Grant: Doctoral Research Award: Canada Graduate Scholarships

Peripheral arterial disease (PAD) is a devastating cardiovascular disease which occurs when there is plaque build-up within the arteries of the lower legs. It can lead to heart attacks, strokes, the need for surgery, leg amputation, and sometimes death. The current method for diagnosing patients with PAD is through the measurement of the blood pressure in the leg, and the arm, known as the ABI. If the blood pressure in the leg is lower than the arm, then this means the patient has PAD. Despite its prevalence, PAD remains under-diagnosed and sub-optimally managed, putting patients at increased risk of life-altering complications. Realizing the critical need for better diagnosis of PAD, the aim of this project to identify a panel of blood and urine proteins for PAD that, when integrated with machine learning models can diagnose patients with PAD, and predict the risk of adverse disease outcomes. I will be collaborating with a world-class network of researchers and physicians from four different hospitals across Ontario to collect blood and urine samples from PAD patients. Samples will be analyzed for levels of relevant proteins. Next, I will be linking our patient data to the Institute for Clinical Evaluative Sciences (IC/ES) database, from which I will access patient's past medical history, such as surgeries, heart attacks, strokes etc. Protein levels, and the IC/ES medical history data will be integrated to train our machine learning model to create a "PAD clinical calculator". Physicians will be able to use this calculator in order to diagnose PAD, and determine the risk of suffering from other events such as heart attacks, stroke, and death. This project will result in the creation of a strong, robust, and accurate model of PAD that can be used as a "clinical calculator", with many benefits: 1) a simpler, easier, and more convenient way to diagnose PAD, 2) for earlier diagnosis and treatment, and 3) as a tool to help predict PAD related outcomes.