Peer-reviewed article
A predictive model for cytarabine syndrome-associated febrile episodes in children with acute lymphoblastic leukemia
Zheng Wu, Lixiang Zhu, Yi Liu, Fangyuan Lai, Zebin Chen et al. ·
Summary
Written by AI, not yet AICS's view · claude-sonnet-5-5This study built a prediction tool to help doctors tell cytarabine syndrome from infection in children with acute lymphoblastic leukemia who develop a fever after cytarabine treatment. It looked back at records of 72 children, 32 with cytarabine syndrome and 40 with infection. Body surface area and hs-CRP (a blood marker of inflammation) were the strongest predictors, and these were combined into a nomogram (a scoring chart). The authors say the tool could help doctors avoid unnecessary antibiotics. It is a diagnostic aid, not a test of a treatment.
Key points
- A retrospective case-control study of 72 children with ALL who had fever (≥37.5 °C) after cytarabine: 32 in the cytarabine syndrome group and 40 in the infection group.
- Body surface area (OR = 0.01, 95% CI 0.00–0.17) and hs-CRP (OR = 0.84, 95% CI 0.72–0.97) were the main independent predictors of cytarabine syndrome.
- The model's AUC was 0.785, with a bootstrap (1000 resamples) 95% CI of 0.675–0.882.
- The calibration curve showed close agreement between predicted and observed outcomes (MAE = 0.039).
- Decision curve analysis showed more net benefit than giving antibiotics to everyone or to no one across most threshold probabilities (0.2–0.8).
Limitations
The sample is small (72 children) and the design is retrospective. Only internal validation (bootstrap) is described, so we cannot tell how well the model works in other hospitals or with new patients. The abstract also does not show whether using the model changes antibiotic use or child outcomes.
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.