Lv Huasheng, Sun Fengyu, Yuan Teng, Shen Haoliang, Baheti Lazaiyi, Chen You
Department of Cardiology, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.
Department of Pediatrics, Xinjiang Medical University, Urumqi, China.
Front Pediatr. 2025 May 26;13:1608334. doi: 10.3389/fped.2025.1608334. eCollection 2025.
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.
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.
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.
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.
五岁以下儿童心力衰竭（HF）的院内死亡风险很高，但现有的儿科风险评估工具对该人群缺乏特异性。迫切需要针对小儿心力衰竭的可靠、可解释的预测模型。
我们回顾性分析了2013年至2024年630例五岁以下因心力衰竭住院的儿童。在排除患有未矫正先天性心脏病或终末期合并症的儿童后，评估了67个变量，并使用Boruta算法确定了七个关键预测因素。开发了六种机器学习模型；选择了极端梯度提升（XGB）模型并使用SHAP进行解释。外部验证包括另外73例病例。
XGB模型具有较高的预测性能（AUC：训练集为0.916，内部验证为0.851，外部验证为0.846）。主要预测因素为N末端B型利钠肽原（NT-proBNP）、pH值、降钙素原（PCT）、乳酸脱氢酶（LDH）、白细胞（WBC）、肌酐和血小板计数。SHAP分析证实了这些变量的临床相关性。
本研究提出了一种可靠、可解释的机器学习模型，用于预测小儿心力衰竭的院内死亡率。它有望实现早期风险分层和及时干预，可能改善这一高危人群的预后。
