Abstract 15999: Machine Learning to Identify Patients at High Risk for Peripheral Arterial Disease From Electronic Health Record Data

Introduction: Peripheral arterial disease (PAD) is a common and underdiagnosed disease associated with significant morbidity and increased risk of major adverse cardiovascular events. Targeted screening of individuals at high risk for PAD could facilitate early diagnosis and allow for prompt initiation of interventions aimed at reducing cardiovascular and limb events. However, no widely accepted PAD risk stratification tools exist. Hypothesis: We hypothesized that machine learning algorithms can identify patients at high risk for PAD, defined by ankle-brachial index (ABI) Methods: Using data from the Vanderbilt University Medical Center EHR, ABIs were extracted for 8,093 patients not previously diagnosed with PAD at the time of initial testing. A total of 76 patient characteristics, including demographics, vital signs, lab values, diagnoses, and medications were analyzed using both a random forest and least absolute shrinkage and selection operator (LASSO) regression to identify features most predictive of ABI Results: The machine learning models identified several features independently correlated with PAD (age, BMI, SBP, DBP, pulse pressure, anti-hypertensive medication, diabetes medication, smoking, and statin use). The test statistic produced by the logistic regression model was correlated with PAD status in our validation set. At a chosen threshold, the specificity was 0.92 and the positive predictive value was 0.73 in this high-risk population. Conclusions: Machine learning can be applied to build unbiased models that identify individuals at risk for PAD using easily accessible information from the EHR. This model can be implemented either through a high-risk flag within the medical record or an online calculator available to clinicians.