Precision health for early detection of peripheral artery disease

Peripheral artery disease (PAD) affects > 200 million individuals worldwide and costs billions of dollar a year to treat. Often patients are not diagnosed in a timely manner, leading to limb loss and increased cost of care. The Ross Lab hopes to change that through using artificial intelligence and big data to develop more precise risk stratification algorithms that can be used in real time, and increasing guideline-recommended treatments.

Ross Lab proposes to characterize the performance of machine learning algorithms in identifying patients with peripheral artery disease (PAD) using EHR data (Aim 1), evaluate whether learned classification models perform better than traditional risk factors for identification of undiagnosed PAD in a prospective patient cohort (Aim 2), and implement an EHR-based screening tool to identify patients with undiagnosed PAD and evaluate the diagnosis and treatment effects (Aim 3). Completion of the proposed research will result in a novel, EHR-based screening tool for identification of undiagnosed vascular disease that can decrease PAD-related cardiovascular morbidity and mortality through earlier and more aggressive medical management. This research will also form the basis for an R01 application before the end of the award to conduct a multi-site randomized-controlled clinical trial to evaluate the impact of EHR- based proactive PAD screening. See R01 description.