A risk-predictive model for obstructive sleep apnea in patients with chronic obstructive pulmonary disease

Tianfeng Peng; Shan Yuan; Wenjing Wang; Zhuanyun Li; Ayshat Mussa Jumbe; Yaling Yu; Zhenghao Hu; Ruijie Niu; Xiaorong Wang; Jinnong Zhang

doi:10.3389/fnins.2023.1146424

A risk-predictive model for obstructive sleep apnea in patients with chronic obstructive pulmonary disease

Front Neurosci. 2023 Mar 17:17:1146424. doi: 10.3389/fnins.2023.1146424. eCollection 2023.

Authors

Tianfeng Peng¹, Shan Yuan², Wenjing Wang^{3

4

5

6}, Zhuanyun Li¹, Ayshat Mussa Jumbe¹, Yaling Yu¹, Zhenghao Hu¹, Ruijie Niu¹, Xiaorong Wang⁷, Jinnong Zhang¹

Affiliations

¹ Department of Emergency Medicine, Tongji Medical College, Union Hospital, Huazhong University of Science and Technology, Wuhan, China.
² Department of Emergency Medicine, Renmin Hospital of Wuhan University, Wuhan, China.
³ Department of Critical Care Medicine, Henan Provincial People's Hospital, Zhengzhou, China.
⁴ Department of Critical Care Medicine, Henan University People's Hospital, Zhengzhou, China.
⁵ Henan Key Laboratory for Critical Care Medicine, Zhengzhou, China.
⁶ Department of Critical Care Medicine, Zhengzhou University People's Hospital, Zhengzhou, China.
⁷ Department of Respiratory and Critical Care Medicine, Tongji Medical College, Union Hospital, Huazhong University of Science and Technology, Wuhan, China.

Abstract

Background: Obstructive sleep apnea syndrome (OSA) is increasingly reported in patients with chronic obstructive pulmonary disease (COPD). Our research aimed to analyze the clinical characteristics of patients with overlap syndrome (OS) and develop a nomogram for predicting OSA in patients with COPD.

Methods: We retroactively collected data on 330 patients with COPD 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. The 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 patients with COPD were enrolled in this study, with 96 patients (29.1%) confirmed with OSA. Patients were randomly divided into the training group (70%, n = 230) and the 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 (mMRC) dyspnea scale (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 the validation group. The DCA showed excellent clinical practicability.

Conclusion: We established a concise and practical nomogram that will benefit the advanced diagnosis of OSA in patients with COPD.

Keywords: chronic obstructive pulmonary disease; nomogram; overlap syndrome; risk factors; sleep apnea syndrome.