Page 136 - 《中国药房》2026年7期

化疗致恶性肿瘤患儿骨髓抑制风险预测模型的系统评价
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何 莉 ，林 欣，蒋小平（重庆医科大学附属儿童医院护理部/儿童少年健康与疾病国家临床医学研究中心/儿
童发育疾病研究教育部重点实验室/儿童神经发育与认知障碍重庆市重点实验室，重庆 400014）

中图分类号 R979.1 文献标志码 A 文章编号 1001-0408（2026）07-0954-06
DOI 10.6039/j.issn.1001-0408.2026.07.22

摘 要 目的 系统评价儿童恶性肿瘤化疗后骨髓抑制风险预测模型的建模策略、关键预测因子与预测性能，为临床决策与研究
提供循证依据。方法 检索2025年4月之前发表于中国知网、万方数据、PubMed等11个数据库中的相关文献。文献筛选和信息
提取由2位研究人员独立完成，模型的偏倚风险和适用性依照PROBAST工具进行严格评估。结果 最终筛选得到7项研究，其中
英文文献4篇、中文文献3篇，涉及12个风险预测模型。多数模型在判别能力方面表现良好，受试者工作特征曲线下面积（AUC）
为0.748～0.981，但仅有2项研究实施了外部验证；有3项研究未充分报告模型的校准信息。PROBAST评估结果显示，所有模型
在偏倚风险方面均为高水平，主要问题包括以回顾性设计为主、样本代表性不足以及缺乏盲法评估等；但在适用性方面，所有模型
的评价结果均为良好。在建模方法方面，多数研究采用传统logistic回归方法构建模型，仅少数研究引入了机器学习算法并对多
种算法进行了系统性比较；采用机器学习方法的模型表现明显优于传统统计方法构建的模型。结论 现有的儿童恶性肿瘤化疗后
骨髓抑制风险预测模型在临床风险预警中显示出潜力，但普遍存在以回顾性单中心设计为主、每变量事件数较低、缺失数据处理
不透明、模型系数报告不一致等设计与方法学局限。未来研究应推进前瞻性设计，融合机器学习与关键临床变量，并严格遵循
TRIPOD声明等报告规范，以提升模型的科学严谨性与临床适用性。
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

Systematic review of risk prediction models for chemotherapy-induced myelosuppression in pediatric
patients with malignant tumors
HE Li，LIN Xin，JIANG Xiaoping（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）

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，
Δ 基金项目 重庆医科大学护理学院院级科学研究项目（No.
incorporate machine learning with key clinical predictors， and
20230308） follow TRIPOD reporting guidelines to enhance scientific rigor
＊第一作者 护士，硕士研究生。研究方向：儿童肿瘤、心理护理。
E-mail：2550650722@qq.com and clinical utility.
# 通信作者 教授，主任护师，硕士生导师，硕士。研究方向：儿童 KEYWORDS risk prediction model； malignant tumor；
肿瘤、心理护理。E-mail：1439638239@qq.com chemotherapy； myelosuppression； child； prediction method

· 954 · China Pharmacy 2026 Vol. 37 No. 7 中国药房 2026年第37卷第7期