Prediction of tumor lysis syndrome in childhood acute lymphoblastic leukemia based on machine learning models: a retrospective study

作者全名："Xiao, Yao; Xiao, Li; Zhang, Yang; Xu, Ximing; Guan, Xianmin; Guo, Yuxia; Shen, Yali; Lei, Xiaoying; Dou, Ying; Yu, Jie"

作者地址："[Xiao, Yao; Xiao, Li; Guan, Xianmin; Guo, Yuxia; Shen, Yali; Lei, Xiaoying; Dou, Ying; Yu, Jie] Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Hematol & Oncol, Chongqing Key Lab Pediat,Childrens Hosp,Minist Ed, Chongqing, Peoples R China; [Zhang, Yang] Chongqing Med Univ, Coll Med Informat, Chongqing, Peoples R China; [Xu, Ximing] Chongqing Med Univ, Big Data Engn Ctr Childrens Med Care, Childrens Hosp, Chongqing, Peoples R China"

通信作者："Yu, J (通讯作者)，Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Hematol & Oncol, Chongqing Key Lab Pediat,Childrens Hosp,Minist Ed, Chongqing, Peoples R China."

来源：FRONTIERS IN ONCOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:001188103600001

JCR分区：Q2

影响因子：3.5

年份：2024

卷号：14

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：machine learning; predictive modeling; acute lymphoblastic leukemia; tumor lysis syndrome; treatment toxicity

摘要："Background Tumor lysis syndrome (TLS) often occurs early after induction chemotherapy for acute lymphoblastic leukemia (ALL) and can rapidly progress. This study aimed to construct a machine learning model to predict the risk of TLS using clinical indicators at the time of ALL diagnosis.Methods This observational cohort study was conducted at the National Clinical Research Center for Child Health and Disease. Data were collected from pediatric ALL patients diagnosed between December 2008 and December 2021. Four 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 2,243 pediatric ALL patients, and the occurrence of TLS was 8.87%. A total of 33 indicators with missing values <= 30% were collected, and 12 risk factors were selected through LASSO regression analysis. The CatBoost model with the best performance after feature screening was selected to predict the TLS of ALL patients. The CatBoost model had an AUC of 0.832 and an accuracy of 0.758. The risk factors most associated with TLS were the absence of potassium, phosphorus, aspartate transaminase (AST), white blood cell count (WBC), and urea levels.Conclusion We developed the first TLS prediction model for pediatric ALL to assist clinicians in risk stratification at diagnosis and in developing personalized treatment protocols. This study is registered on the China Clinical Trials Registry platform (ChiCTR2200060616).Clinical trial registration https://www.chictr.org.cn/, identifier ChiCTR2200060616."

基金机构：Chongqing Medical University10.13039/501100004374

基金资助正文：No Statement Available