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.

Chronic obstructive pulmonary disease and cardiovascular disease: mechanistic links and implications for practice

“…The existing clinical prediction models for CVD do not recognize COPD as a cardiovascular risk factor and thus underestimate the risk of cardiovascular events. Newer clinical prediction, specifically applicable to COPD patients, have been constructed and their broader use should be considered [36]. Because the risk of cardiovascular events is clearly increased in this population, clinicians should be more aggressive about CVD screening in COPD patients than in those without.…”

Optimizing Human Vitality - A Fuzzy Deep Learning Approach for Enhancing Organ Endurance in Healthcare

“…Their work demonstrated the potential of VAEs in healthcare applications. Gupta et al [15] introduced a fuzzy deep learning algorithm for predicting organ endurance in patients with chronic obstructive pulmonary disease (COPD). They integrated fuzzy logic principles into a deep neural network architecture to handle uncertainties and vagueness in COPD-related data, achieving improved accuracy in predicting lung function decline.…”

Evaluation of the Effect of the “General Practitioner-Specialist” Combination in the Community Management of Chronic Obstructive Pulmonary Disease

“…Chronic obstructive pulmonary disease (COPD), abbreviated as COPD, is a common respiratory system disease [1] and a widely prevalent disease [2]. Patients with chronic obstructive pulmonary disease not only have respiratory symptoms, but also involve the cardiovascular system [3], may also experience venous thromboembolism [4], and may also develop anxiety and depression. Therefore, we should pay more attention to the prevention and treatment of chronic obstructive pulmonary disease [5][6].…”