Development and validation of a nomogram prediction model for cardiovascular comorbidities in COPD patients based on...

This study aimed to examine the association between hypertension and cardiovascular comorbidities in patients with chronic obstructive pulmonary disease (COPD) and to construct a nomogram for predicting the risk of cardiovascular comorbidities in this population.

This retrospective study included 1,447 patients with chronic obstructive pulmonary disease (COPD) and no pre-existing cardiovascular disease (CVD) from January 2018 to February 2022 at the Second Affiliated Hospital of Guangdong Medical University, with follow-up extending until August 2025. Patients were randomly assigned to a training cohort (n = 1,012) and an internal validation cohort (n = 435) in a 7:3 ratio. Additionally, 624 patients treated at the Affiliated Hospital of Guangdong Medical University between January 2019 and December 2019 were included as an external validation cohort. Variables with non-zero coefficients were first selected using least absolute shrinkage and selection operator (LASSO) regression and were subsequently entered into univariable and multivariable logistic regression analyses. A nomogram was then constructed based on the results of the multivariable logistic regression, and the predictive performance of the nomogram was further evaluated.

The predictors incorporated into the nomogram model were age, diabetes mellitus, hypertension, and edema. The nomogram demonstrated good discriminative performance, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.82 (95% CI: 0.78-0.85) in the training cohort, 0.82 (95% CI: 0.77-0.87) in the internal validation cohort, and 0.90 (95% CI: 0.87-0.93) in the external validation cohort.

This study demonstrated that the nomogram shows good discriminative performance and can effectively estimate the risk of cardiovascular disease in patients with chronic obstructive pulmonary disease. Age, diabetes mellitus, hypertension, and edema were identified as independent predictors.