Harnessing Big Data to Identify Effective Peripheral Artery Disease Treatments in Chronic Kidney Disease

利用大数据确定慢性肾脏病的有效外周动脉疾病治疗方法

• 批准号: 10580703
• 负责人: Tara I-Hsin Chang
• 金额: $ 35.42万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10580703
• 关键词: Acceleration; Address; Affect; Age; Algorithms; Amputation; Anticoagulants; Arteries; Aspirin; Atherosclerosis; Big Data; Biometry; Blood Platelets; Blood Vessels; Cardiac; Cardiovascular Diseases; Chronic; Chronic Kidney Failure; Clinical; Clinical Research; Clinical Trials; Code; Data; Data Mart; Data Science; Data Set; Databases; Dedications; Diabetes Mellitus; Diagnostic; Disease; Effectiveness; Electronic Health Record; End stage renal failure; Epidemiology; Evaluation; Event; Exclusion; Future; Gangrene; General Population; Goals; Healthcare; Healthcare Systems; Hemorrhage; High Prevalence; Home; Hospitalization; Hypertension; Inflammation; Kidney Diseases; Knowledge; Lead; Limb structure; Lower Extremity; Manuals; Medial; Medicine; Methods; Minerals; Modernization; Multicenter Studies; Natural Language Processing; Nephrology; Observational Study; Operative Surgical Procedures; Oral; Outcome Assessment; Patient Care; Patient-Focused Outcomes; Patients; Peripheral arterial disease; Persons; Pharmaceutical Preparations; Physicians; Population; Predictive Value; Procedures; Quality of life; Records; Reporting; Research; Risk Factors; Safety; Scientist; Specificity; Subgroup; Testing; Time; Uremia; Vascularization; calcification; career; cohort; cost; evidence base; experience; health related quality of life; high risk; improved; innovation; insight; non-healing wounds; novel; performance tests; programs; revascularization surgery; stroke event; virtual

PROJECT SUMMARY / ABSTRACT Peripheral artery disease (PAD), characterized by diseased arteries to the limbs, affects 200 million people worldwide and 9 million people in the U.S. Chronic kidney disease (CKD) affects 20 million people in the U.S. and confers a markedly higher risk for PAD. Yet patients with CKD are less likely to have revascularization procedures and are more likely to undergo lower extremity amputation than patients without CKD. In addition to a high prevalence of traditional risk factors such as hypertension and diabetes mellitus, patients with CKD have other unique risk factors such as chronic inflammation or uremia, which in turn can lead to more aggressive PAD at a younger age. Therefore, patients with CKD need dedicated study. Our overarching goal is to help close these evidence gaps and address these limitations by harnessing the power of Optum Clinformatics Data Mart, which includes over 7 billion claims records on over 83 million unique lives from all 50 states spanning 2005-2019. Our secondary goal is to facilitate future PAD studies using real-world data by leveraging the power of natural language processing to improve our ability to accurately and automatically ascertain PAD from large electronic health record databases. Our innovative algorithm will be of particular importance among subgroups where clinical trial evidence is limited, such as in advanced CKD. Our proposal has the Specific Aims. Aim 1: To evaluate lower extremity revascularization in patients with non-dialysis- requiring CKD. We hypothesize that patients with CKD undergoing surgical versus endovascular revascularization will have longer initial hospitalization, but fewer subsequent major adverse limb events. AIM 2: To evaluate antiplatelet and anticoagulant medications after lower extremity revascularization in patients with non-dialysis-requiring CKD. We hypothesize that real-world patients with CKD treated with antiplatelet medications or direct oral anticoagulants after lower extremity revascularization will have higher rates of bleeding but lower rates of major adverse limb events. AIM 3: To develop an algorithm that accurately and automatically ascertains PAD from electronic health record databases. We hypothesize that a natural language processing-approach applied to diagnostic vascular testing reports will have better test performance (i.e. sensitivity, specificity, positive and negative predictive values) for identifying PAD than a traditional approach that uses administrative billing codes. Manual chart review will serve as the gold standard.

项目摘要 /摘要 外围动脉疾病（PAD），其特征是四肢患病的动脉，影响了2亿人 在全球范围内，美国慢性肾脏疾病（CKD）在美国影响了2000万人 并赋予了垫子的明显更高的风险。但是CKD患者不太可能患有血运重建 与没有CKD的患者相比，程序，更有可能接受下肢截肢。此外 高血压和糖尿病等传统危险因素的患病率很高，患有CKD的患者 还有其他独特的危险因素，例如慢性炎症或尿毒症，这又可能导致更多 年轻时的积极进取。因此，CKD患者需要专门的研究。我们的总体目标是 通过利用optum的力量来帮助缩小这些证据差距并解决这些限制 Clinformatics Data Mart，其中包括超过70亿次超过8300万个独特生命的索赔记录 跨越2005-2019的国家。我们的次要目标是使用现实世界数据来促进未来的PAD研究 利用自然语言处理的力量，以提高我们的准确和自动的能力 从大型电子健康记录数据库中确定垫。我们的创新算法将特别 临床试验证据有限的亚组的重要性，例如高级CKD。我们的建议 具有具体目标。目的1：评估非透析患者的下肢血运重建 需要CKD。我们假设CKD患者接受手术与血管内的患者 血运重建的初始住院时间将更长，但随后的主要不良肢体事件较少。目的 2：评估患者下肢血运重建后抗血小板和抗凝药物 与非透析的CKD。我们假设用抗血小板治疗的CKD的现实世界患者 下肢血运重建后的药物或直接口服抗凝剂将具有更高的率 主要不良肢体事件的出血但较低的发生率。目标3：要准确地开发一种算法 从电子健康记录数据库中自动确定PAD。我们假设是一种自然语言 适用于诊断性血管测试报告的处理方法将具有更好的测试性能（即 识别垫的敏感性，特异性，正面和阴性预测值）比传统方法 使用管理计费代码。手动图表审查将作为黄金标准。