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: 10676773
• 负责人: Jialin Mao
• 金额: $ 16.25万
• 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/10676773
• 关键词: Address; Adverse event; Affect; Algorithms; Amputation; Area; Award; Balloon Angioplasty; Blood Vessels; Characteristics; Clinical; Data; 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; Leadership; Legal patent; 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; Severities; Severity of illness; Specificity; Stents; Symptoms; System; Text; Training; United States National Institutes of Health; Update; Vascular Diseases; Vascularization; Work; Writing; career development; cohort; efficacy evaluation; electronic health data; electronic structure; epidemiology study; follow-up; improved; innovation; interdisciplinary approach; language processing; 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的长期结果

项目成果

Longer patient travel distance is associated with increased non-index readmission after complex aortic surgery.

较长的患者移动距离与复杂主动脉手术后非指数再入院的增加有关。

• DOI: 10.1016/j.jvs.2023.02.005
• 发表时间: 2023
• 期刊: Journal of vascular surgery
• 影响因子: 4.3
• 作者: Feldman,ZachM;Zheng,Xinyan;Mao,Jialin;Sumpio,BrandonJ;Mohebali,Jahan;Chang,DavidC;Goodney,PhilipP;Conrad,MarkF;Srivastava,SunitaD
• 通讯作者: Srivastava,SunitaD

Greater Patient Travel Distance is Associated with Perioperative and One-Year Cost Increases After Complex Aortic Surgery.

患者移动距离较长与复杂主动脉手术后围手术期和一年费用的增加有关。

• DOI: 10.1016/j.avsg.2023.05.040
• 发表时间: 2023
• 期刊: Annals of vascular surgery
• 影响因子: 1.5
• 作者: Feldman,ZachM;Zheng,Xinyan;Mao,Jialin;Sumpio,BrandonJ;Mohebali,Jahan;Chang,DavidC;Goodney,PhilipP;Srivastava,SunitaD;Conrad,MarkF
• 通讯作者: Conrad,MarkF