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已发表论文
基于机器学习的老年阻塞性睡眠呼吸暂停患者颈动脉粥样硬化预测的 SHAP 分析：一项多中心研究
Authors Cai WM , Rui D, Zhao LB, Gao YH, Zhao Z, Xue X, Li TJ, Nie TY, Ma Y , Liu L
Received 10 November 2024
Accepted for publication 27 July 2025
Published 29 September 2025 Volume 2025:17 Pages 2351—2367
DOI https://doi.org/10.2147/NSS.S505241
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Prof. Dr. Ahmed BaHammam
Wei-Meng Cai,1 Dong Rui,1 Li-Bo Zhao,2 Ying-Hui Gao,3 Zhe Zhao,2 Xin Xue,1 Tian-Jiao Li,4 Ting-Yu Nie,4 Yao Ma,5 Lin Liu1
1Department of Pulmonary and Critical Care Medicine of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China; 2Cardiology Department of the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China; 3PKU-Upenn Sleep Center, Peking University International Hospital, Beijing, 102206, People’s Republic of China; 4Medical College, Yan’ an University, Yan’ an, Shaanxi, 716000, People’s Republic of China; 5Graduate School, Medical School of Chinese PLA, Beijing, 100853, People’s Republic of China
Correspondence: Lin Liu, Department of Pulmonary and Critical Care Medicine of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28 Fu-Xing Street, Haidian District, Beijing, 100850, People’s Republic of China, Tel +86-13263189578, Email liulin715@qq.com
Background: Carotid atherosclerosis (CAS) is a critical cardiovascular complication in elderly patients with obstructive sleep apnea (OSA). Current risk assessment tools inadequately capture OSA-specific pathophysiological mechanisms for CAS prediction in this high-risk population.
Methods: This multicenter retrospective study included 1196 elderly patients (≥ 60 years) with polysomnography-confirmed OSA from six tertiary hospitals in China. CAS was diagnosed by carotid ultrasound. LASSO (Least Absolute Shrinkage and Selection Operator) regression identified optimal predictive features from 18 candidate variables. Four machine learning algorithms were developed and validated using 5-fold cross-validation. Model interpretability was achieved through SHapley Additive exPlanations (SHAP) analysis.
Results: Among participants, 273 (22.8%) had CAS. LASSO regression selected eight optimal features. XGBoost achieved the best performance with test AUC of 0.854, accuracy of 79.8%, sensitivity of 81.2%, and specificity of 78.5%. SHAP analysis revealed systolic blood pressure (importance: 0.3148) and percentage of sleep time with oxygen saturation < 90% (T90, importance: 0.2660) as the most influential predictors, surpassing traditional apnea-hypopnea index. Other significant predictors included alcohol consumption, mean oxygen saturation, body mass index, age, platelet count, and smoking status.
Conclusion: This study developed the first machine learning-based CAS prediction model for elderly OSA patients, achieving clinically relevant performance (AUC=0.854). The prominence of T90 over conventional apnea-hypopnea index suggests nocturnal hypoxemic burden is more important than respiratory event frequency for cardiovascular risk stratification in this population.
Keywords: carotid atherosclerosis, obstructive sleep apnea, machine learning, SHAP analysis, cardiovascular risk prediction
1Department of Pulmonary and Critical Care Medicine of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China; 2Cardiology Department of the Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China; 3PKU-Upenn Sleep Center, Peking University International Hospital, Beijing, 102206, People’s Republic of China; 4Medical College, Yan’ an University, Yan’ an, Shaanxi, 716000, People’s Republic of China; 5Graduate School, Medical School of Chinese PLA, Beijing, 100853, People’s Republic of China
Correspondence: Lin Liu, Department of Pulmonary and Critical Care Medicine of the Second Medical Center and National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, No. 28 Fu-Xing Street, Haidian District, Beijing, 100850, People’s Republic of China, Tel +86-13263189578, Email liulin715@qq.com
Background: Carotid atherosclerosis (CAS) is a critical cardiovascular complication in elderly patients with obstructive sleep apnea (OSA). Current risk assessment tools inadequately capture OSA-specific pathophysiological mechanisms for CAS prediction in this high-risk population.
Methods: This multicenter retrospective study included 1196 elderly patients (≥ 60 years) with polysomnography-confirmed OSA from six tertiary hospitals in China. CAS was diagnosed by carotid ultrasound. LASSO (Least Absolute Shrinkage and Selection Operator) regression identified optimal predictive features from 18 candidate variables. Four machine learning algorithms were developed and validated using 5-fold cross-validation. Model interpretability was achieved through SHapley Additive exPlanations (SHAP) analysis.
Results: Among participants, 273 (22.8%) had CAS. LASSO regression selected eight optimal features. XGBoost achieved the best performance with test AUC of 0.854, accuracy of 79.8%, sensitivity of 81.2%, and specificity of 78.5%. SHAP analysis revealed systolic blood pressure (importance: 0.3148) and percentage of sleep time with oxygen saturation < 90% (T90, importance: 0.2660) as the most influential predictors, surpassing traditional apnea-hypopnea index. Other significant predictors included alcohol consumption, mean oxygen saturation, body mass index, age, platelet count, and smoking status.
Conclusion: This study developed the first machine learning-based CAS prediction model for elderly OSA patients, achieving clinically relevant performance (AUC=0.854). The prominence of T90 over conventional apnea-hypopnea index suggests nocturnal hypoxemic burden is more important than respiratory event frequency for cardiovascular risk stratification in this population.
Keywords: carotid atherosclerosis, obstructive sleep apnea, machine learning, SHAP analysis, cardiovascular risk prediction