Leveraging linked registry and electronic health records to examine long-term patient outcomes after peripheral vascular intervention"

利用关联的登记表和电子健康记录检查患者的长期预后 外周血管介入治疗 项目摘要/摘要 外周动脉疾病(PAD)影响着全球超过2亿人。外周血管介入治疗 (PVI)是管理PAD最常见的程序。现有随机对照 静脉注射后患者结局的试验(RCT)和观察研究都有有限的随访时间，原因是 长期数据收集方面的困难。此外，支架置入治疗效果的异质性(Hte) 与经皮腔内血管成形术(PTA)相比，目前还不能很好地理解 效果修改剂评价方法。真实世界数据(RWD)，特别是与电子数据链接的登记处 健康数据(EHR)，对于研究血管手术后的长期结果很有用。但是，方法 因为处理多个数据源和分析非结构化文本数据仍在不断发展。建议数 这项研究旨在解决PVI术后患者长期预后方面的现有证据差距。这将是 通过创新地应用和改进数据链接、自然语言处理(NLP)和效果来促进 修改量评估方法。具体地说，该项目将把登记处和电子健康记录数据联系起来，以1)长期审查 支架置入后的主要不良肢体事件与单纯PTA相比，以及评估治疗的异质性 2)用机器学习的方法开发NLP流水线进行分析 非结构化文本数据和检查支架置入后与单纯PTA后的长期疗效终点，以及；3) 确定部署NLP工具的可行性和更新要求，以期取得长期的重大利益攸关方成果 对其他机构的评估。为了支持研究活动和向独立的过渡， 候选人将在获奖期间从事以下职业发展活动：1)获得- 深入了解NLP和机器学习方法；2)提炼数据科学专业知识以整合 将EHR纳入医疗器械流行病学研究；3)加强对现有血管和新血管的知识 疾病治疗；4)发展和提高赠款撰写和学术领导能力；5)培训 负责任的研究行为。候选人将由一个专家团队进行指导，该团队将与 在手术和设备结果研究、自然语言处理和机器学习方面的优势，以及 血管疾病和外科手术。拟议的职业发展和研究活动将发展 应聘者的技能和专业知识，并导致R01级的申请。候选人的长期目标是 成为专注于先进多学科开发和应用的独立研究员 方法在血管疾病领域评估手术和设备的结果，支持临床， 耐心和监管决策。

Leveraging linked registry and electronic health records to examine long-term patient outcomes after peripheral vascular intervention Project Summary/Abstract Peripheral arterial disease (PAD) affects over 200 million people worldwide. Peripheral vascular interventions (PVI) are the most common procedures that are performed to manage PAD. Existing randomized controlled trials (RCTs) and observational studies of patient outcomes after PVIs all had limited follow-up lengths due to difficulties in long-term data collections. In addition, heterogeneity of treatment effect (HTE) for stent placement vs. percutaneous transluminal angioplasty (PTA) alone has not been well understood with the current approach of effect modifier assessment. Real-world data (RWD), particularly registries linked with electronic health data (EHR), are useful for studying long-term outcomes after vascular procedures. However, methods for working with multiple data sources and analyzing unstructured text data are still evolving. The proposed research aims to address current evidence gaps in long-term patient outcomes after PVI procedures. This will be facilitated by innovatively apply and refine data linkage, natural language processing (NLP), and effect modifier assessment methods. Specifically, this project will link registry and EHR data to 1) examine long-term major adverse limb events after stent placement vs. PTA alone as well as assess heterogeneity of treatment effect by patient characteristics; 2) develop an NLP pipeline with machine learning methods to analyze unstructured text data and examine long-term efficacy endpoints after stent placement vs. PTA alone, and; 3) establish feasibility and updating requirements for the deployment of the NLP tool for long-term PVI outcome assessment to other institutions. To support the research activities and the transition toward independence, the candidate will undertake the following career development activities during the award period: 1) gaining an in- depth understanding of NLP and machine learning methods; 2) refining data science expertise to integrate EHR into medical device epidemiologic research; 3) strengthening knowledge in current and novel vascular disease treatment; 4) developing and improving skills in grant writing and academic leadership; 5) training in responsible conduct of research. The candidate will be mentored by a team of experts with complementary strengths in surgical and device outcomes research, natural language processing and machine learning, and vascular disease and surgery. The proposed career development and research activities will develop the candidate's skillset and expertise and lead to an R01 level application. The candidate's long-term goal is to become an independent researcher focusing on the development and application of advanced multidisciplinary methods in the evaluation of surgical and device outcomes in the vascular disease area, supporting clinical, patient, and regulatory decision-making.