Development of a prediction models for chemotherapy-induced adverse drug reactions: A retrospective observational study using electronic health records

医学 观察研究 健康档案 药物反应 回顾性队列研究 药品 内科学 不利影响 病历 重症监护医学 化疗 肿瘤科 药理学 医疗保健 经济 经济增长
作者
Jeongah On,Hyeoun‐Ae Park,Sooyoung Yoo
出处
期刊:European Journal of Oncology Nursing [Elsevier BV]
卷期号:56: 102066-102066 被引量:24
标识
DOI:10.1016/j.ejon.2021.102066
摘要

Chemotherapy-induced adverse drug reactions (ADRs) are common and diverse, and not only affect changes or interruptions to treatment schedules, but also negatively affect the patient's quality of life. This study aimed to predict eight chemotherapy-induced ADRs based on electronic health records (EHR) data using machine-learning algorithms.We used EHR data of 6812 chemotherapy cycles for 935 adult patients receiving four different chemotherapy regimens (FOLFOX, 5-fluorouracil + oxaliplatin + leucovorin; FOLFIRI, 5-fluorouracil + irinotecan + leucovorin; paclitaxel; and GP, gemcitabine + cisplatin) at a tertiary teaching hospital between January 2015 and June 2016. The predicted ADRs included nausea-vomiting, fatigue-anorexia, diarrhea, peripheral neuropathy, hypersensitivity, stomatitis, hand-foot syndrome, and constipation. Three machine learning algorithms were used to developed prediction models: logistic regression, decision tree, and artificial neural network. We compared the performance of the models with area of under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy.The AUCs of the logistic regression, decision tree, and artificial neural network models were 0.62-0.83, 0.61-0.83, and 0.62-0.83, respectively, and the accuracies were 0.59-0.84, 0.55-0.88, and 0.57-0.88, respectively. Among the algorithms, the logistic regression models performed best and had the highest AUC for six ADRs (range 0.67-0.83). The nausea-vomiting prediction models performed best with an AUC of 0.83 for the three algorithms.The prediction models for chemotherapy-induced ADRs were able to predict eight ADRs using EHR data. The logistic regression models were best suited to predict ADRs. The models developed in this study can be used to predict the risk of ADRs in patients receiving chemotherapy.
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