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.

项目概要 经皮冠状动脉介入治疗 (PCI) 是最常见的心脏手术，有超过 650,000 例 PCI 在美国每年都会进行一次 PCI 术后并发症，其中很大一部分患者发生的并发症是 与发病率和死亡率风险增加有关。 PCI 后事件的可靠确定是 对于性能测量、提交疾病登记、临床试验以及心脏病很重要 导管实验室（CCL）安全监测。基于索赔的 PCI 并发症检测是不够的。 可靠地评估 PCI 后事件需要进行深入的手动图表审查，这需要大量的时间 提供者和行政负担。然而，随着卫生信息技术的进步和全国范围内的 采用电子健康记录（EHR）系统，可以利用 EHR 自动导出 临床事件。 Murugiah 博士建议创建并验证可应用于 用于检测 PCI 术后两个重要事件的 EHR 数据，这是临床试验和质量的共同焦点 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 作为一个大型卫生系统，Murugiah 博士将开发一种混合算法，通过以下方式检测 PCI 后大出血： 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） 包含自然语言处理 (NLP) 的报告、心导管检查报告和进度说明 技术（目标 1）。同样，使用心导管检查报告来了解接受重复治疗的患者 一年内进行血运重建，将开发一种算法来检测 TLR（目标 2）。两种算法都将是 使用来自另一家大型机构的 EHR 数据进行外部验证。最终算法将被实现为 生成出血和 TLR 计划报告的工具，反馈给质量保证团队 CCL 和个体经营者。将对个别运营商进行调查，以获得有关算法的反馈， 报告过程及其感知的好处。最终工具将开源（目标 3）。一个自动化的 用于检测 EHR 内 PCI 后事件的算法可以减轻管理负担，使 从基于 EHR 的观察性研究中产生新知识，并实现实用的临床试验。 此外，该项目可以作为利用结构化数据的混合工具实用性的概念证明 以及用于监测和质量测量的临床文本。 Murugiah 博士的职业兴趣是研究和 使用多维数据集和 EHR 数据改进缺血性心脏病的治疗 实时风险预测模型和决策支持工具，并进行基于EHR的有效性比较 研究和临床试验。在颁奖期间，他将利用其导师团队的经验 其中包括心血管结果研究、临床信息学和计算方面的国家专家 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学的正式培训 学位将为他提供必要的平台，使其成为一名独立调查员。