A risk-predictive model for obstructive sleep apnea in patients with chronic obstructive pulmonary disease - Peng, Tianfeng; Yuan, Shan; Wang, Wenjing; Li, Zhuanyun; Jumbe, Ayshat Mussa; Yu, Yaling; Hu, Zhenghao; Niu, Ruijie; Wang, Xiaorong; Zhang, Jinnong (Frontiers in Neuroscience, 2023)
Background: Obstructive sleep apnea syndrome (OSA) is increasingly reported in chronic obstructive pulmonary disease (COPD) patients. Our research aims to analyze overlap syndrome (OS) patients' clinical characteristics, and develop a nomogram for predicting OSA in COPD patients. Methods: We retroactively collected 330 COPD patients treated at Wuhan Union Hospital (Wuhan, China) from March 2017 to March 2022. Multivariate logistic regression was used to select predictors applied to develop a simple nomogram. Area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA) were used to assess the value of the model. Results: A total of 330 consecutive COPD patients were enrolled in this study, with 96 patients (29.1%) were confirmed with OSA. Patients were randomly divided into the training group (70%, n = 230) and validation group (30%, n = 100). Age (odds ratio (OR):1.062, 1.003-1.124), type 2 diabetes (OR:3.166,1.263-7.939), neck circumference (NC) (OR:1.370,1.098-1,709), modified Medical Research Council dyspnea scale (mMRC) (OR:0.503,0.325-0.777), Sleep Apnea Clinical Score (SACS) (OR:1.083,1.004-1.168), and C-reactive protein (CRP) (OR:0.977,0.962-0.993) were identified as valuable predictors used for developing a nomogram. The prediction model performed good discrimination [AUC: 0.928, 95% confidence interval (CI): 0.873–0.984] and calibration in validation group. The DCA showed excellent clinical practicability. Conclusion: We established a concise and practical nomogram that will benefit advance diagnosis of OSA in COPD patients.