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. 2025 Jul 24:20:1071-1084.
doi: 10.2147/CIA.S528442. eCollection 2025.

Machine Learning-Driven Prediction of One-Year Readmission in HFrEF Patients: The Key Role of Inflammation

Fanghui Ma #  1 , Yue Hu #  1 , Ping Han  1 , Yan Qiu  1 , Ying Liu  1 , Jingjing Ren  1
Affiliations

Machine Learning-Driven Prediction of One-Year Readmission in HFrEF Patients: The Key Role of Inflammation

Fanghui Ma et al. Clin Interv Aging. .

Abstract

Background: Heart failure with reduced ejection fraction (HFrEF) is a global health issue with high morbidity and frequent hospitalizations. Predicting one-year readmission risk is crucial for optimizing treatment and reducing costs.

Methods: We conducted a single-center retrospective study on adult HFrEF patients admitted to the Cardiovascular Department of the First Affiliated Hospital, Zhejiang University School of Medicine on January 2020 and March 2023. Feature selection was performed using LASSO regression, with inflammatory biomarkers (PLR, MLR, NLR, SII, SIRI) prioritized. Seven machine learning (ML) algorithms were trained and validated using a 7:3 dataset split; the metrics of the model included the area under the curve (AUC), accuracy, sensitivity, specificity, F1 score, and Brier score. SHapley Additive exPlanations (SHAP) analysis provided model interpretability. A network-based dynamic nomogram was developed to visualize predictive models.

Results: This study included 733 patients, of whom 231 (31.5%) were readmitted within one year. LASSO regression showed that the key predictors included age, BNP, New York Heart Association (NYHA) class, LVEF, PLR, MLR, AF history, and ACEI/ARB/ARNI usage. The Random Forest (RF) model performed best, with an AUC of 0.89 (95% confidence interval (CI): 0.86-0.93), an accuracy of 0.83, a sensitivity of 0.87, and a specificity of 0.80. SHAP analysis showed that BNP was the most influential feature, followed by NYHA class and LVEF, which were also important predictors. In addition, MLR and PLR also played an important role in prediction, once again confirming the important predictive role of MLR and PLR as inflammatory indicators for readmission within one year in HFrEF patients.

Conclusion: The ML-based RF model effectively predicted one-year readmission in HFrEF patients, with inflammation indicators playing an important role. Integrating such models into clinical practice could improve risk stratification, reduce readmissions, and enhancing patient outcomes.

Keywords: HFrEF; machine learning; prediction model; readmission.

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Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Figure 1
Figure 1
Flow chart of the study design.
Figure 2
Figure 2
Predictor selection and coefficient magnitudes from LASSO regression modeling.
Figure 3
Figure 3
ROC curves of seven machine learning algorithms for 1-year HFrEF readmission prediction.
Figure 4
Figure 4
Global model explanation by the SHAP method. (A) SHAP summary bar plot. (B) SHAP summary dot plot.
Figure 5
Figure 5
Local model explanation by the SHAP method. (A) waterfall plot. (B) Force plot.
Figure 6
Figure 6
Web-based dynamic nomogram used for predicting 1-year readmission in patients with HFrEF. (A) Input page: Enter the patient’s information according to the relevant variables on this page. (B) Graphical summary: This page shows the probability of a patient being readmitted for heart failure and the 95% confidence interval. (C) Numerical summary: Display the specific values of the patient’s indicators and predicted outcomes.
See this image and copyright information in PMC

References

    1. Yancy CW, Jessup M, Bozkurt B, et al. ACC/AHA/HFSA focused update of the 2013 ACCF/AHA guideline for the management of heart failure: a report of the American college of cardiology/American heart association task force on clinical practice guidelines and the heart failure society of America. Circulation. 2017;136(6):e137–e161. doi: 10.1161/CIR.0000000000000509 - DOI - PubMed
    1. Savarese G, Becher PM, Lund LH, et al. Global burden of heart failure: a comprehensive and updated review of epidemiology. Cardiovasc Res. 2023;118(17):3272–3287. doi: 10.1093/cvr/cvac013 - DOI - PubMed
    1. McDonagh TA, Metra M, Adamo M, et al. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure. Eur Heart J. 2021;42(36):3599–3726. doi: 10.1093/eurheartj/ehab368 - DOI - PubMed
    1. Dunlay SM, Roger VL, Redfield MM. Epidemiology of heart failure with preserved ejection fraction. Nat Rev Cardiol. 2017;14(10):591–602. doi: 10.1038/nrcardio.2017.65 - DOI - PubMed
    1. Chioncel O, Lainscak M, Seferovic PM, et al. Epidemiology and one-year outcomes in patients with chronic heart failure and preserved, mid-range and reduced ejection fraction: an analysis of the ESC heart failure long-term registry. Eur J Heart Fail. 2017;19(12):1574–1585. doi: 10.1002/ejhf.813 - DOI - PubMed