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

外周动脉疾病：长期生存

• 批准号: 10734991
• 负责人: Saket Girotra
• 金额: $ 68.03万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10734991
• 关键词: Algorithms; American; American Heart Association; Amputation; Ankle; Arteries; Attenuated; Big Data; Big Data Methods; Black race; Blood Pressure; Blood flow; Cardiovascular Diseases; Cardiovascular system; Caring; Cessation of life; Characteristics; Chronic Care; Chronic Disease; Clinical; Clinical Practice Guideline; Clinical Trials; Code; Coronary Arteriosclerosis; Data; Data Element; Decision Making; Diabetes Mellitus; Diagnosis; Disease Management; Disease Outcome; Disparity; Electronic Health Record; Ethnic Origin; Evaluation; Event; Face; Funding; Future; Glycosylated hemoglobin A; Health Services Research; Hispanic; Hospitals; Incidence; Informatics; Integrated Health Care Systems; International Classification of Disease Codes; Knowledge; Laboratories; Leg; Life Style; Limb structure; Longitudinal cohort; Medical; Methodology; Methods; Myocardial Infarction; Natural Language Processing; Newly Diagnosed; Outcome; Outcome Study; Pain in lower limb; Patient Selection; Patient-Focused Outcomes; Patients; Pattern; Peripheral arterial disease; Pharmaceutical Preparations; Prediction of Response to Therapy; Predictive Value; Probability; Quality of Care; Race; Recommendation; Reporting; Research; Risk; Risk Factors; Science; Scientific Advances and Accomplishments; Selection Bias; Sensitivity and Specificity; Severity of illness; Site; Socioeconomic Factors; Specificity; Stroke; Structural Models; Symptoms; System; Testing; Time; Toes; Treatment Factor; United States Department of Veterans Affairs; Variant; Vascularization; Veterans; Veterans Health Administration; Work; black patient; care delivery; cohort; comorbidity; cost; critical limb Ischemia; disability; discrete data; disease diagnosis; disparity reduction; effective therapy; ethnic disparity; experience; glycemic control; health care disparity; high risk; high risk population; hospital care; improved; indexing; innovation; insight; mortality; multilevel analysis; novel; novel strategies; programs; racial disparity; racial diversity; research and development; time use; tool; treatment pattern

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.

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。使用我们新颖的 NLP 工具来识别患有 PAD 的退伍军人， 我们将检查长期生存和临床结果的轨迹，评估推荐的使用情况 治疗（药物、危险因素控制和血运重建）以及上述治疗与 上述结果。我们还将研究不同种族和族裔之间 PAD 护理和结果的差异，以及 确定这些差异在多大程度上是由于获得高质量护理而造成的。总的来说，我们的工作将 填补 PAD 研究中的重要空白，并为改善这一高危人群的护理服务提供见解。 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。检查 PAD 长期结果的轨迹并评估种族和民族 差异。 A2。检查退伍军人健康中心 PAD 的医疗和侵入性管理模式 管理A3。确定医疗和侵入性治疗与长期结果的关联 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。 我们将使用边际结构模型来评估 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) 对 PAD 长期成果的评估和 确定护理和结果方面的种族差异； (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。