A predictive model for cytarabine syndrome-associated febrile episodes in children with acute lymphoblastic leukemia.
- 1Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, China.
- 2Department of Hematology and Oncology, Shenzhen Children's Hospital, Shenzhen, China.
Abstract
Objective
This study aims to develop a predictive model for cytarabine syndrome (CS)-related fever in pediatric patients with acute lymphoblastic leukemia (ALL). By integrating clinical features and laboratory indicators, we seek to establish an evidence-based diagnostic tool to assist clinicians in decision-making and optimize the use of antimicrobial agents.Methods
Study Design: A retrospective case-control study was conducted involving 72 ALL children (32 in the CS group vs. 40 in the infection group) who developed fever (≥37.5 °C) following cytarabine treatment. Model Development: Multivariate logistic regression was used to identify independent predictors. A nomogram was constructed. The model's discriminative ability was evaluated using the receiver operating characteristic (ROC) curve, calibration was assessed via the Hosmer-Lemeshow test, and clinical utility was validated using decision curve analysis (DCA).Results
Key Predictors: Multivariate analysis revealed that BSA (OR = 0.01, 95% CI: 0.00-0.17) and hs-CRP (OR = 0.84, 95% CI: 0.72-0.97) were the most significant predictors of CS. Model Performance: The ROC analysis demonstrated an AUC of 0.785, with internal validation via 1000 bootstrap resamples yielding a 95% confidence interval of 0.675-0.882. The calibration curve demonstrated excellent agreement between predicted and observed outcomes (MAE = 0.039). Decision curve analysis indicated that the model provided superior net benefits across most threshold probabilities (0.2-0.8) compared to strategies of universal antibiotic use or no intervention.Conclusion
The developed predictive model demonstrates high discriminative performance and robust clinical applicability, functioning as an effective quantitative instrument for early identification of cytarabine syndrome (CS) and facilitating evidence-based antibiotic stewardship.
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BibTeX
@article{wu2026predictive,
title = {{A predictive model for cytarabine syndrome-associated febrile episodes in children with acute lymphoblastic leukemia.}},
author = {Zheng Wu and Lixiang Zhu and Yi Liu and Fangyuan Lai and Zebin Chen and Feiqiu Wen and Huirong Mai and Xuejuan Li},
journal = {Frontiers in pediatrics},
year = {2026},
month = oct,
doi = {10.3389/fped.2026.1957986},
url = {https://europepmc.org/article/MED/42909032},
note = {CC BY},
}
RIS
TY - JOUR
TI - A predictive model for cytarabine syndrome-associated febrile episodes in children with acute lymphoblastic leukemia.
AU - Zheng Wu
AU - Lixiang Zhu
AU - Yi Liu
AU - Fangyuan Lai
AU - Zebin Chen
AU - Feiqiu Wen
AU - Huirong Mai
AU - Xuejuan Li
PY - 2026
DA - 2026/10/07
JO - Frontiers in pediatrics
DO - 10.3389/fped.2026.1957986
UR - https://europepmc.org/article/MED/42909032
AB - Objective This study aims to develop a predictive model for cytarabine syndrome (CS)-related fever in pediatric patients with acute lymphoblastic leukemia (ALL). By integrating clinical features and laboratory indicators, we seek to establish an evidence-based diagnostic tool to assist clinicians in decision-making and optimize the use of antimicrobial agents.Methods Study Design: A retrospective case-control study was conducted involving 72 ALL children (32 in the CS group vs. 40 in the infection group) who developed fever (≥37.5 °C) following cytarabine treatment. Model Development: Multivariate logistic regression was used to identify independent predictors. A nomogram was constructed. The model's discriminative ability was evaluated using the receiver operating characteristic (ROC) curve, calibration was assessed via the Hosmer-Lemeshow test, and clinical utility was validated using decision curve analysis (DCA).Results Key Predictors: Multivariate analysis revealed that BSA (OR = 0.01, 95% CI: 0.00-0.17) and hs-CRP (OR = 0.84, 95% CI: 0.72-0.97) were the most significant predictors of CS. Model Performance: The ROC analysis demonstrated an AUC of 0.785, with internal validation via 1000 bootstrap resamples yielding a 95% confidence interval of 0.675-0.882. The calibration curve demonstrated excellent agreement between predicted and observed outcomes (MAE = 0.039). Decision curve analysis indicated that the model provided superior net benefits across most threshold probabilities (0.2-0.8) compared to strategies of universal antibiotic use or no intervention.Conclusion The developed predictive model demonstrates high discriminative performance and robust clinical applicability, functioning as an effective quantitative instrument for early identification of cytarabine syndrome (CS) and facilitating evidence-based antibiotic stewardship.
SN - 2296-2360
ER -