Wang Yongjian, Zheng Ruishuang, Wu Yunting, Liu Ting, Hao Liqian, Liu Jue, Shi Lili, Guo Qing
Department of Hepatobiliary Oncology, Tianjin Medical University Cancer Hospital and Institute, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Department of Hepatobiliary Oncology, Tianjin Medical University Cancer Hospital and Institute, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin, China; Research Center of Liver Cancer Prevention and Treatment, Tianjin Medical University Cancer Hospital and Institute, National Clinical Research Centre for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Centre for Cancer, Tianjin, China.
Int J Nurs Stud. 2025 Aug;168:105094. doi: 10.1016/j.ijnurstu.2025.105094. Epub 2025 Apr 24.
Chemotherapy-induced nausea and vomiting increase the healthcare burden and lead to adverse clinical outcomes in cancer patients. Although many risk prediction models for chemotherapy-induced nausea and vomiting have been developed, their methodological quality and applicability remain uncertain.
To systematically review and evaluate existing studies on risk prediction models for chemotherapy-induced nausea and vomiting in cancer patients.
PubMed, the Cochrane Library, Embase, Web of science, CINAHL, Scopus, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Wanfang Database, Chinese Biomedical literature Database (CBM) were systematically searched from inception to October 1, 2024. Studies were appraised critically and data extracted by two authors independently based on the Prediction Model Risk of Bias Assessment Tool (PROBAST) and Data Extraction for Systematic Reviews of Prediction Modeling Studies (CHARMS).
A total of 4195 articles were retrieved, ultimately including 17 studies with 62 models for chemotherapy-induced nausea and vomiting. The sample size of the included studies ranged from 137 to 2215, with areas under the curve ranging from 0.602 to 0.850. In this study, the deep forest model demonstrated strong discrimination and calibration, outperforming conventional machine learning and traditional regression models. The five most important predictors in the deep forest model were creatinine clearance, age, sex, anticipatory nausea and vomiting, and antiemetic regimen. Across all included studies, age, chemotherapy regimens, cycles of chemotherapy, history of alcohol consumption, prior episodes of chemotherapy-induced nausea and vomiting, sleep quality before chemotherapy, sex, antiemetic regimens, history of morning sickness, anticipatory nausea and vomiting, were the most frequently reported predictors. All studies were rated as high risk of bias mainly due to poor reporting of the participants and analysis domains, with high concerns regarding applicability in 9 studies.
The research on prediction models for chemotherapy-induced nausea and vomiting model is in its developing stage, with both commonalities and differences in predictors. Despite the overall acceptable performance of chemotherapy-induced nausea and vomiting models, most studies have methodological shortcomings, and few models have been validated. Future studies should refer to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guideline for model design, implementation, and reporting. Moreover, studies with larger sample sizes and multicenter external validation are necessary to enhance the robustness of predictive models.
The protocol for this study is registered with PROSPERO (registration number: CRD42024505012).
化疗引起的恶心和呕吐增加了医疗负担，并导致癌症患者出现不良临床结局。尽管已经开发了许多用于预测化疗引起的恶心和呕吐的风险预测模型，但其方法学质量和适用性仍不确定。
系统评价和评估关于癌症患者化疗引起的恶心和呕吐风险预测模型的现有研究。
从创刊至2024年10月1日，系统检索了PubMed、Cochrane图书馆、Embase、科学网、护理学与健康领域数据库（CINAHL）、Scopus、中国知网（CNKI）、维普中文科技期刊数据库（VIP）、万方数据库、中国生物医学文献数据库（CBM）。由两名作者根据预测模型偏倚风险评估工具（PROBAST）和预测建模研究系统评价数据提取工具（CHARMS）独立对研究进行严格评价并提取数据。
共检索到4195篇文章，最终纳入17项研究，其中包含62个用于预测化疗引起的恶心和呕吐的模型。纳入研究的样本量从137到2215不等，曲线下面积从0.602到0.850。在本研究中，深度森林模型表现出较强的区分度和校准度，优于传统机器学习模型和传统回归模型。深度森林模型中五个最重要的预测因素是肌酐清除率、年龄、性别、预期性恶心和呕吐以及止吐方案。在所有纳入研究中，年龄、化疗方案、化疗周期、饮酒史、既往化疗引起的恶心和呕吐发作史、化疗前睡眠质量、性别、止吐方案、孕吐史、预期性恶心和呕吐是最常报告的预测因素。所有研究均被评为高偏倚风险，主要原因是参与者和分析领域的报告不佳，9项研究对适用性高度关注。
化疗引起的恶心和呕吐预测模型的研究尚处于发展阶段，预测因素既有共性也有差异。尽管化疗引起的恶心和呕吐模型的整体性能可接受，但大多数研究存在方法学缺陷，且很少有模型经过验证。未来的研究应参考个体预后或诊断多变量预测模型的透明报告（TRIPOD）指南进行模型设计、实施和报告。此外，需要进行更大样本量和多中心外部验证的研究，以提高预测模型的稳健性。
本研究方案已在国际前瞻性系统评价注册库（PROSPERO）注册（注册号：CRD42024505012）。
