Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure
BackgroundHeart failure (HF) in children under five years of age carries a high risk of in-hospital mortality, yet existing pediatric risk assessment tools lack specificity for this population. There is a pressing need for reliable, interpretable prediction models tailored to pediatric HF.MethodsWe...
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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 Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2025.1608334/full |
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| author | Huasheng Lv Fengyu Sun Teng Yuan Haoliang Shen Lazaiyi Baheti You Chen |
| author_facet | Huasheng Lv Fengyu Sun Teng Yuan Haoliang Shen Lazaiyi Baheti You Chen |
| author_sort | Huasheng Lv |
| collection | DOAJ |
| description | BackgroundHeart failure (HF) in children under five years of age carries a high risk of in-hospital mortality, yet existing pediatric risk assessment tools lack specificity for this population. There is a pressing need for reliable, interpretable prediction models tailored to pediatric HF.MethodsWe retrospectively analyzed 630 hospitalized children under five with heart failure from 2013 to 2024. After excluding those with uncorrected congenital heart disease or terminal comorbidities, 67 variables were assessed, and seven key predictors were identified using the Boruta algorithm. Six machine learning models were developed; the Extreme Gradient Boosting (XGB) model was selected and interpreted using SHAP. External validation included 73 additional cases.ResultsThe XGB model achieved high predictive performance (AUC: 0.916 training, 0.851 internal validation, 0.846 external validation). The top predictors were NT-proBNP, pH, PCT, LDH, WBC, creatinine, and platelet count. SHAP analysis confirmed the clinical relevance of these variables.ConclusionThis study presents a reliable, interpretable machine learning model for predicting in-hospital mortality in young children with heart failure. It holds promise for early risk stratification and timely intervention, potentially improving outcomes in this high-risk population. |
| format | Article |
| id | doaj-art-c9cd1bf3cdac42f5a750e05c7edc59d6 |
| institution | OA Journals |
| issn | 2296-2360 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj-art-c9cd1bf3cdac42f5a750e05c7edc59d62025-08-20T01:57:05ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602025-05-011310.3389/fped.2025.16083341608334Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failureHuasheng Lv0Fengyu Sun1Teng Yuan2Haoliang Shen3Lazaiyi Baheti4You Chen5Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Pediatrics, Xinjiang Medical University, Urumqi, ChinaDepartment of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaDepartment of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, ChinaBackgroundHeart failure (HF) in children under five years of age carries a high risk of in-hospital mortality, yet existing pediatric risk assessment tools lack specificity for this population. There is a pressing need for reliable, interpretable prediction models tailored to pediatric HF.MethodsWe retrospectively analyzed 630 hospitalized children under five with heart failure from 2013 to 2024. After excluding those with uncorrected congenital heart disease or terminal comorbidities, 67 variables were assessed, and seven key predictors were identified using the Boruta algorithm. Six machine learning models were developed; the Extreme Gradient Boosting (XGB) model was selected and interpreted using SHAP. External validation included 73 additional cases.ResultsThe XGB model achieved high predictive performance (AUC: 0.916 training, 0.851 internal validation, 0.846 external validation). The top predictors were NT-proBNP, pH, PCT, LDH, WBC, creatinine, and platelet count. SHAP analysis confirmed the clinical relevance of these variables.ConclusionThis study presents a reliable, interpretable machine learning model for predicting in-hospital mortality in young children with heart failure. It holds promise for early risk stratification and timely intervention, potentially improving outcomes in this high-risk population.https://www.frontiersin.org/articles/10.3389/fped.2025.1608334/fullpediatric heart failurein-hospital mortalitymachine learningrisk predictioninterpretability |
| spellingShingle | Huasheng Lv Fengyu Sun Teng Yuan Haoliang Shen Lazaiyi Baheti You Chen Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure Frontiers in Pediatrics pediatric heart failure in-hospital mortality machine learning risk prediction interpretability |
| title | Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure |
| title_full | Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure |
| title_fullStr | Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure |
| title_full_unstemmed | Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure |
| title_short | Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure |
| title_sort | development and validation of a machine learning model for in hospital mortality prediction in children under 5 years with heart failure |
| topic | pediatric heart failure in-hospital mortality machine learning risk prediction interpretability |
| url | https://www.frontiersin.org/articles/10.3389/fped.2025.1608334/full |
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