Predictive model for early death risk in pediatric hemophagocytic lymphohistiocytosis patients based on machine learning

作者全名："Xiao, Li; Zhang, Yang; Xu, Ximing; Dou, Ying; Guan, Xianmin; Guo, Yuxia; Wen, Xianhao; Meng, Yan; Liao, Meiling; Hu, Qinshi; Yu, Jie"

作者地址："[Xiao, Li; Dou, Ying; Guan, Xianmin; Guo, Yuxia; Wen, Xianhao; Meng, Yan; Liao, Meiling; Yu, Jie] Chongqing Med Univ, Dept Hematol & Oncol, Childrens Hosp, Minist Educ,Key Lab Child Dev & Disorders,Natl Cli, Chongqing, Peoples R China; [Zhang, Yang] Chongqing Med Univ, Coll Med Informat, Chongqing, Peoples R China; [Xu, Ximing; Hu, Qinshi] Chongqing Med Univ, Childrens Hosp, Big Data Ctr Childrens Med Care, Chongqing, Peoples R China; [Yu, Jie] 2 Zhongshan Rd, Chongqing 400014, Peoples R China"

通信作者："Yu, J (通讯作者)，2 Zhongshan Rd, Chongqing 400014, Peoples R China."

来源：HELIYON

ESI学科分类：　

WOS号：WOS:001121437300001

JCR分区：Q1

影响因子：3.4

年份：2023

卷号：9

期号：11

开始页：　

结束页：　

文献类型：Article; Early Access

关键词：machine learning; Predictive modeling; HLH; early death; Interpretability

摘要："Background: Hemophagocytic Lymphohistiocytosis (HLH) is a rare and life-threatening disease in children, with a high early mortality rate. This study aimed to construct machine learning model to predict the risk of early death using clinical indicators at the time of HLH diagnosis.Methods: This observational cohort study was conducted at the National Clinical Research Center for Child Health and Disease. Data was collected from pediatric HLH patients diagnosed by the HLH-2004 protocol between January 2006 and December 2022. Six machine learning models were constructed using the Least Absolute Shrinkage and Selection Operator (LASSO) to select key clinical indicators for model construction.Results: The study included 587 pediatric HLH patients, and the early mortality rate was 28.45 %. The logistic and XGBoost model with the best performance after feature screening were selected to predict early death of HLH patients. The logistic model had an AUC of 0.915 and an accuracy of 0.863, while the XGBoost model had an AUC of 0.889 and an accuracy of 0.829. The risk factors most associated with early death were the absence of immunochemotherapy, decreased TC levels, increased BUN and total bilirubin, and prolonged TT. We developed an online calculator tool for predicting the probability of early death in children with HLH.Conclusions: We developed the first web-based early mortality prediction tool for pediatric HLH to assist clinicians in risk stratification at diagnosis and in developing personalized treatment pro-tocols. This study is registered on the China Clinical Trials Registry platform (ChiCTR2200061315)."

基金机构："Intelligence Medicine Project of Chongqing Medical University [YJSZHYX202103, YJSZHYX202215]"

基金资助正文：<BOLD>Funding</BOLD> This work was supported by the Intelligence Medicine Project of Chongqing Medical University (YJSZHYX202103 and YJSZHYX202215) .