Study on the predictive model for the efficacy of neurokinin-1 receptor antagonists combined with 5-hydroxytryp-tamine 3 receptor antagonists and dexamethasone for preventing nausea and vomiting induced by highly emetogenic chemotherapy
- VernacularTitle:神经激肽1受体拮抗剂联合5-羟色胺3受体拮抗剂、地塞米松预防HEC相关性恶心呕吐的有效性预测模型研究
- Author:
Jingyue ZHANG
1
;
Hanxu ZHANG
1
;
Chong YANG
2
;
Yinjuan SUN
3
;
Diansheng ZHONG
3
;
Linlin ZHANG
3
;
Hengjie YUAN
1
Author Information
1. Dept. of Pharmacy，Tianjin Medical University General Hospital，Tianjin 300052，China
2. Dept. of Pharmacy，Tianjin Huanhu Hospital，Tianjin 300350，China
3. Dept. of Medical Oncology，Tianjin Medical University General Hospital，Tianjin 300052，China
- Publication Type:Journal Article
- Keywords:
highly emetogenic chemotherapy;
chemotherapy-induced nausea and vomiting;
neurokinin-1 receptor antagonist;
5-
- From:
China Pharmacy
2026;37(2):220-225
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To construct a predictive model for evaluating the efficacy of a triple antiemetic regimen （neurokinin- 1 receptor antagonist+5-hydroxytryptamine 3 receptor antagonist+dexamethasone） for preventing nausea and vomiting induced by highly emetogenic chemotherapy （HEC） based on interpretable deep learning algorithms. METHODS Clinical data of cancer patients who received HEC and were treated with the standard triple antiemetic regimen in the oncology department of Tianjin Medical University General Hospital from January 2018 to December 2022 were collected retrospectively. Demographic， clinical and metabolism-related variables were integrated. After data pre-processing， two deep learning algorithms （deep random forest and dense neural network） and four machine learning algorithms （support vector machine， categorical boosting， random forest and decision tree） were used to build predictive models. Subsequently， model performance evaluation and model interpretability analysis were conducted. RESULTS Among the six candidate models， the deep random forest model demonstrated the best predictive performance on the test set， with an area under the receiver operating characteristic curve of 0.850， an accuracy of 0.911， a precision of 0.805， a recall of 0.783， an F1 score of 0.793， and a Brier score of 0.075. Interpretability analysis revealed that creatinine clearance rate （Ccr） was the key predictive factor， and low Ccr levels， female gender， younger age， highly emetogenic drugs （particularly cisplatin-containing chemotherapy regimens）， and anticipatory nausea and vomiting were positively correlated with the risk of HEC-related nausea and vomiting. CONCLUSIONS The deep random forest model exhibits the best performance in predicting the efficacy of triple antiemetic regimen for preventing HEC-related nausea and vomiting. The key predictors in this model primarily include Ccr，anticipatory nausea and vomiting， gender， age， and highly emetogenic drugs.
