Machine learning to forecast rituximab responses for paediatric immune thrombocytopenia: Forging a path towards personalized medical care.

Primary immune thrombocytopenia (ITP) is an autoimmune disorder characterized by decreased platelet counts and increased bleeding risk. Although paediatric ITP often resolves spontaneously, some children do not respond to first-line treatments, thus requiring rituximab as a second-line therapy to reduce bleeding risks and corticosteroid exposure. Currently, there is no reliable method to predict the efficacy of rituximab. Our study aimed to develop a machine learning (ML) model to predict the initial response to rituximab in these patients. We analysed data from 156 paediatric ITP patients treated at Beijing Children's Hospital between 2020 and 2023 and identified 25 key predictive features. Among the four evaluated ML models, the multilayer perceptron model exhibited the highest predictive accuracy. SHapley Additive exPlanations analysis revealed that antinuclear antibody titre, thyroglobulin antibody, corticosteroid response and bleeding severity were significant positive predictors, while thyroid peroxidase antibody, CD3+ CD4+ IL-17+ T cells and the duration of disease before rituximab treatment were negatively associated with treatment responses. This ML model could be used to predict rituximab responses in paediatric ITP, which is expected to optimize treatment strategies and improve patient outcomes.