[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128741-en":3,"doc-seo-128741-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128741,1099523885074,"Ivy","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure","Heart failure in children under five years of age is associated with a high in-hospital mortality risk, while existing pediatric tools provide limited specificity for this subgroup. A retrospective cohort study analyzed 630 hospitalized children with heart failure (2013–2024) and used Boruta to select seven key predictors from 67 variables. Six machine learning models were trained, with XGBoost chosen and interpreted using SHAP, and validated externally in 73 additional cases.","TYPE Original Research PUBLISHED 26 May 2025  \nDOI 10.3389/fped.2025.1608334  \nEDITED BY  \nYoshihide Mitani, Mie University, Japan  \nREVIEWED BY  \nFederico Gutierrez-Larraya, University Hospital La Paz, Spain Kottaimalai Ramaraj, Kalasalingam University, India  \n*CORRESPONDENCE  \nYou Chen  \n [donny666@sina.com](donny666@sina.com)  \nRECEIVED 08 April 2025  \nACCEPTED 12 May 2025  \nPUBLISHED 26 May 2025  \nCITATION  \nLv H, Sun F, Yuan T, Shen H, Baheti L and Chen Y (2025) Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure.  \nFront. Pediatr. 13:1608334 .  \ndoi: 10.3389/fped.2025.1608334  \nCOPYRIGHT  \n© 2025 Lv, Sun, Yuan, Shen, Baheti and Chen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDevelopment and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure  \nHuasheng Lv1, Fengyu Sun2, Teng Yuan1, Haoliang Shen1, Lazaiyi Baheti1 and You Chen1*  \n1Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China, 2Department of Pediatrics, Xinjiang Medical University, Urumqi, China  \nBackground: Heart 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.  \nMethods: We 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.  \nResults: The 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.  \nConclusion: This 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.  \nKEYWORDS  \npediatric heart failure, in-hospital mortality, machine learning, risk prediction, interpretability  \n1 Introduction  \nFor Heart failure (HF) in pediatric populations represents a significant global health challenge, contributing substantially to mortality rates among children under five years of age worldwide (1) . In young children, the most common underlying etiologies of HF include congenital heart disease and cardiomyopathy (2) . Although the overall incidence of pediatric HF is relatively low—estimated between 0.9 and 7.4 cases per 100,000 children annually—the condition carries a markedly high morbidity and mortality burden. Reported in-hospital mortality rates among pediatric HF patients range from 7% to as high as 26%, particularly in younger children or those with complex comorbidities (3, 4) . In the United States alone, more than 14,000 pediatric  \nFrontiers in Pediatrics 01 [frontiersin.org](frontiersin.org)  \nhospitalizations annually are attributed to heart failure, highlighting its substantial clinical impact rela","cbCain5NIOKHSfPR","https://ap.wps.com/l/cbCain5NIOKHSfPR","pdf",2749822,1,14,"English","en",105,"# Introduction\n## Study objective and clinical need\n## Rationale for machine learning in pediatric HF\n# Methods\n## Data source and cohort selection\n## Predictor selection and model development\n## Model interpretation and validation\n# Results\n## Predictive performance\n## Key predictors identified by SHAP\n# Conclusion","[{\"question\":\"Why is a specialized mortality prediction model needed for children under five with heart failure?\",\"answer\":\"Because existing pediatric risk scores lack specificity for heart failure in this age group, the study addresses the need for more reliable and interpretable prediction models. It focuses on children under five at heightened risk of rapid deterioration.\"},{\"question\":\"How was the final model interpreted?\",\"answer\":\"The selected XGBoost model was interpreted using SHAP, supporting the clinical relevance of key variables driving predictions.\"},{\"question\":\"What were the model’s validation results and top predictors?\",\"answer\":\"The XGBoost model showed high discrimination with AUC values of 0.916 (training), 0.851 (internal validation), and 0.846 (external validation). The leading predictors included NT-proBNP, pH, PCT, LDH, WBC, creatinine, and platelet count.\"}]","Development and validation of a machine learning model for in-hospital mortality prediction in children under 5 years with heart failure | PDF",1786003020,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"development-and-validation-of-a-machine-learning-model-for-in-hospital-mortality-prediction-in-children-under-5-years-with-heart-failure","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/development-and-validation-of-a-machine-learning-model-for-in-hospital-mortality-prediction-in-children-under-5-years-with-heart-failure/128741/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is a specialized mortality prediction model needed for children under five with heart failure?","Question",{"text":76,"@type":77},"Because existing pediatric risk scores lack specificity for heart failure in this age group, the study addresses the need for more reliable and interpretable prediction models. It focuses on children under five at heightened risk of rapid deterioration.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the final model interpreted?",{"text":81,"@type":77},"The selected XGBoost model was interpreted using SHAP, supporting the clinical relevance of key variables driving predictions.",{"name":83,"@type":74,"acceptedAnswer":84},"What were the model’s validation results and top predictors?",{"text":85,"@type":77},"The XGBoost model showed high discrimination with AUC values of 0.916 (training), 0.851 (internal validation), and 0.846 (external validation). The leading predictors included NT-proBNP, pH, PCT, LDH, WBC, creatinine, and platelet count.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]