Guo-Qiang Song1,2,3, De-Lu Wang1,2, Hua-Man Wu1,2, Qiao-Jun Wang1,2, Fei Han2, Guo-Qiang Hu4, Rui Chen5,6. 1. Department of Respiratory Medicine, The Second Affiliated Hospital of Soochow University, Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, Jiangsu, China. 2. Sleep Center, The Second Affiliated Hospital of Soochow University, Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, Jiangsu, China. 3. Department of Respiratory Medicine, Changxing County Hospital of Traditional Chinese Medicine, No. 99Changlv Road, Huzhou, 313000, Zhejiang, China. 4. Department of Respiratory Medicine, Changxing County Hospital of Traditional Chinese Medicine, No. 99Changlv Road, Huzhou, 313000, Zhejiang, China. changxinghgq@163.com. 5. Department of Respiratory Medicine, The Second Affiliated Hospital of Soochow University, Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, Jiangsu, China. chenruigood@126.com. 6. Sleep Center, The Second Affiliated Hospital of Soochow University, Soochow University, No. 1055, Sanxiang Road, Suzhou, 215004, Jiangsu, China. chenruigood@126.com.
Abstract
BACKGROUND AND OBJECTIVE: The diagnosis of obstructive sleep apnea (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. METHODS: The effectiveness of using body mass index (BMI) and a new visual prediction model to screen for OSA was evaluated using a development set (1769 participants) and confirmed using an independent validation set (642 participants). RESULTS: 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. CONCLUSIONS: BMI can be used as an indicator to 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 with OSA among outpatients. Use of this prediction model may help to improve clinical decision-making.
BACKGROUND AND OBJECTIVE: The diagnosis of obstructive sleep apnea (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. METHODS: The effectiveness of using body mass index (BMI) and a new visual prediction model to screen for OSA was evaluated using a development set (1769 participants) and confirmed using an independent validation set (642 participants). RESULTS: 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. CONCLUSIONS: BMI can be used as an indicator to 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 with OSA among outpatients. Use of this prediction model may help to improve clinical decision-making.
Authors: Vishesh K Kapur; Dennis H Auckley; Susmita Chowdhuri; David C Kuhlmann; Reena Mehra; Kannan Ramar; Christopher G Harrod Journal: J Clin Sleep Med Date: 2017-03-15 Impact factor: 4.062
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