Key result
Machine learning model achieves 0.85 AUC predicting 30-day readmission in advanced HF.
Why the study?
Patients with advanced heart failure face a high risk of early readmission, but early identification of high-risk individuals remains a clinical challenge.
Does a machine learning model incorporating inflammatory and metabolic biomarkers accurately predict 30-day readmission in patients with advanced heart failure?
Does a machine learning model incorporating inflammatory and metabolic biomarkers accurately predict 30-day readmission in patients with advanced heart failure?
A machine learning model utilizing inflammatory and metabolic biomarkers alongside clinical features can accurately predict 30-day readmission risk in patients with advanced heart failure.
May enable targeted interventions to reduce readmission in advanced HF; extends ML models with inflammatory and metabolic biomarkers but requires validation.
Background Patients with advanced heart failure (AdHF) face a high risk of early readmission, leading to poor outcomes and increased healthcare burden. Early identification of high‐risk individuals remains a clinical challenge. Methods This retrospective study included 769 AdHF patients from the Affiliated Hospital of Xuzhou Medical University, with an independent external validation performed on a cohort of 495 AdHF patients from Shanghai Tenth People′s Hospital. After handling missing data via multiple imputation, the dataset was randomly split into training and validation sets in a 7:3 ratio. Class imbalance was addressed by applying the synthetic minority oversampling technique (SMOTE) exclusively within the training set, followed by the least absolute shrinkage and selection operator (LASSO)‐based feature selection. Seven machine learning models were developed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curve, accuracy, sensitivity, specificity, F1 score, and Brier score, among others. SHAP values and a web‐based dynamic nomogram were used for model interpretation and clinical application. Results The 30‐day readmission rate was 30.9%. RF achieved the highest AUC (0.85), accuracy (0.79), and the lowest Brier score (0.159). Key predictors included C‐reactive protein (CRP), triglyceride‐glucose‐body mass index (TYG‐BMI), neutrophil‐to‐lymphocyte ratio (NLR), age, New York Heart Association (NYHA) class, atrial fibrillation (AF), comorbidity count, and angiotensin‐converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor–neprilysin inhibitors (ACEI/ARB/ARNI) use. SHAP analysis confirmed the importance of inflammatory and metabolic markers. A web‐based nomogram was constructed to allow interactive risk prediction. Conclusion This study presents a robust and interpretable model for predicting a 30‐day readmission in AdHF patients, highlighting the role of inflammation, metabolism, and comorbidity burden. The model can assist clinicians in risk stratification and personalized postdischarge management.
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Zang et al. (2026) studied this question. The machine learning model achieved an AUC of 0.85 for predicting 30-day readmission in advanced heart failure patients, with a 30.9% readmission rate observed.
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