Key result
An XGBoost machine learning model accurately predicted the risk of in-hospital heart failure with preserved ejection fraction in patients with premature myocardial infarction, achieving an AUC of 0.854 and significantly outperforming logistic regression.
Why the study?
HFpEF in patients with premature myocardial infarction is a crucial factor affecting long-term prognosis, prompting the need for a machine learning model to predict in-hospital HFpEF early and quickly.
Cohort (n=840)
No
An XGBoost machine learning model using 10 clinical variables can accurately predict the risk of in-hospital HFpEF in patients with premature myocardial infarction.
Answers are grounded in this paper and cite their sources.
May support ML-based HFpEF risk stratification after premature MI; leaves open prospective validation before clinical use.
Background Heart Failure with Preserved Ejection Fraction (HFpEF) in patients with Premature Myocardial Infarction (PMI) is a crucial factor affecting long-term prognosis. This study aims to develop a model based on a machine learning algorithm that can predict the risk of in-hospital HFpEF in patients with PMI early and quickly. Methods This prospective study consecutively included PMI patients from January 2017 to December 2022. Lasso-Logistic, XGBoost, Random Forest, K-Nearest Neighbor, and Support Vector Machine models were constructed. The prediction performance of the models was compared through AUC, Accuracy, Precision, F1 score, and Brier score. Shapley Additive exPlanations is used to explain the model. A prediction system was developed to identify high-risk patients. Results The study finally included 840 PMI patients. 268 (31.90%) developed in-hospital HFpEF. The XGBoost model has the best prediction performance (AUC 0.854; Accuracy 0.798; Precision 0.686; F1 score 0.586; Brier score 0.143). The final model included ten variables, which were Brain natriuretic peptide (BNP) > 100pg/ml, SYNTAX Score > 14.5, Age, Monocyte to Lymphocyte Ratio (MLR) > 0.3, Hematocrit (HCT) < 45%, Heart rate (HR) > 75 bpm, Body Mass Index (BMI) ≥ 24 kg/m 2 , C-reactive Protein to Lymphocyte Ratio (CLR) > 2.83, Hypertension and Fibrinogen (Fg) > 4 g/L. Conclusions The explainable prediction model established based on the XGBoost algorithm can accurately predict the risk of in-hospital HFpEF in PMI patients and is available at https://hfpefpmi.shinyapps.io/apppredict/ . This system is expected to assist clinicians in decision-making by providing timely, prioritized, and precise interventions for PMI patients, ultimately reducing the incidence of HFpEF and improving long-term prognosis.
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Wang et al. (2025) conducted a cohort in Premature Myocardial Infarction (PMI) (n=840). XGBoost machine learning model vs. Lasso-Logistic regression and other machine learning models was evaluated on Prediction of in-hospital Heart Failure with Preserved Ejection Fraction (AUC) (NRI 0.149, 95% CI 0.008-0.290, p=0.039). An XGBoost machine learning model accurately predicted the risk of in-hospital heart failure with preserved ejection fraction in patients with premature myocardial infarction, achieving an AUC of 0.854 and significantly outperforming logistic regression.
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