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

外周动脉疾病：长期生存

• 批准号: 10275610
• 负责人: Saket Girotra
• 金额: $ 17.21万
• 依托单位: UNIVERSITY OF IOWA
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-20 至 2022-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10275610
• 关键词: Address; Algorithms; American; Amputation; Ankle; Arteries; Big Data; Big Data Methods; Biometry; Blood Pressure; Blood Vessels; Blood flow; Cardiology; Cardiovascular system; Caring; Cessation of life; Chronic Care; Chronic Disease; Clinical; Clinical Data; Clinical Practice Guideline; Clinical Treatment; Clinical Trials; Code; Coronary Arteriosclerosis; Data; Data Element; Decision Making; Diabetes Mellitus; Diagnosis; Disease Management; Disease Outcome; Electronic Health Record; Epidemiology; Ethnic Origin; Event; Face; Funding; Future; Glycosylated hemoglobin A; Guidelines; Health system; Hospitals; Incidence; Income; Informatics; Integrated Health Care Systems; International Classification of Disease Codes; Kidney Failure; Knowledge; Laboratories; Leg; Life Style; Limb structure; Longitudinal cohort; Low income; Machine Learning; Measures; Medical; Methodology; Methods; Modeling; Myocardial Infarction; Natural Language Processing; Newly Diagnosed; Operative Surgical Procedures; Outcome; Outcome Study; Pain in lower limb; Patient-Focused Outcomes; Patients; Pattern; Peripheral arterial disease; Pharmaceutical Preparations; Predictive Value; Primary Health Care; Quality of Care; Race; Reporting; Research; Risk; Risk Factors; Science; Scientific Advances and Accomplishments; Severities; Site; Specificity; Stroke; Symptoms; Testing; Toes; Treatment Factor; United States; Variant; Veterans; Veterans Health Administration; Work; adverse outcome; base; care delivery; care outcomes; cohort; comorbidity; cost; critical limb Ischemia; disability; discrete data; disease diagnosis; disparity reduction; effective therapy; ethnic minority population; experience; glycemic control; high risk; high risk population; improved; indexing; innovation; insight; limb amputation; mortality; mortality risk; multilevel analysis; novel; novel strategies; patient subsets; programs; stroke event; survival outcome; time use; tool

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.

项目概要/摘要 背景：外周动脉疾病（PAD）是一种常见且发病率很高的疾病。近25%的患者 诊断后 3 年内死亡，可能是由于心血管 (CV) 事件发生率高：心肌梗死 (MI) 或中风。很大一部分人因腿部疼痛、活动能力差和截肢而遭受残疾。 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 的心血管和肢体结果以及现有治疗的影响在很大程度上仍然有限，因为治疗效果不佳 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颖方法 (NLP) 在退伍军人健康管理局 (VHA) 内高精度地识别 PAD 患者。 意义：外周动脉疾病：长期生存和结果研究 (PEARLS) 研究将 通过多种方式推进 PAD 的科学知识。我们将使用我们的 NLP 工具来组装最大的之一 对世界各地的 PAD 队列进行长期跟踪以评估生存轨迹和临床结果， 评估推荐治疗的使用情况（药物、危险因素控制和血运重建）以及 上述治疗与上述结果的关联。总的来说，我们的工作将解决以下方面的重要差距： PAD 研究并得出有关改善这一高危人群护理服务的策略的见解。 创新：使用基于信息学的方法来收集一组新诊断的 PAD 患者 大型综合卫生系统具有高度创新性。我们相信，我们的队列识别方法将 进行变革并促进大数据分析用于研究、改善护理服务和未来的临床试验。 具体目标：A1。使用新型 NLP 开发全国新诊断 PAD 退伍军人队列 算法。 A2。检查医疗和侵入性管理模式并确定患者和设施级别 相关。 A3。确定 PAD 的医疗和侵入性治疗对长期结果的影响。 方法：我们将实施我们的 NLP 算法来识别 VHA 期间新诊断出 PAD 的患者 2015-2020 年并获取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、心血管事件（心肌梗死、中风）和肢体事件（截肢）。我们将检查 PAD 治疗的利用情况 和风险因素控制，使用多级模型确定患者级别和医院级别的治疗预测因素。 我们将使用离散生存模型来评估 PAD 治疗与长期结果的关联。 实施/后续步骤：关键交付成果将包括 a) 了解哪些患者群体处于 死亡和不良后果的最大风险； (b) 确定 PAD 治疗对长期治疗的相对影响 对决策有用的术语结果和 c) 对处理中的地点水平变化的评估 模式。我们预计我们的研究结果将帮助我们制定全面的疾病管理计划 提高护理质量并减少使用有效治疗方法的差异。