Cardiovascular disease (CVD) is a common comorbidity associated with chronic obstructive pulmonary disease (COPD), but few studies have been conducted to identify CVD risk in COPD patients. This study was to develop a predictive model of CVD in COPD patients based on the National Health and Nutrition Examination Survey (NHANES) database. A total of 3,226 COPD patients were retrieved from NHANES 2007–2012, dividing into the training (n = 2351) and testing (n = 895) sets. The prediction models were conducted using the multivariable logistic regression and random forest analyses, respectively. Receiver operating characteristic (ROC) curves, area under the curves (AUC) and internal validation were used to assess the predictive performance of models. The logistic regression model for predicting the risk of CVD was developed regarding age, gender, body mass index (BMI), high-density lipoprotein (HDL), glycosylated hemoglobin (HbA1c), family history of heart disease, and stayed overnight in the hospital due to illness last year, which the AUC of the internal validation was 0.741. According to the random forest analysis, the important variables-associated with CVD risk were screened including smoking (NNAL and cotinine), HbA1c, HDL, age, gender, diastolic blood pressure, poverty income ratio, BMI, systolic blood pressure, and sedentary activity per day. The AUC of the internal validation was 0.984, indicating the random forest model for predicting the CVD risk in COPD cases was superior to the logistic regression model. The random forest model performed better predictive effectiveness for the cardiovascular risk among COPD patients, which may be useful for clinicians to guide the clinical practice.

中文翻译：

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慢性阻塞性肺疾病患者心血管风险的预测：对国家健康和营养检查调查数据库的研究

心血管疾病 (CVD) 是与慢性阻塞性肺疾病 (COPD) 相关的常见合并症，但很少有研究确定 COPD 患者的 CVD 风险。本研究旨在基于国家健康和营养检查调查 (NHANES) 数据库开发 COPD 患者 CVD 的预测模型。从 NHANES 2007-2012 中总共检索到 3,226 名 COPD 患者，分为训练集（n = 2351）和测试集（n = 895）。预测模型分别使用多变量逻辑回归和随机森林分析进行。接受者操作特征 (ROC) 曲线、曲线下面积 (AUC) 和内部验证用于评估模型的预测性能。用于预测 CVD 风险的逻辑回归模型是针对年龄、性别、体重指数（BMI）、高密度脂蛋白（HDL）、糖化血红蛋白（HbA1c）、心脏病家族史，去年因病住院过夜，内部验证AUC为0.741。根据随机森林分析，筛选出与 CVD 风险相关的重要变量，包括吸烟（NNAL 和可替宁）、HbA1c、HDL、年龄、性别、舒张压、贫困收入比、BMI、收缩压和久坐活动每天。内部验证的AUC为0.984，表明预测COPD病例CVD风险的随机森林模型优于逻辑回归模型。随机森林模型对 COPD 患者的心血管风险具有更好的预测效果，