Predicting Chronicity in Children and Adolescents With Newly Diagnosed Immune Thrombocytopenia at the Timepoint of Diagnosis Using Machine Learning‐Based Approaches

Pediatric Blood & Cancer 2026 Open access

Read full text
Authors: Severin Kasser, Alice Bizeul, Meera Chitlur, Hugo Donato, Jelena Roganovic, Julia E. Vogt, T. Kühne
DOI: 10.1002/1545-5017.70103
Cited by: 3
References: 11

Abstract

ABSTRACT Objectives To identify predictors of chronic ITP (cITP) and to develop a model based on several machine learning (ML) methods to estimate the individual risk of chronicity at the timepoint of diagnosis. Methods We analyzed a longitudinal cohort of 944 children enrolled in the Intercontinental Cooperative immune thrombocytopenia (ITP) Study Group (ICIS) Children's Initiative. cITP was defined as a platelet count

Fields of Study & Subjects

Platelet Disorders and Treatments Heparin-Induced Thrombocytopenia and Thrombosis Blood properties and coagulation

Publication details

Journal / source
Pediatric Blood & Cancer
Publisher
Wiley
Volume
73
Issue
3
Pages
e70103–e70103
Publication date
12 January 2026
Language
EN