The Impact of Comorbidity Patterns on Clinical Outcomes in Heart Failure: A Machine Learning-Based Cluster Analysis

Heart failure (HF) is a major global health burden, and complex comorbidity patterns can worsen clinical outcomes and complicate patient care. This study aimed to identify distinct comorbidity-based clusters among HF patients and evaluate their associations with short-term clinical outcomes. We analyzed electronic health records from 1,010,573 HF patients in China between 2021 and 2024 and classified into 5 distinct clusters using the Clustering Large Applications (CLARA) algorithm. Cluster 5, characterized by the highest comorbidity burden, was associated with an increased risk of 30-day readmission (adjusted OR: 1.29, 95% CI: 1.25 to 1.33), whereas Clusters 2 and 3 demonstrated lower risks compared with the reference group. XGBoost achieved the best predictive performance among multiple machine learning models (area under the receiver operating characteristic curve 0.76; Brier score 0.17). Age and Charlson Comorbidity Index score were the most influential predictors, and features derived from the comorbidity clusters provided additional predictive value. In conclusion, these findings demonstrate substantial heterogeneity among HF patients, highlight the clinical relevance of comorbidity-based clustering, and suggest its potential to improve risk stratification and personalized care strategies.