A Nomogram for Predicting Cardiovascular Diseases in Chronic Obstructive Pulmonary Disease Patients.

Abstract

Cardiovascular diseases (CVDs) are the most common comorbidities in the chronic obstructive pulmonary disease (COPD), which increase the risk of hospitalization, length of stay, and death in COPD patients. This study aimed to identify the predictors for CVDs in COPD patients and construct a prediction model based on these predictors. In total, 1022 COPD patients in National Health and Nutrition Examination Surveys (NHANES) were involved in the cross-sectional study. All subjects were randomly divided into the training set (n = 709) and testing set (n = 313). The differences before and after the manipulation of the missing data were compared via sensitivity analysis. Univariate and multivariable analyses were employed to screen the predictors of CVDs in COPD patients. The performance of the prediction model was evaluated via the area under the curve (AUC), accuracy, sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), and calibration. Subgroup analysis was performed in patients using different COPD diagnosis methods and patients smoking or not smoking in the testing set. We found that male, older age, a smoking history, overweight, a history of blood transfusion, a history of heart disease in close relatives, higher levels of white blood cell (WBC), and monocyte (MONO) were associated with the increased risk of CVDs in COPD patients. Higher levels of platelets (PLT) and lymphocyte (LYM) were associated with reduced risk of CVDs in COPD patients. A prediction model for the risk of CVDs in COPD patients was established based on predictors including gender, age, a smoking history, BMI, a history of blood transfusion, a history of heart disease in close relatives, WBC, MONO, PLT, and LYM. The AUC value of the prediction model was 0.75 (95% CI: 0.71–0.79) in the training set and 0.79 (95%CI: 0.73–0.85) in the testing set. The prediction model established showed good predictive performance in predicting CVDs in COPD patients.