Development and Validation of a Nomogram for Predicting Obstructive Sleep Apnea in Patients with Pulmonary Arterial Hypertension

Abstract

Meixi Hu,1,* Anqi Duan,1,* Zhihua Huang,1,* Zhihui Zhao,1 Qing Zhao,1 Lu Yan,1 Yi Zhang,1 Xin Li,1 Qi Jin,2 Chenhong An,1 Qin Luo,1 Zhihong Liu1 1Center for Respiratory and Pulmonary Vascular Disease, Department of Cardiology, Fuwai Hospital, National Clinical Research Center for Cardiovascular Diseases, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People’s Republic of China; 2Department of Cardiology, Shanghai Institute of Cardiovascular Disease, Zhongshan Hospital, Fudan University, Shanghai, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qin Luo; Zhihong Liu, Center for Respiratory and Pulmonary Vascular Disease, Department of Cardiology, Fuwai Hospital, National Clinical Research Center for Cardiovascular Diseases, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences, and Peking Union Medical College, No. 167 Beilishi Road, Xicheng District, Beijing, 100037, People’s Republic of China, Tel/Fax [phone], Email [email protected]; [email protected]: Patients with pulmonary arterial hypertension (PAH) are at high risk for obstructive sleep apnea (OSA), which may adversely affect pulmonary hemodynamics and long-term prognosis. However, there is no clinical prediction model to evaluate the probability of OSA among patients with PAH. Our study aimed to develop and validate a nomogram for predicting OSA in the setting of PAH.Patients and Methods: From May 2020 to November 2021, we retrospectively analyzed the medical records of 258 patients diagnosed with PAH via right-heart catheterization. All participants underwent overnight cardiorespiratory polygraphy for OSA assessment. General clinical materials and biochemical measurements were collected and compared between PAH patients with or without OSA. Lasso regression was performed to screen potential predictors. Multivariable logistic regression analysis was conducted to establish the nomogram. Concordance index, calibration curve, and decision curve analysis were used to determine the discrimination, calibration, and clinical usefulness of the nomogram.Results: OSA was present in 26.7% of the PAH patients, and the prevalence did not differ significantly between male (29.7%) and female (24.3%) patients. Six variables were selected to construct the nomogram, including age, body mass index, hypertension, uric acid, glycated hemoglobin, and interleukin-6 levels. Based on receiver operating characteristic analysis, the nomogram demonstrated favorable discrimination accuracy with an area under the curve (AUC) of 0.760 for predicting OSA, exhibiting a better predictive value in contrast to ESS (AUC = 0.528) (P < 0.001). Decision curve analysis and clinical impact curve analysis also indicated the clinical utility of the nomogram.Conclusion: By establishing a comprehensive and practical nomogram, we were able to predict the presence of OSA in patients with PAH, which may facilitate the early identification of patients that benefit from further diagnostic confirmation and intervention.Keywords: Epworth sleepiness scale, pulmonary hypertension, biomarkers, clinical prediction model

Keywords

• epworth sleepiness scale
• pulmonary hypertension
• biomarkers
• clinical prediction model