Introduction: Childhood immune thrombocytopenia (ITP) is a rare autoimmune disorder characterized by isolated thrombocytopenia. Prolonged ITP (persistent and chronic) leads to a reduced quality of life for children in many domains. To provide optimal support for children, with ITP, it is important to be able to predict those who will develop prolonged ITP. This study aimed to develop a mathematical model based on platelet recovery that allows the early prediction of prolonged ITP. Methods: In this retrospective study, we used platelet counts from the six months following the diagnosis of ITP to model the kinetics of platelet evolution using a pharmacokinetic-pharmacodynamic model. Results: In a learning set (n=103), platelet counts were satisfactorily described by our kinetic model. The K heal parameter, which describes spontaneous platelet recovery, allowed a distinction between acute and prolonged ITP with an AUC of 0.74. In a validation set (n=58), spontaneous platelet recovery was robustly predicted using platelet counts from 15 (AUC=0.76) or 30 (AUC=0.82) days after ITP diagnosis. Discussion: In our model, platelet recovery quantified using the k heal parameter allowed prediction of the clinical course of ITP. Future prospective studies are needed to improve the predictivity of this model, in particular, by combining it with the predictive scores previously reported in the literature.