Automated ascertainment of bleeding and target lesion revascularization after percutaneous coronary intervention (PCI) using electronic health record (EHR) data

使用电子健康记录 (EHR) 数据自动确定经皮冠状动脉介入治疗 (PCI) 后的出血和目标病变血运重建

• 批准号: 10555326
• 负责人: Karthik Murugiah
• 金额: $ 16.61万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-01-25 至 2026-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10555326
• 关键词: Adoption; Algorithms; Award; Back; Blood Transfusion; Cardiac; Cardiac Catheterization Procedures; Cardiovascular system; Career Choice; Clinical; Clinical Data; Clinical Informatics; Clinical Trials; Collaborations; Computational Linguistics; Custom; Data; Data Set; Data Sources; Derivation procedure; Detection; Electronic Health Record; Event; Feedback; Generations; Goals; Health; Health Sciences; Health system; Healthcare Systems; Hemorrhage; Hospitalization; Hospitals; Hybrids; Image; Individual; Institution; Knowledge; Laboratories; Lesion; Manuals; Measurement; Medical Informatics; Mentors; Mentorship; Methods; Monitor; Morbidity - disease rate; Myocardial Ischemia; Natural Language Processing; Natural Language Processing pipeline; Observational Study; Outcomes Research; Patients; Performance; Positioning Attribute; Pragmatic clinical trial; Procedures; Process; Provider; Reporting; Research Personnel; Resources; Risk; Safety; Schedule; Signal Transduction; Site; Source; Standardization; Stents; Structure; Surveys; Techniques; Testing; Text; Time; Training; Vascularization; Work; adjudication; automated algorithm; clinical outcome measures; comparative effectiveness study; comparative effectiveness trial; data integration; data lake; data registry; deep learning; disease registry; electronic health record system; experience; health information technology; improved; information model; mortality; multidimensional data; open source; percutaneous coronary intervention; portability; predictive modeling; prospective; quality assurance; restenosis; risk prediction model; skills; stent thrombosis; structured data; supervised learning; support tools; tool; training opportunity; unstructured data

PROJECT SUMMARY Percutaneous coronary intervention (PCI) is the most common cardiac procedure with over 650,000 PCI performed annually in the U.S. Post-PCI complications which occur in a significant proportion of patients are associated with an increased risk of morbidity and mortality. Reliable ascertainment of post-PCI events is important for performance measurement, submission to disease registries, clinical trials, and for cardiac catheterization laboratory (CCL) safety monitoring. Claims based detection of PCI complications is inadequate. Assessing post-PCI events reliably requires an in-depth manual chart review, which incurs a significant provider and administrative burden. However, with advances in health information technology and nationwide adoption of electronic health record (EHR) systems, it possible to utilize EHR for the automatic derivation of clinical events. Dr. Murugiah proposes to create and validate automated algorithms which can be applied to EHR data to detect two important post-PCI events which are a common focus of clinical trials and quality improvement efforts – in-hospital bleeding and 1-year target lesion revascularization (TLR). Using EHR data at a large health system, Dr. Murugiah will develop a hybrid algorithm to detect major bleeding post-PCI by leveraging structured data fields such as laboratory values, as well as unstructured data such as imaging reports, cardiac catheterization reports, and progress notes incorporating Natural Language Processing (NLP) techniques (Aim 1). Similarly, using cardiac catheterization reports for patients undergoing repeat revascularization within 1 year, an algorithm will be developed to detect TLR (Aim 2). Both algorithms will be externally validated using EHR data from another large institution. The final algorithm will be implemented into a tool generating scheduled reports of bleeding and TLR, to be fed back to the quality assurance team for the CCL and to individual operators. Individual operators will be surveyed to obtain feedback about the algorithm, reporting process, and their perceived benefit. The final tools will be made open source (Aim 3). An automated algorithm for the detection of post-PCI events within EHR can reduce administrative burden, enable the generation of new knowledge from EHR based observational studies, and enable pragmatic clinical trials. Further, this project can serve as a proof of concept of the utility of hybrid tools leveraging both structured data and clinical text for surveillance and quality measurement. Dr. Murugiah has a career interest in studying and improving the treatment for ischemic heart disease using multidimensional datasets and EHR data to develop real time risk prediction models and decision support tools, and conduct EHR based comparative effectiveness studies and clinical trials. During the award period he will leverage the experience of his mentorship team which includes national experts in cardiovascular outcomes research, clinical informatics, and computational linguistics. He will also acquire formal training in clinical informatics by completing a Master of Health Science degree which will provide him the necessary platform to make the transition into an independent investigator.

项目摘要 经皮冠状动脉干预（PCI）是最常见的心脏手术，超过650,000 PCI 每年在美国PCI后每年进行的并发症发生在很大一部分的患者中 与发病率和死亡率的风险增加有关。可靠的PCI后事件的确定是 对于绩效测量，提交疾病注册，临床试验和心脏 导管实验室（CCL）安全监控。基于索赔的PCI并发症的检测不足。 评估PCI后事件可靠需要深入的手动图表审查，这会引起重大 提供者和行政伯恩。但是，随着健康信息技术和全国的进步 采用电子健康记录（EHR）系统，可以利用EHR自动推导 临床事件。 Murugiah博士的提议创建和验证自动化算法，可以应用于 EHR数据检测两个重要的PCI事件，这是临床试验和质量的普遍重点 改进工作 - 院内出血和1年目标病变血运重建（TLR）。使用EHR数据 Murugiah博士是一个大型卫生系统 利用结构化数据字段，例如实验室值以及非结构化数据，例如成像 报告，心脏导管报告和编码自然语言处理（NLP）的进度注释 技术（目标1）。同样，使用心脏导管插入报告进行重复的患者 血运重建在1年内，将开发出一种算法来检测TLR（AIM 2）。两种算法将是 使用来自另一家大型机构的EHR数据进行外部验证。最终算法将实施 一种生成预定报告的工具出血和TLR的报告，可以回馈给质量保证团队 CCL和个人操作员。将对个人操作员进行调查，以获取有关算法的反馈， 报告过程及其感知的收益。最终工具将成为开源（AIM 3）。自动化 用于检测EHR中PCI后事件的算法可以减少管理烧伤，使得能够 从基于EHR的观察性研究中产生新知识，并实现实用临床试验。 此外，该项目可以作为利用两个结构化数据的混合工具实用性的概念证明 以及用于监视和质量测量的临床文本。 Murugiah博士对学习和 使用多维数据集和EHR数据改善缺血性心脏病的治疗 实时风险预测模型和决策支持工具，并基于EHR的比较有效性 研究和临床试验。在奖励期间，他将利用他的Mentalship团队的经验 其中包括心血管结果研究，临床信息和计算的国家专家 语言学。他还将通过完成健康科学硕士学位来获得临床信息的正式培训 该学位将为他提供过渡到独立调查员的必要平台。