中国药房2026，Vol.37Issue(7)：954-959,6.DOI:10.6039/j.issn.1001-0408.2026.07.22
化疗致恶性肿瘤患儿骨髓抑制风险预测模型的系统评价
Systematic review of risk prediction models for chemotherapy-induced myelosuppression in pediatric patients with malignant tumors
摘要
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.关键词
风险预测模型/恶性肿瘤/化疗/骨髓抑制/儿童/预测方法Key words
risk prediction model/malignant tumor/chemotherapy/myelosuppression/child/prediction method分类
医药卫生引用本文复制引用
何莉,林欣,蒋小平..化疗致恶性肿瘤患儿骨髓抑制风险预测模型的系统评价[J].中国药房,2026,37(7):954-959,6.基金项目
重庆医科大学护理学院院级科学研究项目(No.20230308) （No.20230308）
