Min Zhou,1 Xiue Du2 

1Department of Intensive Care Unit, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu, 221004, People’s Republic of China; 2Department of Intensive Care Unit, Suining County People’s Hospital, Xuzhou, Jiangsu, 221200, People’s Republic of China

Correspondence: Min Zhou, Email 15050047978@163.com

Background: To develop a machine learning (ML)-based prediction model focused on the one-year mortality risk in patients with advanced heart failure (AdHF), aiming to improve prediction accuracy by integrating inflammatory biomarkers and clinical parameters, assist clinical decision-making, and enhance patient outcomes.
Methods: A retrospective cohort study. Data were obtained from the electronic medical records system of the Affiliated Hospital of Xuzhou Medical University. AdHF patients admitted to the ICU and cardiology department from January 2015 to December 2023 were included with a one-year follow-up. 52 variables potentially affecting prognosis were incorporated. The LASSO algorithm was used for feature selection and dimensionality reduction. Data were split into training and validation sets. Seven ML algorithms were applied to build and evaluate models. The SHAP method was used for model analysis and a dynamic nomogram was created.
Results: The study included 715 AdHF patients. The random forest (RF) model performed best, with an area under the curve (AUC) of 0.83 (95% confidence interval: 0.77– 0.88), an accuracy of 0.72, a sensitivity of 0.74, and an F1 score of 0.73. Key predictors of one-year mortality risk included Beta blockers, ACEI/ARB/ARNI, BNP, CRP, NLR, AF, MI, NYHA class, and age. SHAP analysis revealed that elevated CRP, NLR, and age were associated with increased risk, while Beta blockers, ACEI/ARB/ARNI, and lower BNP values were associated with reduced risk. An online dynamic nomogram was developed to provide personalized risk predictions based on patient-specific conditions.
Conclusion: A successful ML-based prediction model was developed to accurately predict the one-year mortality risk in AdHF patients, with inflammation-driven factors being significant. The RF model integrating clinical features and inflammatory markers showed excellent performance and could assist clinical decision-making. Future research should conduct larger, multi-center, and prospective studies to further validate these findings.

Keywords: advanced heart failure, inflammation, machine learning, one - year mortality, risk prediction model