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Development and evaluation of nomogram prediction model for refractory chemotherapy-induced nausea and vomiting
- ZHONGGUO YAOFANG Vol. 36, Issue 9, Pages: 1105-1110(2025)
- 作者机构：
1.郑州市第三人民医院药学部，郑州 450099
2.郑州市第二人民医院药学部，郑州 450006
3.郑州市第三人民医院呼吸肿瘤内科，郑州 450099
- 作者简介：
- 基金信息：
DOI：10.6039/j.issn.1001-0408.2025.09.15
CLC： R730.6;R975+.4Received：29 October 2024，
Revised：2025-03-26，
Accepted：27 March 2025，
Published：15 May 2025
- 稿件说明：
移动端阅览
孙博,李淑芳,刘勋,等.难治性化疗所致恶心呕吐的列线图预测模型建立与评估 [J].中国药房,2025,36(09):1105-1110.
SUN Bo,LI Shufang,LIU Xun,et al.Development and evaluation of nomogram prediction model for refractory chemotherapy-induced nausea and vomiting[J].ZHONGGUO YAOFANG,2025,36(09):1105-1110.
孙博,李淑芳,刘勋,等.难治性化疗所致恶心呕吐的列线图预测模型建立与评估 [J].中国药房,2025,36(09):1105-1110. DOI： 10.6039/j.issn.1001-0408.2025.09.15.
SUN Bo,LI Shufang,LIU Xun,et al.Development and evaluation of nomogram prediction model for refractory chemotherapy-induced nausea and vomiting[J].ZHONGGUO YAOFANG,2025,36(09):1105-1110. DOI： 10.6039/j.issn.1001-0408.2025.09.15.
摘要
目的
2
构建难治性化疗所致恶心呕吐（CINV）的列线图预测模型并进行评估。
方法
2
收集2017年1月－2023年12月于郑州市第三人民医院化疗的恶性肿瘤患者资料，根据是否发生难治性CINV分为发生组和未发生组。采用多因素Logistic回归分析筛选难治性CINV的预测因素并构建列线图预测模型；采用受试者工作特征曲线评估模型的预测性能；采用Bootstrap法评价模型的校准度；采用决策曲线分析（DCA）评估模型在不同风险阈值下3种策略的临床净收益；采用临床影响曲线评价模型在不同风险阈值下的临床价值；采用Shapley加性解释（SHAP）法评估各因素对预测模型的贡献度。
结果
2
共纳入388例患者，其中219例患者发生了难治性CINV。多因素Logistic回归分析结果显示，胃肠疾病史、预期性恶心呕吐、化疗致吐风险分级、电解质水平等11项因素是难治性CINV的预测因素。模型的曲线下面积为0.80[95%置信区间为（0.76，0.84）]，平均误差为0.036。DCA结果表明，当风险阈值为0.05～0.85时，预测模型的临床净收益较高。SHAP分析结果显示，胃肠疾病史（0.924）、化疗致吐风险分级（0.866）和电解质水平（0.581）是排前3名的预测因素。
结论
2
胃肠疾病史、预期性恶心呕吐、化疗致吐风险分级、电解质水平等11项因素是难治性CINV的预测因素。基于上述因素建立的模型预测能力较好，可用于预测难治性CINV的发生风险。
Abstract
OBJECTIVE
2
To construct and evaluate nomogram prediction model for refractory chemotherapy-induced nausea and vomiting （CINV）.
METHODS
2
The data of malignant tumor patients who received chemotherapy at the Third People’s Hospital of Zhengzhou from January 2017 to December 2023 were collected. These patients were categorized into the occurrence group and the non-occurrence group according to the occurrence of refractory CINV. Multivariate Logistic regression analysis was employed to screen predictive factors for refractory CINV and constructing a nomogram prediction model. Model performance was assessed via receiver operating characteristic curve analysis. Model calibration was evaluated using Bootstrap resampling. Decision curve analysis （DCA） was used to determine the clinical net benefit of three strategies under different risk thresholds. Clinical impact curves were utilized to assess the clinical value of the model at different risk thresholds. Shapley additive explanations （SHAP） analysis was performed to evaluate individual factor contributions to the predictive model.
RESULTS
2
A total of 388 patients were included， with 219 experiencing refractory CINV. Multivariate Logistic regression identified 11 predictive factors for refractory CINV， including gastrointestinal disease history， anticipated nausea and vomiting， chemotherapy-induced emetic risk classification， and electrolyte levels， etc. The model’s area under the curve was 0.80 [95% confidence interval （0.76， 0.84）]， with a mean error of 0.036. DCA demonstrated the prediction model had higher clinical net benefit when the risk threshold was between 0.05 and 0.85. SHAP analysis revealed the top three predictive factors as gastrointestinal disease history （0.924）， chemotherapy-induced emetic risk classification （0.866）， and electrolyte levels （0.581）.
CONCLUSIONS
2
Eleven factors， including gastrointestinal disease history， anticipated nausea and vomiting， chemotherapy-induced emetic risk classification， and electrolyte levels， are identified as predictors of refractory CINV. The model based on these factors has good predictive ability， which can be used to predict the risk of refractory CINV.
关键词
Keywords
references
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