Development and external validation of a machine learning prediction model for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis in children using routine blood parameters: a retrospective cohort study

Li Xiao; Meiling Liao; Yan Meng; Jie Yu; Yinghui Cui; Haiyan Liu; Wanxing Liu; Ruijue Wang; Zhu Li

doi:10.1186/s12879-026-13891-6

Development and external validation of a machine learning prediction model for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis in children using routine blood parameters: a retrospective cohort study

BMC Infect Dis. 2026 Jun 27. doi: 10.1186/s12879-026-13891-6. Online ahead of print.

Authors

Li Xiao¹, Meiling Liao¹, Yan Meng², Jie Yu², Yinghui Cui², Haiyan Liu², Wanxing Liu¹, Ruijue Wang³, Zhu Li⁴

Affiliations

¹ Big Data Center for Children's Medical Care, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Children's Hospital of Chongqing Medical University, Intelligent Application of Big Data in Pediatrics Engineering Research Center of Chongqing Education Commission of China, Chongqing, China.
² Department of Hematology and Oncology, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, 400014, China.
³ Department of Hepatobiliary Surgery, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Children's Hospital of Chongqing Medical University, Chongqing, China. wangruijue@cqmu.edu.cn.
⁴ Department of Emergency, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Children's Hospital of Chongqing Medical University, Intelligent Application of Big Data in Pediatrics Engineering Research Center of Chongqing Education Commission of China, Chongqing, China. lizhu_198528@163.com.

PMID: 42365248
DOI: 10.1186/s12879-026-13891-6

Abstract

Background: Epstein-Barr virus (EBV) infection is a common pediatric infectious disease. Infectious mononucleosis (IM) and hemophagocytic lymphohistiocytosis (HLH), two major complications of EBV infection, share similar clinical manifestations in the early stage. While IM is typically self-limiting, HLH is life-threatening and requires immediate intervention. Early differentiation between these two conditions is crucial for clinical decision-making; however, reliable prediction models based on readily available laboratory parameters remain scarce. This study aimed to develop and validate a machine learning prediction model using routine blood parameters obtained within 24 h of hospital admission in children with confirmed acute EBV infection to distinguish EBV-associated IM (EBV-IM) from EBV-associated HLH (EBV-HLH).

Methods: This retrospective cohort study included 4,871 pediatric patients diagnosed with either EBV-IM or EBV-HLH from two campuses of Children's Hospital of Chongqing Medical University. Demographic information and initial complete blood count (CBC) parameters within 24 h of admission were collected. The cohort was divided into a model development group (Yuzhong Campus, n = 2,848; 70% for training, 30% for internal testing) and an external validation group (Liangjiang Campus, n = 2,023). Thirteen machine learning algorithms were evaluated using random search with 5-fold cross-validation for hyperparameter tuning. Shapley Additive exPlanations (SHAP) analysis was performed to interpret model predictions.

Results: EBV-HLH accounted for 12.46% (607/4,871) of the total cohort, with significantly different prevalence between the development and validation cohorts (18.29% vs. 4.25%, p < 0.001). Significant differences were observed between cohorts in age and all CBC parameters except gender (p < 0.05). The Random Forest (RF) model demonstrated optimal performance in the internal validation set (AUC = 0.993, 95% CI: 0.990-0.996). In the external validation cohort, the RF model maintained robust discriminative ability (AUC = 0.971, 95% CI: 0.949-0.992). Calibration curves indicated excellent agreement between predicted probabilities and actual risks. SHAP analysis identified WBC, PLT, LAC, and Hb as the most critical predictors of EBV-HLH. DCA demonstrated substantial clinical net benefit. A free online decision-support tool (https://wangrj1988.shinyapps.io/EBV-HLH-IM/) was developed based on the RF model to facilitate real-time risk assessment.

Conclusions: The RF-based model using routine CBC parameters enables admission-based risk assessment of pediatric EBV-HLH with excellent generalizability, offering a cost-effective tool for diverse healthcare settings.

Trial registration: Clinical trial number: not applicable.

Keywords: Epstein-Barr virus; External validation; Hemophagocytic lymphohistiocytosis; Infectious mononucleosis; Machine learning; prediction model.