三种急性化疗相关性恶心呕吐预测工具的比较

目的 比较3种工具在肿瘤静脉化疗患者急性化疗相关恶心呕吐（chemotherapy-induced nausea and vomiting，CINV）风险评估中的预测价值。方法 采用便利抽样法，选取2022年1月至2022年12月在浙江省肿瘤医院肝胆胰肿瘤内科住院化疗的626例肿瘤患者为研究对象。应用Geoge团队的CINV风险预测工具、Dranitsaris评分系统、CINV列线图模型分别预测患者急性CINV发生，绘制受试者操作特征曲线，计算曲线下面积、敏感度、特异性和约登指数以比较其预测效能。结果 最终纳入研究的患者有622例，有效率为99.36%。发生CINV的患者有318例，占比51.13%：其中2级及以上急性期CINV者18.17%（113/622）。使用3种工具对急性期CINV风险评估：Geoge团队的CINV风险预测工具、Dranitsaris评分系统、CINV列线图模型AUC分别为0.591、0.616、0.558，Dranitsaris评分系统敏感度最高、George团队的CINV风险预测工具特异性最高。结论 肿瘤患者化疗后CINV发生率处于较高水平，Geoge团队的CINV风险预测工具、Dranitsaris评分系统、CINV列线图模型预测对国内肿瘤患者急性期CINV预测准确度略低，需构建本土化的、适用于多病种的标准化CINV风险评估模型。

Comparison of the predictive value of three risk assessment tools on the chemotherapy-induced nausea and vomiting

Objective To explore the comparison of the predictive value of three risk assessment tools on the chemotherapy-induced nausea and vomiting (CINV) in cancer patients. Methods From January 2022 to December 2022, convenience sampling was used to select 626 cancer patients with Intravenous chemotherapy in the Department of Hepatobiliary Pancreatic Oncology of Zhejiang Cancer Hospital as the research object. CINV risk assessment of patients was performed using George teams acute CINV prediction tool, Dranitsari’s CINV risk assessment and CINV nomogram model. Area under curve(AUC), sensitivity, specificity and Youden index were used to compare the predictive value of the three tools. Results Totally 622 patients were ultimately included in the study, with an overall effective rate of 99.36%. There were 51.13% (318/622)patients who experienced CINV. Specifically, patients with grade 2 or higher acute CINV accounted for 18.17% (113/622). When using the three tools for acute CINV risk assessment, the AUC was respectively 0.591,0.616 and 0.558. And Dranitsari’s CINV risk assessment has the highest sensitivity, acute and delayed chemotherapy-induced nausea and vomiting prediction tool has the highest specificity. Comparatively, Dranitsari’s CINV risk assessment on the Yorden index is better. Conclusion The incidence of CINV in cancer patients is at a high level. The three tools can not effectively predict the risk of acute CINV. We need to develop a localized, multi-disease, standardized CINV risk assessment model for hospital.