International journal of nursing studies · 2025
Risk prediction model for chemotherapy-induced nausea and vomiting in cancer patients: a systematic review.
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Wang Yongjian, Zheng Ruishuang, Wu Yunting, Liu Ting, Hao Liqian, Liu Jue, Shi Lili, Guo Qing
Аннотация
BACKGROUND: 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.
OBJECTIVES: To systematically review and evaluate existing studies on risk prediction models for chemotherapy-induced nausea and vomiting in cancer patients.
METHODS: 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).
RESULTS: 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.
CONCLUSION: 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.
REGISTRATION: The protocol for this study is registered with PROSPERO (registration number: CRD42024505012).
OBJECTIVES: To systematically review and evaluate existing studies on risk prediction models for chemotherapy-induced nausea and vomiting in cancer patients.
METHODS: 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).
RESULTS: 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.
CONCLUSION: 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.
REGISTRATION: The protocol for this study is registered with PROSPERO (registration number: CRD42024505012).