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Early Prediction Model for Etoposide-Based Protocols Resistance in Pediatric HLH.
Jiajia Wu1,2, Longlong Xie3, Xun Li4
1The School of Pediatrics, Hengyang Medical School, University of South China, Changsha, Hunan, People's Republic of China.
Pediatric Health, Medicine and Therapeutics
|June 29, 2026
Summary
Predicting etoposide resistance in pediatric hemophagocytic lymphohistiocytosis (HLH) is crucial. Pre-treatment IL-10 and platelet counts, along with early post-treatment markers, can identify non-responders for timely salvage therapy.
Area of Science:
- Pediatric Hematology
- Oncology
- Immunology
Background:
- Etoposide-based chemotherapy is the standard first-line treatment for pediatric hemophagocytic lymphohistiocytosis (HLH).
- A significant proportion of pediatric HLH patients (20-30%) exhibit primary resistance to etoposide-based protocols.
- Timely identification of non-responsive patients is essential for initiating effective salvage therapies.
Purpose of the Study:
- To develop and validate a predictive model for etoposide resistance in pediatric HLH.
- To utilize readily available clinical and laboratory parameters for early prediction.
- To improve treatment outcomes by enabling prompt salvage therapy.
Main Methods:
- Retrospective analysis of 79 pediatric HLH patients treated with etoposide-based protocols.
- Stratification into refractory (n=20) and responsive (n=59) groups based on 8-week treatment outcomes.
- Multivariable logistic regression and ROC analyses to identify predictors of resistance, with internal validation via bootstrapping.
Main Results:
- Pre-chemotherapy IL-10 >131.2 μmol/L (OR=5.1) and platelet count <55.5×10⁹/L (OR=4.2) independently predicted refractoriness.
- A combined model using these parameters achieved an AUC of 0.822.
- Incorporating Day 3 post-chemotherapy platelet and lymphocyte counts further improved prediction accuracy (AUC 0.880).
Conclusions:
- Pre-chemotherapy IL-10 and platelet counts are reliable indicators of etoposide resistance in pediatric HLH.
- Integrating early post-chemotherapy parameters allows for ultra-early and precise risk stratification.
- This predictive model facilitates timely transition to salvage therapies for non-responsive patients.
