Systematic review of risk predictive models for chemotherapy-induced myelosuppression in breast cancer

LIU Yang; LI Hongjian; WU Jianhua; LIU Xuetao; JIAO Min; YU Luhai

doi:10.6039/j.issn.1001-0408.2025.05.19

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Systematic review of risk predictive models for chemotherapy-induced myelosuppression in breast cancer

更新时间：2025-09-26

- Systematic review of risk predictive models for chemotherapy-induced myelosuppression in breast cancer
- ZHONGGUO YAOFANG Vol. 36, Issue 5, Pages: 612-618(2025)
- 作者机构：
  
  1.新疆维吾尔自治区人民医院药学部，乌鲁木齐　830001
  2.石河子大学药学院，新疆石河子　832000
- 作者简介：
- 基金信息：
- DOI：10.6039/j.issn.1001-0408.2025.05.19
  CLC： R979.1
- Received：29 August 2024，
  
  Revised：2025-01-07，
  
  Accepted：07 January 2025，
  
  Published：15 March 2025
- 稿件说明：
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刘阳,李红健,吴建华,等.乳腺癌化疗致骨髓抑制风险预测模型的系统评价[J].中国药房,2025,36(05):612-618.

LIU Yang,LI Hongjian,WU Jianhua,et al.Systematic review of risk predictive models for chemotherapy-induced myelosuppression in breast cancer[J].ZHONGGUO YAOFANG,2025,36(05):612-618.
刘阳,李红健,吴建华,等.乳腺癌化疗致骨髓抑制风险预测模型的系统评价[J].中国药房,2025,36(05):612-618. DOI： 10.6039/j.issn.1001-0408.2025.05.19.

LIU Yang,LI Hongjian,WU Jianhua,et al.Systematic review of risk predictive models for chemotherapy-induced myelosuppression in breast cancer[J].ZHONGGUO YAOFANG,2025,36(05):612-618. DOI： 10.6039/j.issn.1001-0408.2025.05.19.

摘要

目的

系统评价乳腺癌化疗致骨髓抑制的风险预测模型，为临床医疗工作者选择或开发有效预测模型提供科学的参考依据。

方法

系统检索中国知网、维普网、万方数据库、PubMed、Web of Science、Cochrane Library、Embase、Scopus数据库中有关乳腺癌化疗致骨髓抑制风险预测模型的研究，检索时限为建库至2024年5月7日。由2名研究者独立筛选文献，根据预测模型系统评价的严格评估和数据清单提取数据，并采用预测模型研究的偏倚风险评价工具分析纳入研究的偏倚风险和适用性。

结果

共纳入7项研究，包含12个模型，其中11个模型报告了受试者工作特征曲线下面积，为0.600～0.908；2个模型报告了校准方法；纳入模型常见的预测变量为年龄、化疗前中性粒细胞计数、化疗前淋巴细胞计数、化疗前白蛋白含量。7项研究整体偏倚风险高（主要原因为研究设计缺陷、样本量不足、变量处理方式不当、未报告缺失数据、模型评估指标缺乏等）但适用性良好。

结论

乳腺癌化疗致骨髓抑制风险预测模型的预测性能有待进一步提升，且模型整体偏倚风险高。未来的研究应遵循模型开发与报告规范，并结合机器学习算法开发出预测性能好、稳定性强、偏倚风险低的风险预测模型，为临床提供决策依据。

Abstract

OBJECTIVE

To systematically evaluate risk prediction models for chemotherapy-induced myelosuppression in breast cancer， and provide a scientific reference for clinical healthcare workers in selecting or developing effective predictive models.

METHODS

A systematic search was conducted for studies on predictive models of the risk of chemotherapy-induced myelosuppression in breast cancer across the CNKI， VIP， Wanfang， PubMed， Web of Science， Cochrane Library， Embase， and Scopus databases， with a time frame of the establishment of the database to May 7， 2024. Literature was independently screened by 2 investigators， data were extracted according to critical appraisal and data extraction for systematic reviews of predictive model studies， and the risk of bias evaluation tool for predictive model studies was used to analyze the risk of bias and applicability of the included studies.

RESULTS

There were totally 7 studies， comprising 12 models. Among them， 11 models indicated an area under the subject operating characteristic curve of 0.600-0.908； 2 models indicated calibration. The common predictor variables of the included models were age， pre-chemotherapy neutrophil count， pre-chemotherapy lymphocyte count， and pre-chemotherapy albumin. The overall risk of bias of the 7 studies was high， which was mainly attributed to the flaws in the study design， insufficient sample sizes， inappropriate treatment of variables， non-reporting of missing data， and the lack of indicators for the assessment of the models， but the applicability was good.

CONCLUSIONS

The predictive performance of risk predictive models for chemotherapy-induced myelosuppression in breast cancer remains to be further enhanced， and the overall risk of model bias is high. Future studies should follow the specifications of model development and reporting， then combine machine learning algorithms to develop risk predictive models with good predictive performance， high stability， and low risk of bias， so as to provide a decision-making basis for the clinic.

关键词

Keywords

references

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