Development and evaluation of nomogram prediction model for refractory chemotherapy-induced nausea and vomiting
- VernacularTitle:难治性化疗所致恶心呕吐的列线图预测模型建立与评估
- Author:
Bo SUN
1
;
Shufang LI
1
;
Xun LIU
2
;
Lu CHEN
3
;
Erfeng ZHANG
1
;
Huipin WANG
1
Author Information
1. Dept. of Pharmacy，the Third People’s Hospital of Zhengzhou，Zhengzhou 450099，China
2. Dept. of Pharmacy，Zhengzhou Second Hospital，Zhengzhou 450006，China
3. Dept. of Respiratory Oncology，the Third People’s Hospital of Zhengzhou，Zhengzhou 450099，China
- Publication Type:Journal Article
- Keywords:
chemotherapy-induced nausea and vomiting;
refractory;
prediction model;
nomogram
- From:
China Pharmacy
2025;36(9):1105-1110
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To construct and evaluate nomogram prediction model for refractory chemotherapy-induced nausea and vomiting （CINV）. METHODS 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 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 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.
