Comorbidity severity adjusted model for predicting mortality in hospitalized patients with COPD

Abstract

Background Chronic obstructive pulmonary disease (COPD) is a leading cause of morbidity and mortality worldwide, ranking eighth in disability-adjusted life years (DALYs). Patients with COPD frequently exhibit multimorbidity—including cardiovascular disease, cerebrovascular disease, diabetes, and lung cancer. Accordingly, the Charlson Comorbidity Index (CCI) is widely used as a standard tool for comorbidity adjustment; however, as a general-purpose index, it may not fully capture disease-specific patterns of multimorbidity, underscoring the urgency for tailored approaches. Therefore, we aimed to construct a comorbidity severity adjustment model to improve the prediction of in-hospital mortality among COPD patients using large-scale registry data from the Korea Disease Control and Prevention Agency. Method This retrospective study used the Korea National Hospital Discharge In-Depth Injury Survey (KNHDIS) from 2011 to 2023, including 13,385 hospitalized COPD patients (ICD-10 code J44). All analyses accounted for the complex sample design with stratification, clustering, and sampling weights. The baseline model used multivariable logistic regression with conventional covariates, whereas the extended model incorporated comorbidity clusters derived from Apriori association rule mining to capture interconnected disease patterns. Odds ratios (OR) and 95% confidence intervals (CI) were calculated to assess determinants of in-hospital mortality. Result In the baseline model, advanced age, emergency admission, larger hospital size, and higher CCI scores were significantly associated with increased mortality. In the extended model with Apriori-derived clusters, additional high-risk profiles were identified: (i) septicemia with pneumonia, (ii) respiratory failure with sequelae of tuberculosis, and (iii) pulmonary heart disease with sequelae of tuberculosis. The predictive performance improved with the inclusion of comorbidity clusters (AUC 0.753 vs. 0.732), indicating that the cluster-augmented model provided superior discrimination compared with the baseline model. Conclusion These findings indicate that integrating data-driven comorbidity patterns into traditional risk models enhances mortality risk stratification for COPD inpatients and may support both clinical decision-making and the development of evidence-based health policies.