National Surveillance of Acute Kidney Injury Following Cardiac Catheterization

描述（由申请人提供）： 心导管插入术是发生急性肾损伤（阿基）的重要医疗诊断或治疗暴露风险。这种风险因患者术前的医疗状况以及手术期间或手术前或手术后的暴露情况而异。根据研究的队列，术后阿基发生在1%至31%的患者中，并且与30%的1年死亡率相关。对于更下游的结果，阿基增加了进展为慢性肾脏疾病的风险，这可能导致透析，心血管不良结局增加，生活质量降低，以及显著的肾功能损害。 个人和医疗保健费用。心导管插入术是一个高风险、密切观察且可干预的临床护理窗口，预防阿基的发生将对退伍军人的健康和退伍军人管理局的护理成本产生重大影响，据估计每位患者的护理成本约为7，500美元。然而，自动化结果监测并没有广泛进行，VA目前没有信息学工具来对每年接受该程序的40，000名退伍军人进行这种监测。 本项目的总体目标是开发基础设施和工具，以在心导管插入术后进行国家VA近实时自动不良事件监测，并证明这些工具在术后阿基用例中的实用性。更具体地说，我们将1）使用交互式学习技术开发和验证近实时自然语言处理（NLP）工具，以提取与阿基相关但收集在结构化数据中的信息，2）开发和验证心脏导管插入术后阿基的一系列强大的逻辑回归预测模型，用于识别高风险患者和人群，以及3）进行 使用新的监测方法对接受心导管插入术的退伍军人进行机构护理变化的自动化国家回顾性和前瞻性分析。 本提案将分析2009年至2015年期间来自VA心血管评估、报告和导管插入实验室跟踪（CART-CL）自愿临床登记研究和电子健康记录系统（CPRS）的回顾性和前瞻性队列数据。将纳入在此期间在VA接受心导管插入术的所有成人患者。所有变量将 从CART-CL和CPRS的结构化数据元素中提取风险因素，使用近真实的时间NLP从非结构化数据中提取风险因素。将通过全面文献综述、专家共识和回顾性信号评价期间的发现来识别风险因素，并通过使用Lasso回归变量选择技术进行选择。将为急性肾损伤网络阿基的每个阶段开发逻辑回归模型，通过自举法进行内部验证，并通过北方新英格兰心血管疾病研究组经皮冠状动脉介入治疗登记研究进行外部验证。将使用最大序贯概率比检验和贝叶斯分层逻辑回归进行机构监测分析。最强的机构离群值将对经历结局的患者病例进行手动病例审查，以确定关键的临床护理变化。将建立由CART和VA介入心脏病学负责人组成的管理委员会，以监督检测到的信号，以便识别并反馈给各个机构。 该提案将在多个领域改善退伍军人的护理。这项工作有可能发现与阿基相关的新风险因素，为在手术前识别高风险患者提供强大的风险分层工具，并允许检测与阿基风险增加相关的机构离群值和临床护理过程变化，这些风险可能适合质量改进干预。最后，信息学基础设施和NLP开发有可能应用于阿基以外的各种暴露和结局，用于心导管监测。

DESCRIPTION (provided by applicant): Cardiac catheterization represents a significant medical diagnostic or treatment exposure risk for the development of acute kidney injury (AKI). That risk varies widely depending on the patient's pre-procedural medical conditions as well as exposures during or immediately before or after the procedure. Post procedural AKI occurs in 1% to 31% of the patients, depending on the cohort studied, and is associated with a 30% one- year mortality rate. For outcomes further downstream, AKI increases the risk of progressing to chronic kidney disease, which can lead to dialysis, increased cardiovascular adverse outcomes, reductions in quality of life, and significant personal and health care costs. Cardiac catheterization is a high risk, closely observed, and intervenable clinical care window, and preventing an occurrence of AKI would have significant impact on veteran's health and VA costs of care, which has been estimated to be approximately $7,500 per patient. However, automated outcomes surveillance is not widely performed, and the VA does not currently have the informatics tools to conduct this surveillance for the 40,000 veterans a year undergoing the procedure. The overall objective of this project is to develop the infrastructure and tools to perform national VA near real-time automated adverse event surveillance after cardiac catheterization, and to demonstrate the utility of the tools within the use case of post-procedural AKI. More specifically, we will 1) develop and validate near real-time natural language processing (NLP) tools using interactive learning techniques in order to extract information that is relevant to AKI but is collected in structured data, 2) develop and validate a robust family of logistic regression prediction models for AKI following cardiac catheterization for use in the identification of high risk patients and populations, and 3) conduct automated national retrospective and prospective analyses of institutional care variation among veterans receiving cardiac catheterization using novel surveillance methods. This proposal will analyze retrospective and prospective cohort data from the VA Cardiovascular Assessment, Reporting, and Tracking for Catheterization Laboratories (CART-CL) voluntary clinical registry and electronic health record system (CPRS) from 2009 to 2015. All adult patients who received a cardiac catheterization in the VA during this time period will be included. All variables will be extracted from the structured data elements of CART-CL and CPRS, with near real time NLP used to extract risk factors from unstructured data. Risk factors will be identified by comprehensive literature review, expert consensus, and discovery during evaluation of retrospective signals, and selected through the use of the Lasso regression variable selection technique. Logistic regression models will be developed for each of the Acute Kidney Injury Network AKI stages, internally validated with bootstrapping, and externally validated with the Northern New England Cardiovascular Disease Study Group percutaneous coronary intervention registry. Institutional surveillance analyses will be conducted using maximized sequential probability ratio testing and Bayesian hierarchical logistic regression. The strongest institutional outliers will have manual case review of patient cases that experienced the outcome in order to ascertain key clinical care variations. A governance board consisting of CART and VA interventional cardiology leaders will be established to supervise detected signals for identification and feedback to individual institutions. This proposal will improve veterans' care n a number of areas. This work has the potential to discover new risk factors associated with AKI, to provide robust risk stratification tools for the identification of high risk patients prior to te procedure, and allow the detection of institutional outliers and clinical care process variation tht is associated with increased AKI risk that may be amenable to quality improvement interventions. Finally, the informatics infrastructure and NLP development has the potential to be applied in a wide variety of exposures and outcomes beyond AKI for cardiac catheterization surveillance.