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

基本信息

批准号：
10375593
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
项目类别：
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/10375593
关键词：
Address Affect Age Algorithms Amputation Anticoagulants Arteries Aspirin Atherosclerosis Big Data Biometry Blood Vessels Cardiac Cardiovascular Diseases Chronic Chronic Kidney Failure Clinical Clinical Research Clinical Trials Code Data Data Mart Data Science Data Set Databases Diabetes Mellitus Diagnostic Disease Effectiveness Electronic Health Record Epidemiology Evaluation Event Future Gangrene General Population Goals Gold Healthcare Healthcare Systems Hemorrhage High Prevalence Home Hospitalization Hypertension Inflammation Kidney 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 Patient Care Patient-Focused Outcomes Patients Peripheral arterial disease Persons Pharmaceutical Preparations Physicians Population Predictive Value Procedures Records Reporting Research Risk Factors Safety Scientist Sensitivity and Specificity Stroke Subgroup Testing Time Uremia calcification career cohort cost evidence base experience health related quality of life high risk improved innovation insight non-healing wounds novel performance tests programs virtual

项目摘要

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.

项目概要/摘要外周动脉疾病 (PAD) 以四肢动脉病变为特征，影响着 2 亿人全球有 900 万人患有慢性肾病 (CKD)，美国有 2000 万人受到慢性肾病 (CKD) 的影响。并显着增加患 PAD 的风险。然而 CKD 患者进行血运重建的可能性较小与没有 CKD 的患者相比，他们更有可能接受下肢截肢手术。此外慢性肾病患者高血压、糖尿病等传统危险因素的患病率较高有其他独特的危险因素，例如慢性炎症或尿毒症，这反过来又会导致更多年轻时具有侵袭性 PAD。因此，CKD患者需要专门研究。我们的总体目标是利用 Optum 的力量帮助缩小这些证据差距并解决这些限制 Clinformatics 数据集市，其中包括来自所有 50 个国家的超过 8300 万个独特生命的超过 70 亿条索赔记录 2005 年至 2019 年各州。我们的第二个目标是利用真实世界数据促进未来的 PAD 研究利用自然语言处理的力量来提高我们准确、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（例如晚期 CKD）中具有重要意义。我们的建议有具体目标。目标 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治疗相比血运重建将延长初始住院时间，但随后的主要肢体不良事件会减少。目的 2：评估患者下肢血运重建后的抗血小板和抗凝药物患有非透析需要的 CKD。我们假设现实世界的 CKD 患者接受抗血小板治疗下肢血运重建后药物或直接口服抗凝剂的发生率较高出血但主要不良肢体事件的发生率较低。目标 3：开发一种准确且准确的算法自动从电子健康记录数据库中确定 PAD。我们假设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（即与传统方法相比，识别 PAD 的灵敏度、特异性、阳性和阴性预测值使用管理帐单代码。手动图表审查将作为黄金标准。