Systematic review of risk predictive models for chemotherapy-induced myelosuppression in breast cancer
- VernacularTitle:乳腺癌化疗致骨髓抑制风险预测模型的系统评价
- Author:
Yang LIU
1
,
2
;
Hongjian LI
1
;
Jianhua WU
1
;
Xuetao LIU
2
;
Min JIAO
1
;
Luhai YU
1
Author Information
1. Dept. of Pharmacy，Xinjiang Uygur Autonomous Region People’s Hospital，Urumqi 830001，China
2. School of Pharmacy，Shihezi University，Xinjiang Shihezi 832000，China
- Publication Type:Journal Article
- Keywords:
breast cancer;
chemotherapy;
myelosuppression;
risk predictive model;
systematic review
- From:
China Pharmacy
2025;36(5):612-618
- CountryChina
- Language:Chinese
-
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.
