Wanika Linda, Evans Neil D, Chappell Michael J
School of Engineering, University of Warwick, Coventry, United Kingdom.
Front Drug Saf Regul. 2023 Sep 15;3:1267623. doi: 10.3389/fdsfr.2023.1267623. eCollection 2023.
Across multiple studies, the most common serious adverse event groups that Small Cell Lung Cancer (SCLC) patients experience, whilst undergoing chemotherapy treatment, are: Blood and Lymphatic Disorders, Infections and Infestations together with Metabolism and Nutrition Disorders. The majority of the research that investigates the relationship between adverse events and SCLC patients, focuses on specific adverse events such as neutropenia and thrombocytopenia. This study aims to utilise machine learning in order to identify those patients who are at risk of developing common serious adverse event groups, as well as their specific adverse event classification grade. Data from five clinical trial studies were analysed and 12 analysis groups were formed based on the serious adverse event group and grade. The best test runs for each of the models were able to produce an area under the curve (AUC) score of at least 0.714. The best model was the Blood and Lymphatic Disorder group, SAE grade 0 vs. grade 3 (best AUC = 1, sensitivity rate = 0.84, specificity rate = 0.96). The top features that contributed to this prediction were total bilirubin, alkaline phosphatase, and age. Future work should investigate the relationship between these features and common SAE groups.
在多项研究中，小细胞肺癌（SCLC）患者在接受化疗治疗时经历的最常见严重不良事件组为：血液和淋巴系统疾病、感染和寄生虫感染以及代谢和营养紊乱。大多数研究不良事件与SCLC患者之间关系的研究都集中在特定的不良事件上，如中性粒细胞减少症和血小板减少症。本研究旨在利用机器学习来识别那些有发生常见严重不良事件组风险的患者，以及他们特定的不良事件分类等级。分析了来自五项临床试验研究的数据，并根据严重不良事件组和等级形成了12个分析组。每个模型的最佳测试运行能够产生至少0.714的曲线下面积（AUC）分数。最佳模型是血液和淋巴系统疾病组，严重不良事件等级0与等级3（最佳AUC = 1，灵敏度率 = 0.84，特异度率 = 0.96）。促成这一预测的首要特征是总胆红素、碱性磷酸酶和年龄。未来的工作应该研究这些特征与常见严重不良事件组之间的关系。
