Artificial Intelligence for early Detection of Peripheral Artery Disease (AID-PAD)

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PROJECT SUMMARY / ABSTRACT Peripheral artery disease, an atherosclerotic disorder typically of the lower extremities, is a life threatening and debilitating condition affecting millions of Americans. Once diagnosed, medical management including initiation of antiplatelet therapy, lipid lowering medications, and behavioral therapy such as supervised exercise and smoking cessation have all been shown to significantly improve health outcomes for those with PAD. However, diagnosis of PAD can be difficult due to poor patient and provider awareness of the disease, a high prevalence of atypical symptoms and conflicting guideline recommendations on screening. Furthermore, despite having similar to higher prevalence of disease, Blacks, females and individuals in lower socioeconomic groups are diagnosed later in the disease process, contributing to poorer outcomes. To address low diagnosis rates we developed an artificial intelligence (AI)-based model to detect PAD prior to clinician diagnosis using vast amounts of electronic health record (EHR) data and advanced machine learning algorithms. However, for our technology to have real-world impact, there is a clear need to: 1) Validate performance of our AI-based PAD detection model across diverse clinical settings and populations (Aim 1), 2), Evaluate clinical utility of using an AI-based PAD screening tool and design effective clinical workflows to enhance net benefit and adoption (Aim 2), and 3) Evaluate the effect of an AI-based PAD screening tool on rates of PAD diagnosis and medical management patterns (Aim 3). Aim 1 will be conducted using EHR data from 3 clinical sites with distinctly different patient populations. Our final model will be validated using the unique American Family Cohort registry, a rich outpatient-based EHR dataset made up of patients from all 50 states, including nearly 1,000,000 rural residents and over 600,000 racial/ethnic minorities. We will perform rigorous evaluation of AI model bias using algorithmic fairness metrics. Using decision analysis we will evaluate model utility to ensure our models demonstrate positive net benefit prior to deployment and we will also employ a unique quality improvement and mixed methods approach to work with providers to develop clinical workflows that foster the use of AI for PAD detection and maximize model benefit. Lastly, using a stepped wedge clinical trial design we will perform a pragmatic analysis of the effect of an AI-based PAD screening tool on rates of PAD diagnosis and treatment. At the conclusion of this study, we will have developed an understanding of how an AI-based PAD screening tool can be used to improve PAD detection, reduce disparities in diagnosis rates, and improve medical management.