Can machine learning augment clinician adjudication of events in cardiovascular trials? A case study of major adverse cardiovascular events (MACE) across CVRM trials. (14th October 2021)

• Record Type: Journal Article
• Authors: Lea, H; Hutchinson, E; Meeson, A; Nampally, S; Dennis, G; Wallander, M; Andersson, T; Persson, A; Johnston, S C; Weatherall, J; Khan, F; Khader, S
• Is Part Of: European heart journal. Volume 42(2021)Supplement 1
• Publication Date: 2021-10-14
• Subjects: Artificial Intelligence (Machine Learning, Deep Learning); Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehab724.3061
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background and introduction: Accurate identification of clinical outcome events is critical to obtaining reliable results in cardiovascular outcomes trials (CVOTs). Current processes for event adjudication are expensive and hampered by delays. As part of a larger project to more reliably identify outcomes, we evaluated the use of machine learning to automate event adjudication using data from the SOCRATES trial (NCT01994720), a large randomized trial comparing ticagrelor and aspirin in reducing risk of major cardiovascular events after acute ischemic stroke or transient ischemic attack (TIA). Purpose: We studied whether machine learning algorithms could replicate the outcome of the expert adjudication process for clinical events of ischemic stroke and TIA. Could classification models be trained on historical CVOT data and demonstrate performance comparable to human adjudicators? Methods: Using data from the SOCRATES trial, multiple machine learning algorithms were tested using grid search and cross validation. Models tested included Support Vector Machines, Random Forest and XGBoost. Performance was assessed on a validation subset of the adjudication data not used for training or testing in model development. Metrics used to evaluate model performance were Receiver Operating Characteristic (ROC), Matthews Correlation Coefficient, Precision and Recall. The contribution of features, attributes of data used by the algorithm as it is trained to classify an event, that contributed to a classification were examined using both Mutual Information and Recursive Feature Elimination. Results: Classification models were trained on historical CVOT data using adjudicator consensus decision as the ground truth. Best performance was observed on models trained to classify ischemic stroke (ROC 0.95) and TIA (ROC 0.97). Top ranked features that contributed to classification of Ischemic Stroke or TIA corresponded to site investigator decision or variables used to define the event in the trial charter, such as duration of symptoms. Model performance was comparable across the different machine learning algorithms tested with XGBoost demonstrating the best ROC on the validation set for correctly classifying both stroke and TIA. Conclusions: Our results indicate that machine learning may augment or even replace clinician adjudication in clinical trials, with potential to gain efficiencies, speed up clinical development, and retain reliability. Our current models demonstrate good performance at binary classification of ischemic stroke and TIA within a single CVOT with high consistency and accuracy between automated and clinician adjudication. Further work will focus on harmonizing features between multiple historical clinical trials and training models to classify several different endpoint events across trials. Our aim is to utilize these clinical trial datasets to optimize the delivery of CVOTs in further cardiovascular drug development. Funding Acknowledgement: Type of funding sources: Private company. Main funding source(s): AstraZenca Plc