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

项目总结/摘要 外周动脉疾病（PAD）的特征是四肢动脉病变，影响2亿人 慢性肾脏病（CKD）影响着美国2000万人。 并且赋予PAD显著更高的风险。然而，CKD患者进行血运重建的可能性较小， 与无CKD的患者相比，CKD患者更容易接受下肢截肢手术。此外 传统危险因素如高血压和糖尿病的高患病率，CKD患者 有其他独特的风险因素，如慢性炎症或尿毒症，这反过来又会导致更多的 在年轻的时候有侵略性的PAD。因此，CKD患者需要专门的研究。我们的首要目标是 通过利用Optum的力量，帮助缩小这些证据差距并解决这些局限性 Clinformatics数据集市，其中包括超过70亿索赔记录，超过8300万独特的生活，从所有50 2005年至2019年的国家。我们的第二个目标是促进未来的PAD研究使用真实世界的数据， 利用自然语言处理的力量来提高我们准确和自动地 从大型电子健康记录数据库中确定PAD。我们的创新算法将特别 在临床试验证据有限的亚组中，如在晚期CKD中的重要性。我们的建议 有具体的目标。目的1：评价非透析患者的下肢血运重建， 需要CKD。我们假设接受手术与血管内治疗的CKD患者 血运重建的初始住院时间较长，但随后的主要不良肢体事件较少。目的 2：评价患者下肢血运重建后的抗血小板和抗凝药物 不需要透析的慢性肾脏病患者我们假设，在现实世界中，接受抗血小板治疗的CKD患者 药物或直接口服抗凝剂在下肢血运重建后的发生率更高， 出血但主要不良肢体事件发生率较低。目的3：开发一种算法， 自动从电子健康记录数据库中删除PAD。我们假设一种自然语言 应用于诊断血管测试报告的处理方法将具有更好的测试性能（即， 灵敏度、特异性、阳性和阴性预测值）用于识别PAD 使用管理计费代码的公司手动病历审查将作为黄金标准。

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.