Enriching the Study Population for Ischemic Stroke Therapeutic Trials Using a Machine Learning Algorithm

Abstract

BackgroundStrokes represent a leading cause of mortality globally. The evolution of developing new therapies is subject to safety and efficacy testing in clinical trials, which operate in a limited timeframe. To maximize the impact of these trials, patient cohorts for whom ischemic stroke is likely during that designated timeframe should be identified. Machine learning may improve upon existing candidate identification methods in order to maximize the impact of clinical trials for stroke prevention and treatment and improve patient safety.MethodsA retrospective study was performed using 41,970 qualifying patient encounters with ischemic stroke from inpatient visits recorded from over 700 inpatient and ambulatory care sites. Patient data were extracted from electronic health records and used to train and test a gradient boosted machine learning algorithm (MLA) to predict the patients' risk of experiencing ischemic stroke from the period of 1 day up to 1 year following the patient encounter. The primary outcome of interest was the occurrence of ischemic stroke.ResultsAfter training for optimization, XGBoost obtained a specificity of 0.793, a positive predictive value (PPV) of 0.194, and a negative predictive value (NPV) of 0.985. The MLA further obtained an area under the receiver operating characteristic (AUROC) of 0.88. The Logistic Regression and multilayer perceptron models both achieved AUROCs of 0.862. Among features that significantly impacted the prediction of ischemic stroke were previous stroke history, age, and mean systolic blood pressure.ConclusionMLAs have the potential to more accurately predict the near risk of ischemic stroke within a 1-year prediction window for individuals who have been hospitalized. This risk stratification tool can be used to design clinical trials to test stroke prevention treatments in high-risk populations by identifying subjects who would be more likely to benefit from treatment.

A Multi-Modal AI-Driven Cohort Selection Tool to Predict Suboptimal Non-Responders to Aflibercept Loading-Phase for Neovascular Age-Related Macular Degeneration: PRECISE Study Report 1

“…Patient selection and suitability is one of the key challenges of RCT design, and incorrect patient selection can be detrimental to the success of a trial. Artificial intelligence (AI) systems can be used to aid patient selection for a clinical trial by using the patient's predicted response to the trialled drug (predictive enrichment) and/or predicted clinical outcome (prognostic enrichment) [10][11][12].…”

A multi-modal AI-driven cohort selection tool based on response to loading-phase aflibercept for neovascular age-related macular degeneration: PRECISE study

“…Arti cial intelligence (AI) systems can be used to aid patient selection for a clinical trial by using the patient's predicted response to the trialled drug (predictive enrichment) and/or predicted clinical outcome (prognostic enrichment). [10][11][12] In this rst of a series of PRECISE study reports, we present and evaluate the e cacy of an AI-based cohort selection tool for the identi cation of patients for an enriched trial cohort based on their predicted suboptimal response to loading phase a ibercept, de ned as persistent macular uid post loading-phase.…”