Prediction of avatrombopag-induced thrombocytosis in pediatric immune thrombocytopenia: an AI-based real-world study

Avatrombopag (AVA), an oral thrombopoietin receptor agonist (TPO-RA), has demonstrated favorable efficacy in the treatment of pediatric immune thrombocytopenia (ITP). However, treatment-related thrombocytosis represents a clinically relevant adverse event that may compromise treatment safety and continuity. Currently, no validated tools are available to predict the risk of AVA-induced thrombocytosis before treatment initiation. In this real-world study, we aimed to develop and validate a predictive model for AVA-associated thrombocytosis in children with ITP. A total of 74 pediatric patients treated with AVA at the Hematology-Oncology Center of Beijing Children's Hospital between July 2021 and January 2024 were included. We compared the proposed model with established classical machine learning baselines, including Logistic Regression (LR), Support Vector Machine (SVM), Multilayer Perceptron (MLP), Random Forest (RF), and XGBoost, as well as state-of-the-art deep learning models for tabular data, including TabPFN, FT-Transformer, and HyperTab. Among the evaluated models, the FT-Transformer achieved the best performance, with an accuracy of 0.785 ± 0.023 and an area under the receiver operating characteristic curve (AUC) of 0.851 ± 0.021. Model interpretability was enhanced using Shapley Additive Explanations (SHAP), enabling visualization of individual feature contributions to thrombocytosis risk. This AI-driven prediction model, grounded in real-world clinical data, demonstrates robust predictive performance and offers clinically interpretable insights. It provides a reliable reference for individualized risk assessment and supports safer, more precise use of AVA in pediatric ITP management.