Risk prediction models for chemotherapy-induced nausea and vomiting: A systematic review and meta-analysis

Xuemei Xie^1*Hang Li¹Yue Li²Han Fu³Yunqiong Wang¹Jia Cheng^1*

• ¹Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China
• ²Sichuan University West China Second University Hospital, Chengdu, China
• ³The Southwest Hospital of AMU, Chongqing, China

Objectives: To systematically review and critically appraise currently available risk prediction models for chemotherapy-induced nausea and vomiting (CINV). Methods: We searched nine electronic databases from inception to April 2025. Data extraction followed the CHARMS checklist. Risk of bias and applicability were assessed using the PROBAST tool, and reporting transparency was evaluated against the TRIPOD statement. Results: 15 studies describing 16 distinct CINV risk prediction models were included. Reported area under the curve (AUC) values ranged from 0.629 to 0.850. Frequently incorporated predictors included age, gender, history of anticipatory nausea and vomiting, chemotherapy regimen, and number of chemotherapy cycles. All studies demonstrated a high risk of bias, primarily attributable to suboptimal data sources and inadequate reporting in the analytical domain. Meta-analysis of AUC values from eight development models yielded a pooled estimate of 0.74 (95% CI: 0.68-0.81), indicating moderate discrimination. Conclusions: Existing CINV risk prediction models exhibit significant methodological limitations and remain largely in the developmental phase. While common predictors emerge, controversies persist. Future research should prioritize developing novel models with larger sample sizes, rigorous methodology, multicenter external validation, enhanced clinical utility, and improved reporting transparency.