AUTHOR=Wanika Linda , Evans Neil D. , Chappell Michael J.
TITLE=Identification of small cell lung cancer patients who are at risk of developing common serious adverse event groups with machine learning
JOURNAL=Frontiers in Drug Safety and Regulation
VOLUME=3
YEAR=2023
URL=https://www.frontiersin.org/journals/drug-safety-and-regulation/articles/10.3389/fdsfr.2023.1267623
DOI=10.3389/fdsfr.2023.1267623
ISSN=2674-0869
ABSTRACT=Introduction: 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.
Aim: 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.
Methods: Data from five clinical trial studies were analysed and 12 analysis groups were formed based on the serious adverse event group and grade.
Results: 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).
Conclusion: 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.