Rou Zhang, Zhijuan Liu, Ran Li, Li Ai, Yongxia Li

Department of Respiratory Medicine and Critical Care Medicine, The Second Affiliated Hospital of Kunming Medical University, Kunming, People’s Republic of China

Correspondence: Li Ai, Email 228878270@qq.com Yongxia Li, Email liyongxia0720@126.com

Purpose: Pulmonary hypertension (PH) is a common cardiovascular complication of obstructive sleep apnea (OSA), posing a significant threat to the health and life of patients with OSA. However, no clinical prediction model is currently available to evaluate the risk of PH in OSA patients. This study aimed to develop and validate a nomogram for predicting PH risk in OSA patients.
Patients and Methods: We collected medical records of OSA patients diagnosed by polysomnography (PSG) from January 2016 to June 2024. Transthoracic echocardiography (TTE) was performed to evaluate PH. A total of 511 OSA patients were randomly divided into training and validation sets for model development and validation. Potential predictive factors were initially screened using univariate logistic regression and Lasso regression. Independent predictive factors for PH risk were identified via multivariate logistic regression, and a nomogram model was constructed. Model performance was assessed in terms of discrimination, calibration, and clinical applicability.
Results: Eight independent predictive factors were identified: age, recent pulmonary infection, coronary atherosclerotic heart disease (CHD), apnea-hypopnea index (AHI), mean arterial oxygen saturation (MSaO2), lowest arterial oxygen saturation (LSaO2), alpha-hydroxybutyrate dehydrogenase (α-HBDH), and fibrinogen (FIB). The nomogram model demonstrated good discriminative ability (AUC = 0.867 in the training set, AUC = 0.849 in the validation set). Calibration curves and decision curve analysis (DCA) also indicated good performance. Based on this model, a web-based nomogram tool was developed.
Conclusion: We developed and validated a stable and practical web-based nomogram for predicting the probability of PH in OSA patients, aiding clinicians in identifying high-risk patients for early diagnosis and treatment.

Keywords: obstructive sleep apnea, pulmonary hypertension, clinical prediction model, nomogram, risk factor