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: 10463785
• 负责人: Jialin Mao
• 金额: $ 16.19万
• 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/10463785
• 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; Persons; Predictive Value; Procedures; Quality of life; Radiology Specialty; 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长期的患者预后 周围血管干预 项目摘要/摘要 周围动脉疾病（PAD）影响了全球超过2亿人。周围血管干预措施 （PVI）是执行用于管理PAD的最常见过程。现有的随机控制 PVI后患者预后的试验（RCT）和观察性研究均有限 长期数据收集的困难。另外，用于支架放置的治疗效果的异质性（HTE） 与经皮易感性血管成形术（PTA）相对于电流尚未得到充分了解 效果修改器评估方法。现实世界数据（RWD），尤其是与电子相关的注册表 健康数据（EHR），可用于研究血管手术后的长期结局。但是，方法 要使用多个数据源并分析非结构化文本数据仍在发展。提议 研究旨在解决PVI程序后长期患者预后的当前证据差距。这会 通过创新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。具体而言，该项目将将注册表和EHR数据链接到1）检查长期 支架放置后的主要不良肢体事件与单独的PTA以及评估治疗的异质性 患者特征的效果； 2）使用机器学习方法开发NLP管道来分析 单独放置和PTA后，非结构化的文本数据并检查长期疗效终点，并且； 3） 建立用于部署NLP工具以进行长期PVI结果的可行性和更新要求 评估其他机构。为了支持研究活动和向独立的过渡， 候选人将在颁奖期内进行以下职业发展活动：1） 对NLP和机器学习方法的深入了解； 2）完善数据科学专业知识以整合 EHR进入医疗设备流行病学研究； 3）增强当前和新型血管的知识 疾病治疗； 4）发展和提高赠款写作和学术领导的技能； 5）培训 负责任的研究。候选人将由互补的专家团队进行指导 手术和设备结果的优势研究，自然语言处理和机器学习以及 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级别的应用。候选人的长期目标是 成为专注于高级多学科发展和应用的独立研究人员 在评估血管疾病区域外科和装置结果的方法，支持临床， 病人和监管决策。