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

利用关联的登记和电子健康记录来检查外周血管介入治疗后患者的长期结果”

• 批准号: 10283367
• 负责人: Jialin Mao
• 金额: $ 16.23万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10283367
• 关键词: Address; Adverse event; Affect; Algorithms; Amputation; Area; Award; Balloon Angioplasty; Blood Vessels; Characteristics; Clinical; Data; Data Analyses; Data Collection; Data Linkages; Data Science; Data Set; Data Sources; Decision Making; Detection; Development; Devices; Electronic Health Record; Evaluation; Event; Fostering; Generations; Goals; Grant; Health; Heterogeneity; Hospitals; Informatics; Institution; Intervention; Knowledge; Label; Lead; Leadership; Length; Lesion; Limb structure; Link; Longitudinal Studies; Machine Learning; Measures; Medical Device; Mentors; Methods; Mission; Natural Language Processing; Natural Language Processing pipeline; New York City; Observational Study; Operative Surgical Procedures; Outcome; Outcome Assessment; Outcomes Research; Patient-Focused Outcomes; Patients; Performance; Peripheral; Peripheral arterial disease; Predictive Value; Procedures; Quality of life; Radiology Specialty; Randomized; Randomized Controlled Trials; Registries; Reporting; Research; Research Activity; Research Personnel; Research Support; Sampling; Sensitivity and Specificity; Severities; Severity of illness; Stents; Symptoms; System; Text; Training; United States National Institutes of Health; Update; Vascular Diseases; Work; Writing; career development; cohort; electronic structure; epidemiology study; follow-up; health data; improved; innovation; interdisciplinary approach; machine learning method; multidisciplinary; multiple data sources; novel; patient registry; patient subsets; responsible research conduct; skills; tool; treatment effect; unstructured data

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.

利用链接的注册表和电子健康记录来检查患者术后的长期结果 周围血管介入治疗 项目概要/摘要 外周动脉疾病 (PAD) 影响着全球超过 2 亿人。周围血管介入治疗 (PVI) 是用于管理 PAD 的最常见程序。现有随机对照 PVI 后患者结果的试验 (RCT) 和观察性研究的随访时间都有限，因为 长期数据收集困难。此外，支架置入治疗效果（HTE）的异质性 与单独的经皮腔内血管成形术 (PTA) 相比，目前尚未得到很好的了解 效果调节剂评估方法。真实世界数据 (RWD)，特别是与电子设备相关的注册表 健康数据 (EHR) 对于研究血管手术后的长期结果非常有用。然而，方法 处理多个数据源和分析非结构化文本数据的能力仍在不断发展。拟议的 研究旨在解决 PVI 手术后患者长期预后方面目前的证据差距。这将 通过创新地应用和完善数据链接、自然语言处理 (NLP) 和效果来促进 修饰符评估方法。具体来说，该项目将把登记处和电子病历数据联系起来：1）检查长期 支架置入后与单独 PTA 相比的主要不良肢体事件以及评估治疗的异质性 患者特征的影响； 2) 使用机器学习方法开发 NLP 管道进行分析 非结构化文本数据并检查支架置入后与单独 PTA 相比的长期疗效终点； 3） 建立部署 NLP 工具以获得长期 PVI 结果的可行性和更新要求 对其他机构的评估。为了支持研究活动和向独立的过渡， 候选人将在奖励期间进行以下职业发展活动：1）获得in- 深入理解NLP和机器学习方法； 2）提炼数据科学专业知识以整合 EHR 进入医疗器械流行病学研究； 3）加强当前和新型血管的知识 疾病治疗； 4）发展和提高资助写作和学术领导的技能； 5）培训 负责任地进行研究。候选人将得到一组具有互补性的专家的指导 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 血管疾病和手术。拟议的职业发展和研究活动将发展 候选人的技能和专业知识，并导致 R01 级别的申请。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 评估血管疾病领域手术和设备结果的方法，支持临床、 耐心和监管决策。