Machine Learning Algorithm For Improving Classification Of Myocardial Infarction Across A Diverse Population Using Us Veteran Electronic Health Record Data

Introduction: The majority of population-based studies of myocardial infarction (MI) rely on billing codes for classification. Classification algorithms employing machine learning (ML) increasingly used for phenotyping using electronic health record (EHR) data. Hypothesis: ML algorithms integrating billing and information from narrative notes extracted using natural language processing (NLP) can improve classification of MI compared to billing code algorithms. Improved classification will improve power to compare risk factors across population subgroups. Methods: Retrospective cohort study of nationwide Veterans Affairs (VA) EHR data. MI classified using 2 approaches: (1) published billing code algorithm, (2) published phenotyping pipeline incorporating NLP and ML. Results compared against gold standard chart review of MI outcomes in 308 Veterans. We also tested known association between high density lipoprotein cholesterol (HDL-C) and MI outcomes classified using the 2 approaches among Black and White Veterans, stratified by sex and race; prior study showed HDL-C less protective for Black compared to White individuals. Results: We studied 17,176,658 million Veterans, mean age 69 years, 94% male, 12% self-report Black, 71% White. The billing code algorithm classified MI at positive predictive value (PPV) 0.64 compared to the published ML approach, PPV 0.90; the latter classified a modestly higher percentage of non-White Veterans. Using ML algorithm for MI, we replicated a reduced protective effect of HDL-C in Black vs White male and female Veterans (Table); with the billing code algorithm no association was observed between low density lipoprotein cholesterol (LDL-C) or HDL-C with MI among Black female Veterans. Conclusions: Using nationwide VA data, application of an ML approach improved classification of MI particularly among non-White Veterans, resulting in improved power to study differences in association for MI risk factors among Black and White Veterans.