中国中老年COPD人群合并高血压病风险模型构建及验证

Construction and Validation of a Risk Model for Hypertension in Middle-Aged and Elderly Chinese COPD Patients

摘要: 目的：构建并验证中国中老年慢性阻塞性肺疾病(COPD)人群合并高血压病的风险预测模型，为临床早期筛查与个体化干预提供依据。方法：基于2011年中国健康与养老追踪调查(CHARLS)数据库基线数据，纳入年龄 ≥ 45岁COPD患者1763例，以是否合并高血压为结局变量，采用单因素与多因素Logistic回归筛选独立危险因素，建立风险预测模型，通过ROC曲线、校准曲线、决策曲线分析(DCA)评价模型区分度、校准度与临床实用性；并纳入康复大学青岛医院1000例COPD患者进行外部验证。结果：COPD患者中合并高血压病患病率为47.70% (841/1763)，多因素分析结果显示，年龄、体重、BMI水平、甘油三酯、尿酸、PEF、冠心病史、糖尿病史与COPD患者合并高血压病的发生密切相关(均P < 0.05)。建模组曲线下面积为0.690，校准曲线显示模型一致性良好；外部验证组曲线下面积为0.665，DCA提示建模组及验证组在较宽阈值范围内具有一定的临床净获益。结论：本研究构建的风险预测模型区分度中等(AUC ≈ 0.69)，可作为初步风险筛查工具，为临床COPD患者合并高血压病风险评估提供一定参考。但模型预测精度有限，且受限于横断面设计及变量选择，尚不足以替代临床综合判断，当前成果仍属探索性质。

Abstract: Objective: To develop and validate a risk prediction model for hypertension in middle-aged and elderly Chinese patients with chronic obstructive pulmonary disease (COPD), and to provide a basis for early clinical screening and individualized intervention. Methods: Based on baseline data from the 2011 China Health and Retirement Longitudinal Study (CHARLS), a total of 1763 COPD patients aged ≥ 45 years were included, with hypertension as the outcome variable. Independent risk factors were identified using univariate and multivariate logistic regression analyses, which were then used to construct the prediction model. The model’s discrimination, calibration, and clinical utility were evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). External validation was performed in a separate cohort of 1000 COPD patients from Qingdao Hospital of Rehabilitation University. Results: The prevalence of hypertension among COPD patients was 47.70% (841/1763). Multivariate analysis revealed that age, weight, body mass index (BMI), triglyceride levels, uric acid levels, peak expiratory flow (PEF), history of coronary heart disease, and history of diabetes were significantly associated with hypertension comorbidity in COPD patients (all P < 0.05). The area under the ROC curve (AUC) for the modeling group was 0.690, and the calibration curve indicated good model fit. The AUC for the external validation group was 0.665. DCA indicated that both the modeling and validation groups provided clinical net benefit within specific threshold probability ranges. Conclusion: The prediction model demonstrated moderate discrimination (AUC ≈ 0.69) and may serve as an initial screening aid for risk assessment of hypertension in COPD patients. However, the model’s predictive accuracy is modest, and its utility is constrained by the cross-sectional design and the limited set of candidate predictors. It is therefore not a substitute for comprehensive clinical judgment and should be considered exploratory in nature.