Multicenter Machine Learning Model for Assessing the Impact of Malignancy on In-Hospital Mortality in Heart Failure Patients: A Clinical Decision Support System with Interpretable Artificial Intelligence

Background Heart failure (HF) and malignancy represent two major global health burdens that frequently coexist and lead to poor clinical outcomes. However, the specific impact of malignancy on in-hospital mortality in HF patients remains incompletely understood, and reliable predictive tools specifically for HF patients with comorbid malignancy are currently lacking. Methods This multicenter retrospective study analyzed data from the eICU Collaborative Research Database (eICU database), Medical Information Mart for Intensive Care IV (MIMIC-IV) databases, and Medical Information Mart for Intensive Care III (MIMIC-III) databases, including 21,636 HF patients (3,397 with malignancy). We employed three analytical approaches: propensity score matching (PSM), inverse probability treatment weighting (IPTW), and multivariable logistic regression to assess malignancy-associated mortality risk. For predictive modeling, five machine learning algorithms were trained on the eICU database (70% training, 30% internal validation) and externally validated using both MIMIC-IV and MIMIC-III datasets. Results All three analytical methods (PSM, IPTW, and multivariable regression) yielded highly consistent results, demonstrating that malignancy significantly increased in-hospital mortality risk (PSM: OR 1.14, 95% CI 1.02–1.26; IPTW: OR 1.16, 95% CI 1.03–1.30; multivariable regression: OR 1.20, 95% CI 1.07–1.35). The AdaBoost model, developed using 19 key predictive variables selected by the Boruta algorithm, demonstrated excellent performance with a training set AUC of 0.849 and internal validation AUC of 0.740, while maintaining good discriminative ability in external validation (MIMIC-IV: AUC 0.739; MIMIC-III: AUC 0.699). To enhance model interpretability, SHapley Additive exPlanations analysis revealed the top five predictive variables: Simplified Acute Physiology Score II, mechanical ventilation requirement, heart rate, body temperature, and respiratory rate. For clinical implementation, we developed a web-based calculator (available at: https://nanzihan1998.shinyapps.io/Mortality/) to facilitate real-time mortality risk assessment. Conclusions Malignancy independently worsens outcomes in HF patients. Our interpretable machine learning model incorporating multiple clinically relevant predictors provides accurate mortality risk stratification, facilitating personalized clinical decision-making for this high-risk population. Future studies should incorporate longitudinal data and novel biomarkers to further improve predictive performance.