Systematic review of risk prediction models for chemotherapy-induced myelosuppression in pediatric patients with malignant tumors
- VernacularTitle:化疗致恶性肿瘤患儿骨髓抑制风险预测模型的系统评价
- Author:
Li HE
1
;
Xin LIN
1
;
Xiaoping JIANG
1
Author Information
1. Dept. of Nursing，Children’s Hospital of Chongqing Medical University/ National Clinical Medical Research Center for Children and Adolescents’Health and Diseases/Ministry of Education Key Laboratory of Child Development and Disorders/Chongqing Key Laboratory of Child Neurodevelopment and Cognitive Disorders，Chongqing 400014，China
- Publication Type:Journal Article
- Keywords:
risk prediction model;
malignant tumor
- From:
China Pharmacy
2026;37(7):954-959
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in pediatric patients with malignant tumors， evaluate their modeling strategies， key predictors， and predictive performance， and provide evidence-based references for clinical decision-making and research. METHODS A literature search was conducted across 11 databases， including CNKI， Wanfang Data， and PubMed， for relevant studies published before April 2025. Two reviewers independently performed literature screening and data extraction， and the risk of bias and applicability of the models were evaluated using the PROBAST tool. RESULTS Ultimately， seven studies were selected， of which four were English articles and three were Chinese articles， involving 12 risk prediction models. Although model discrimination was good （AUC 0.748-0.981）， only two models underwent external validation； furthermore， calibration was inadequately reported in three studies. PROBAST indicated that all models exhibited a high risk of bias， with major issues including a predominance of retrospective designs， inadequate sample representativeness， and lack of blinding. However， in terms of applicability， all models received favorable evaluations. In terms of modeling methods， most studies employed traditional logistic regression approaches to construct models， while only a minority introduced machine learning algorithms and conducted systematic comparisons among multiple algorithms. Models developed using machine learning methods significantly outperformed those constructed with traditional statistical methods. CONCLUSIONS The existing risk prediction models for myelosuppression after chemotherapy in children with malignant tumors demonstrate potential in clinical risk early warning. However， they generally suffer from design and methodological limitations， such as a predominance of retrospective single-center designs， few events per variable， opaque handling of missing data， and inconsistent reporting of model coefficients. Future studies should adopt prospective designs， incorporate machine learning with key clinical predictors， and follow TRIPOD reporting guidelines to enhance scientific rigor and clinical utility.
