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

项目总结/摘要 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 在诊断后3年内死亡，可能是由于心血管（CV）事件的高发生率：心肌梗死 (MI)或者中风因腿部疼痛、行动不便和截肢而残疾的比例要高得多。 仅与PAD相关的医院护理费用就超过210亿美元。然而，关于长期生存的研究， PAD的CV和肢体结局以及现有治疗的影响仍然有限，这在很大程度上是由于 PAD诊断代码的准确性。我们的团队开发了一种使用自然语言处理的新方法 (NLP)在退伍军人健康管理局（VHA）内以高准确度识别PAD患者。 意义：外周动脉疾病：长期生存和结局研究（PEARLS）研究将 以多种方式推进PAD的科学知识。我们将使用我们的NLP工具来组装一个最大的 在世界范围内的PAD队列，并长期跟踪他们，以评估生存和临床结局的轨迹， 评价推荐治疗（药物、风险因素控制和血运重建）的利用率， 上述治疗与上述结局的相关性。总的来说，我们的工作将解决以下方面的重要差距： PAD的研究和产生的见解有关的战略，以改善在这一高风险人群的护理提供。 创新：使用基于信息学的方法，将一组新诊断的PAD患者聚集在一起， 一个大规模的综合保健系统具有很强的创新性。我们相信，我们的队列识别方法将 实现变革并促进用于研究、改善护理提供和未来临床试验的大数据分析。 具体目标：A1。使用新型NLP开发新诊断PAD的退伍军人的全国队列 算法A2.检查医疗和侵入性管理的模式，并确定患者和机构级别 相互关联A3.确定PAD的医学和侵入性管理对长期结局的影响。 方法学：我们将实施我们的NLP算法，以在VHA中识别新的PAD诊断患者， 2015-2020年，并获得临床和治疗相关变量的数据。我们将纵向跟踪我们的队列， 死亡率、CV事件（MI、卒中）和肢体事件（截肢）。我们将检查PAD治疗的使用情况 和风险因素控制，使用多水平模型识别患者水平和医院水平的治疗预测因子。 我们将使用离散生存模型来评估PAD治疗与长期结局的相关性。 实施/后续步骤：关键可交付成果将包括a）了解哪些患者群体处于 死亡率和不良结局的最大风险;（B）确定PAD治疗对长期 对决策有用的长期结果，以及c）对治疗中站点水平差异的评估 模式.我们设想，我们的研究结果将有助于我们制定全面的疾病管理计划， 提高护理质量，减少有效治疗使用方面的差距。

Project Summary/Abstract 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. We will use our NLP tool to assemble one of the largest cohorts of PAD in the world and follow them long-term to assess the trajectory of 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. Collectively, our work will address important gaps in PAD research and yield insights regarding strategies to improve 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. Develop a national cohort of Veterans with newly diagnosed PAD using a novel NLP algorithm. A2. Examine patterns of medical and invasive management and determine patient- and facility-level correlates. A3. Determine the impact of medical and invasive management of PAD on 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 discrete survival models to evaluate the association of PAD treatments with long-term outcomes. Implementation/Next Steps: Key deliverables will include a) an understanding of which patient groups are at greatest risk for mortality and adverse 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.