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

项目摘要/摘要 外周动脉疾病(PAD)，以四肢动脉病变为特征，影响着2亿人 慢性肾脏病(CKD)影响着美国的2000万人。 并使PAD的风险显著增加。然而，慢性肾脏病患者不太可能进行血运重建。 与没有慢性肾脏病的患者相比，患者更有可能接受下肢截肢手术。此外 由于高血压和糖尿病等传统危险因素的高发，CKD患者 有其他独特的危险因素，如慢性炎症或尿毒症，这反过来可能导致更多 攻击性垫在较小的年龄。因此，CKD患者需要专门的研究。我们的首要目标是 通过利用Optus的力量来帮助弥合这些证据差距并解决这些限制 Clinformatics Data Mart，其中包括超过70亿份关于所有50个生命中超过8300万个独立生命的索赔记录 跨越2005-2019年的州。我们的次要目标是通过使用真实世界的数据来促进未来的PAD研究 利用自然语言处理的能力来提高我们准确和自动识别的能力 从大型电子健康记录数据库中确定PAD。我们的创新算法将是特别的 在临床试验证据有限的亚组中的重要性，例如在晚期CKD中。我们的建议 有特定的目标。目的1：评价非透析患者的下肢血管重建术。 需要CKD。我们假设接受手术和血管内治疗的慢性肾脏病患者 血管重建术的初始住院时间较长，但随后的主要不良肢体事件较少。目标 2：评价下肢血管重建术后抗血小板和抗凝药物的应用 不需要透析的慢性肾脏病。我们假设现实世界中的慢性肾脏病患者接受抗血小板治疗 药物或直接口服抗凝剂在下肢血管重建后将有更高的发生率 出血，但较低的主要不良肢体事件发生率。目标3：开发一种准确和 自动从电子健康记录数据库中确定PAD。我们假设一种自然语言 处理-应用于诊断血管测试报告的方法将具有更好的测试性能(即 敏感性、特异性、阳性预测值和阴性预测值)诊断PAD优于传统方法 使用管理帐单代码。手动海图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.