Peripheral Artery Disease: Long-term Survival & Outcomes Study (PEARLS)

背景：外周动脉疾病（PAD）是一种常见且高度病态的疾病。近25%的患者

Background: Peripheral artery disease (PAD) is a common and highly morbid condition. Nearly 25% of patients die within 3 years of diagnosis, likely due to a high incidence of cardiovascular (CV) events: myocardial infarction (MI) or stroke. A significantly larger proportion experience disability due to leg pain, poor mobility and amputation. The cost of PAD-related hospital care alone exceeds $21 billion. However, research regarding long-term survival, CV, and limb outcomes in PAD and the impact of existing treatments remain limited in large part due to the poor accuracy of PAD diagnosis codes. Our team has developed a novel approach using natural language processing (NLP) to identify PAD patients with a high degree of accuracy within the Veterans Health Administration (VHA). Significance: The Peripheral Artery Disease: Long-term Survival & Outcomes Study (PEARLS) study will advance scientific knowledge for PAD in several ways. Using our novel NLP tool to identify Veterans with PAD, we will examine the trajectory of long-term survival and clinical outcomes, evaluate utilization of recommended treatments (medications, risk factor control and revascularization) and the association of above treatments with the above outcomes. We will also examine disparities in PAD care and outcomes by race and ethnicity and determine the extent to which these disparities our due to access in high quality care. Collectively, our work will address important gaps in PAD research and yield insights for improving care delivery in this high-risk population. Innovation: The use of an informatics-based method to assemble a cohort of newly diagnosed PAD patients in a large integrated health system is highly innovative. We believe that our approach for cohort identification will be transformational and promote big data analytics for research, improving care delivery, and future clinical trials. Specific Aims: A1. Examine the trajectory of long-term outcomes of PAD and assess racial and ethnic disparities. A2. Examine patterns of medical and invasive management of PAD in the Veterans Health Administration A3. Determine the association of medical and invasive management with long-term outcomes Methodology: We will implement our NLP algorithm to identify patients with new PAD diagnosis in VHA during 2015-2020 and obtain data on clinical and treatment related variables. We will follow our cohort longitudinally for mortality, CV events (MI, stroke) and limb events (amputation). We will examine utilization of PAD treatments and risk factor control, identify patient-level and hospital-level predictors of treatment using multi-level models. We will use marginal structural models to evaluate the association of PAD treatments with long-term outcomes. Implementation/Next Steps: Key deliverables will include a) an assessment of long-term outcomes in PAD and identifying racial disparities in care and outcomes; (b) determining the relative impact of PAD treatments on long- term outcomes which can be useful for decision-making and c) an assessment of site-level variation in treatment patterns. We envision that our findings will help us develop comprehensive disease management program to improve quality of care and reduce disparities in use of effective treatments.