Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction
BackgroundHeart 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 patien...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1571185/full |
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| author | Jing-xian Wang Chang-ping Li Zhuang Cui Yan Liang Yu-hang Wang Yu Zhou Yin Liu Jing Gao Jing Gao Jing Gao Jing Gao |
| author_facet | Jing-xian Wang Chang-ping Li Zhuang Cui Yan Liang Yu-hang Wang Yu Zhou Yin Liu Jing Gao Jing Gao Jing Gao Jing Gao |
| author_sort | Jing-xian Wang |
| collection | DOAJ |
| description | BackgroundHeart 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.MethodsThis 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.ResultsThe 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/m2, C-reactive Protein to Lymphocyte Ratio (CLR) > 2.83, Hypertension and Fibrinogen (Fg) > 4 g/L.ConclusionsThe 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. |
| format | Article |
| id | doaj-art-ccb4e2269a7143cab8aa26e6a7f52162 |
| institution | Kabale University |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-ccb4e2269a7143cab8aa26e6a7f521622025-08-20T03:52:38ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-05-011210.3389/fcvm.2025.15711851571185Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarctionJing-xian Wang0Chang-ping Li1Zhuang Cui2Yan Liang3Yu-hang Wang4Yu Zhou5Yin Liu6Jing Gao7Jing Gao8Jing Gao9Jing Gao10Clinical School of Thoracic, Tianjin Medical University, Tianjin, ChinaSchool of Public Health, Tianjin Medical University, Tianjin, ChinaSchool of Public Health, Tianjin Medical University, Tianjin, ChinaDepartment of Cardiology, Tianjin Chest Hospital, Tianjin, ChinaClinical School of Thoracic, Tianjin Medical University, Tianjin, ChinaChest Hospital, Tianjin University, Tianjin, ChinaDepartment of Cardiology, Tianjin Chest Hospital, Tianjin, ChinaClinical School of Thoracic, Tianjin Medical University, Tianjin, ChinaChest Hospital, Tianjin University, Tianjin, ChinaCardiovascular Institute, Tianjin Chest Hospital, Tianjin, ChinaTianjin Key Laboratory of Cardiovascular Emergency and Critical Care, Tianjin, ChinaBackgroundHeart 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.MethodsThis 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.ResultsThe 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/m2, C-reactive Protein to Lymphocyte Ratio (CLR) > 2.83, Hypertension and Fibrinogen (Fg) > 4 g/L.ConclusionsThe 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.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1571185/fullpremature myocardial infarctionheart failure with preserved ejection fractionmachine learningXGBoostprediction |
| spellingShingle | Jing-xian Wang Chang-ping Li Zhuang Cui Yan Liang Yu-hang Wang Yu Zhou Yin Liu Jing Gao Jing Gao Jing Gao Jing Gao Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction Frontiers in Cardiovascular Medicine premature myocardial infarction heart failure with preserved ejection fraction machine learning XGBoost prediction |
| title | Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction |
| title_full | Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction |
| title_fullStr | Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction |
| title_full_unstemmed | Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction |
| title_short | Machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction |
| title_sort | machine learning algorithms to predict heart failure with preserved ejection fraction among patients with premature myocardial infarction |
| topic | premature myocardial infarction heart failure with preserved ejection fraction machine learning XGBoost prediction |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1571185/full |
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