A predictive model for cytarabine syndrome-associated febrile episodes in children with acute lymphoblastic leukemia.

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.