Two effective clinical prediction models to screen for obstructive sleep apnoea based on body mass index and other parameters.

Abstract

The diagnosis of obstructive sleepapnea(OSA) relies on polysomnography which is time-consuming and expensive. We therefore aimed to develop two simple, non-invasive models to screen adults for OSA. The effectiveness of using body mass index (BMI) and a new visual prediction model to screenfor OSA was evaluated using a development set (1769 participants) and confirmed using an independent validation set (642 participants). Based on the development set, the best BMI cut-off value for diagnosing OSA was 26.45 kg/m2, with an area under the curve (AUC) of 0.7213 (95% confidence interval (CI), 0.6861-0.7566), a sensitivity of 57% and a specificity of 78%. Through forward conditional logistic regression analysis using a stepwise selection model developed from observed data, seven clinical variables were evaluated as independent predictors of OSA: age, BMI, sex, Epworth Sleepiness Scale score, witnessed apnoeas, dry mouth and arrhythmias. With this new model, the AUC was 0.7991 (95% CI, 0.7668-0.8314) for diagnosing OSA (sensitivity, 75%; specificity, 71%). The results were confirmed using the validation set. A nomogram for predicting OSA was generated based on this new model using statistical software. BMI can be used as an indicatorto screen for OSA in the community. We created an internally validated, highly distinguishable, visual and parsimonious prediction model comprising BMI and other parameters that can be used to identify patients withOSA among outpatients. Use of this prediction model may help to improve clinical decision-making.