A Nomogram for Predicting Cardiovascular Diseases in Chronic Obstructive Pulmonary Disease Patients
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1Department of Respiration, Fu Xing Hospital, Capital Medical University, Beijing, China.
Insights
Cardiovascular diseases (CVDs) are common in chronic obstructive pulmonary disease (COPD) patients. This study identified key predictors like male gender, age, smoking history, and specific blood cell counts to build a model predicting CVD risk in COPD.
Area of Science:
- Cardiology
- Pulmonology
- Medical Informatics
Background:
- Cardiovascular diseases (CVDs) are prevalent comorbidities in patients with chronic obstructive pulmonary disease (COPD).
- These comorbidities significantly increase risks of hospitalization, prolonged hospital stays, and mortality among COPD patients.
- Identifying predictors for CVDs in COPD is crucial for risk stratification and management.
Purpose of the Study:
- To identify key predictors associated with cardiovascular diseases (CVDs) in patients diagnosed with chronic obstructive pulmonary disease (COPD).
- To develop and validate a predictive model for CVD risk in COPD patients using identified predictors.
Main Methods:
- A cross-sectional study utilizing data from 1022 chronic obstructive pulmonary disease (COPD) patients in the National Health and Nutrition Examination Surveys (NHANES).
- Patients were divided into training (n=709) and testing (n=313) sets for model development and validation.
- Univariate and multivariable analyses were performed to identify predictors; model performance was assessed using AUC, accuracy, sensitivity, specificity, NPV, PPV, and calibration.
Main Results:
- Predictors associated with increased CVD risk in COPD patients included male gender, older age, smoking history, overweight status, history of blood transfusion, family history of heart disease, and higher white blood cell (WBC) and monocyte (MONO) counts.
- Higher platelet (PLT) and lymphocyte (LYM) levels were associated with a reduced risk of CVDs.
- The developed prediction model, incorporating gender, age, smoking history, BMI, blood transfusion history, family heart disease history, WBC, MONO, PLT, and LYM, demonstrated strong predictive performance with an AUC of 0.75 in the training set and 0.79 in the testing set.
Conclusions:
- A robust prediction model for cardiovascular disease risk in chronic obstructive pulmonary disease (COPD) patients has been successfully established.
- The model effectively utilizes clinical and laboratory parameters, including demographic factors, medical history, and specific blood cell counts, to predict CVD risk.
- This tool shows significant potential for improving risk assessment and guiding clinical management strategies for COPD patients at risk of cardiovascular complications.
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.
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