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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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| 篇名： | 化疗致恶性肿瘤患儿骨髓抑制风险预测模型的系统评价 |
| TITLE： | Systematic review of risk prediction models for chemotherapy-induced myelosuppression in pediatric patients with malignant tumors |
| 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。 |
| 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. |
| 期刊： | 2026年第37卷第07期 |
| 作者： | 何莉；林欣；蒋小平 |
| AUTHORS： | HE Li，LIN Xin，JIANG Xiaoping |
| 关键字： | 风险预测模型；恶性肿瘤；化疗；骨髓抑制；儿童；预测方法 |
| KEYWORDS： | risk prediction model； malignant tumor； |
| 阅读数： | 85 次 |
| 本月下载数： | 1 次 |
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