A Risk-Factor Model for Antineoplastic Drug-Induced Serious Adverse Events in Cancer Inpatients: A Retrospective Study Based on the Global Trigger Tool and Machine Learning

作者全名："Zhang, Ni; Pan, Ling-Yun; Chen, Wan-Yi; Ji, Huan-Huan; Peng, Gui-Qin; Tang, Zong-Wei; Wang, Hui-Lai; Jia, Yun-Tao; Gong, Jun"

作者地址："[Zhang, Ni; Ji, Huan-Huan; Jia, Yun-Tao] Childrens Hosp Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Pharm, Chongqing Key Lab Pediat,Minist Educ Key Lab Child, Chongqing, Peoples R China; [Zhang, Ni; Jia, Yun-Tao] Chongqing Med Univ, Sch Pharm, Chongqing, Peoples R China; [Pan, Ling-Yun; Chen, Wan-Yi; Peng, Gui-Qin; Tang, Zong-Wei] Chongqing Univ Canc Hosp, Dept Pharm, Chongqing, Peoples R China; [Wang, Hui-Lai; Gong, Jun] Chongqing Med Univ, Univ Town Hosp, Dept Informat Ctr, Chongqing, Peoples R China"

通信作者："Jia, YT (通讯作者)，Childrens Hosp Chongqing Med Univ, Natl Clin Res Ctr Child Hlth & Disorders, Dept Pharm, Chongqing Key Lab Pediat,Minist Educ Key Lab Child, Chongqing, Peoples R China.; Jia, YT (通讯作者)，Chongqing Med Univ, Sch Pharm, Chongqing, Peoples R China.; Wang, HL; Gong, J (通讯作者)，Chongqing Med Univ, Univ Town Hosp, Dept Informat Ctr, Chongqing, Peoples R China."

来源：FRONTIERS IN PHARMACOLOGY

ESI学科分类：PHARMACOLOGY & TOXICOLOGY

WOS号：WOS:000826872900001

JCR分区：Q1

影响因子：5.6

年份：2022

卷号：13

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：antineoplastic drugs; machine learning; serious adverse events; Global Trigger Tool; prediction

摘要："The objective of this study was to apply a machine learning method to evaluate the risk factors associated with serious adverse events (SAEs) and predict the occurrence of SAEs in cancer inpatients using antineoplastic drugs. A retrospective review of the medical records of 499 patients diagnosed with cancer admitted between January 1 and December 31, 2017, was performed. First, the Global Trigger Tool (GTT) was used to actively monitor adverse drug events (ADEs) and SAEs caused by antineoplastic drugs and take the number of positive triggers as an intermediate variable. Subsequently, risk factors with statistical significance were selected by univariate analysis and least absolute shrinkage and selection operator (LASSO) analysis. Finally, using the risk factors after the LASSO analysis as covariates, a nomogram based on a logistic model, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), adaptive boosting (AdaBoost), light-gradient-boosting machine (LightGBM), random forest (RF), gradient-boosting decision tree (GBDT), decision tree (DT), and ensemble model based on seven algorithms were used to establish the prediction models. A series of indicators such as the area under the ROC curve (AUROC) and the area under the PR curve (AUPR) was used to evaluate the model performance. A total of 94 SAE patients were identified in our samples. Risk factors of SAEs were the number of triggers, length of stay, age, number of combined drugs, ADEs occurred in previous chemotherapy, and sex. In the test cohort, a nomogram based on the logistic model owns the AUROC of 0.799 and owns the AUPR of 0.527. The GBDT has the best predicting abilities (AUROC = 0.832 and AUPR = 0.557) among the eight machine learning models and was better than the nomogram and was chosen to establish the prediction webpage. This study provides a novel method to accurately predict SAE occurrence in cancer inpatients."

基金机构："Intelligent Medicine Project of Chongqing Medical University [ZHYX2019005]; Program for Youth Innovation in Future Medicine, Chongqing Medical University [W0081]; Chongqing Clinical Pharmacy Key Specialty Construction Project; Key Project of Chongqing Science and Health Joint Medical Scientific Research Project [2022ZDXM020]"

基金资助正文："This work was supported by the Intelligent Medicine Project of Chongqing Medical University (ZHYX2019005), Program for Youth Innovation in Future Medicine, Chongqing Medical University (W0081), Chongqing Clinical Pharmacy Key Specialty Construction Project, and Key Project of Chongqing Science and Health Joint Medical Scientific Research Project (2022ZDXM020)."