BMI, gender, age, neck circumference), when applied in our sample of gastric bypass patients, neither model achieved the sensitivity and specificity for predicting OSA previously reported by Dixon et al.
Kolotkin, RL; LaMonte, MJ; Walker, JM; Cloward, TV; Davidson, LE; Crosby, RD
Volume / Issue
Start / End Page
Pubmed Central ID
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)