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Platelet-related parameters at initial diagnosis for predicting the risk of chronicity in children with newly
Shuxian Zheng1, Jing Liang1, Wen Jiang2
1Department of Blood Transfusion, The Sixth Affiliated Hospital of Xinjiang Medical University, Ürümqi, Xinjiang, China.
Insights
Predicting chronic immune thrombocytopenia (ITP) in children is possible using initial platelet count, mean platelet volume, and immature platelet fraction. These factors help identify children at risk for developing chronic ITP.
Area of Science:
- Pediatric Hematology
- Immunology
- Clinical Diagnostics
Background:
- Immune thrombocytopenia (ITP) is an autoimmune bleeding disorder.
- Predicting progression to chronic ITP in children is crucial for management.
- Current prediction models for pediatric ITP chronicity require refinement.
Purpose of the Study:
- To evaluate the clinical value of platelet parameters and characteristics in predicting chronic immune thrombocytopenia (CITP) in children.
- To develop an individualized risk prediction model for pediatric ITP progression.
Main Methods:
- Retrospective cohort study of newly diagnosed pediatric ITP patients.
- Comparison of platelet count (PLT), mean platelet volume (MPV), and immature platelet fraction (IPF%) between chronic and non-chronic ITP groups.
- Logistic regression analysis and nomogram construction for risk prediction.
Main Results:
- Older age, lower platelet count, and higher immature platelet fraction at diagnosis were independent risk factors for chronicity.
- A combined predictive model using age, PLT, and IPF% showed superior performance.
- The developed nomogram demonstrated good calibration and clinical utility.
Conclusions:
- Age, platelet count, and immature platelet fraction effectively predict chronicity in pediatric ITP.
- The integrated nomogram offers a promising tool for individualized risk assessment.
- Further validation in multicenter prospective studies is warranted.
Objective:
To investigate the clinical value of platelet-related parameters and clinical characteristics at initial diagnosis in predicting the risk of progression to chronic disease in children with newly diagnosed immune thrombocytopenia (ITP), and to develop an individualized risk prediction model.
Methods:
This single-center retrospective cohort study consecutively enrolled pediatric patients who were newly diagnosed with primary immune thrombocytopenia at the Department of Pediatrics of our hospital between January 2022 and January 2025. According to disease course and whether the duration exceeded 12 months, patients were categorized into the chronic immune thrombocytopenia (CITP) group (n = 31) and the non-CITP group (n = 136). Platelet count (PLT), mean platelet volume (MPV), and immature platelet fraction (IPF%) were compared between groups. Independent predictors of chronicity were predicted using univariate and multivariate logistic regression analyses. A visualized nomogram was constructed based on key predictive factors.
Results:
Children in the CITP group were significantly older at initial diagnosis. Among platelet-related parameters, the platelet count at diagnosis was significantly lower in the CITP group, whereas mean platelet volume and immature platelet fraction were significantly higher. Multivariate analysis demonstrated that older age, lower platelet count, and higher immature platelet fraction were independent risk factors for progression from newly diagnosed ITP to chronic disease. ROC curve analysis indicated that the combined predictive model incorporating age, platelet count, and immature platelet fraction achieved the best performance, which was significantly superior to any single indicator alone. The nomogram constructed based on these variables showed good calibration, and DCA confirmed a substantial net clinical benefit across a wide range of threshold probabilities.
Conclusion:
Age at diagnosis, platelet count, and immature platelet fraction are effective predictors of chronicity in children with ITP. A nomogram integrating these three parameters demonstrates favorable predictive accuracy and potential clinical applicability. Nevertheless, these findings require further validation through multicenter prospective studies.
