National Surveillance of Acute Kidney Injury Following Cardiac Catheterization

描述(由申请人提供)： 心导管术是导致急性肾损伤(AKI)的重要医疗诊断或治疗暴露风险。这种风险差异很大，取决于患者手术前的医疗状况，以及手术期间、手术前或手术后立即暴露的情况。根据所研究的队列，术后AKI的发生率为1%至31%，并与30%的一年死亡率相关。对于进一步下游的结局，AKI增加了进展为慢性肾脏疾病的风险，这可能导致透析、增加心血管不良结局、生活质量下降和显著 个人和医疗保健费用。心导管置入术是一个高风险、密切观察和可干预的临床护理窗口，预防AKI的发生将对退伍军人的健康和退伍军人管理局的医疗费用产生重大影响，据估计，每个患者约为7,500美元。然而，自动化结果监测并没有得到广泛的执行，退伍军人管理局目前也没有信息学工具来对每年接受这一程序的40,000名退伍军人进行这种监测。该项目的总体目标是开发基础设施和工具，以便在心导管术后进行全国VA近乎实时的自动不良事件监测，并在术后AKI的使用案例中展示这些工具的实用性。更具体地说，我们将1)开发和验证使用交互式学习技术的近实时自然语言处理(NLP)工具，以便提取与AKI相关但收集在结构化数据中的信息，2)开发和验证用于心导管术后AKI的稳健的Logistic回归预测模型家族，用于识别高危患者和人群，以及3)进行 使用新的监测方法对接受心导管置入术的退伍军人进行机构护理变化的自动国家回顾和前瞻性分析。这项提案将分析从2009年到2015年来自VA心血管评估、报告和跟踪(CART-CL)自愿临床登记和电子健康记录系统(CPRS)的回顾和预期队列数据。所有在此期间在退伍军人医院接受心导管置入术的成年患者将被包括在内。所有变量都将是 从CART-CL和CPRS的结构化数据元素中提取，使用接近实时的NLP从非结构化数据中提取风险因素。风险因素将通过全面的文献回顾、专家共识和在回顾信号评估过程中的发现来识别，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AKI的每个阶段开发Logistic回归模型，内部用Bootstrapping验证，外部用新英格兰北部心血管疾病研究小组经皮冠状动脉介入治疗登记。机构监督分析将使用最大序贯概率比检验和贝叶斯分层Logistic回归进行。最强的机构异常值将对经历结果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。将建立一个由CART和VA介入心脏病学负责人组成的治理委员会，以监督检测到的信号，以进行识别并向个别机构反馈。这项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AKI相关的新的风险因素，为在TE程序之前识别高危患者提供强大的风险分层工具，并允许检测与AKI风险增加相关的机构异常值和临床护理过程变化，这些风险可能适合于质量改进干预。最后，信息学基础设施和NLP开发有潜力应用于AKI以外的各种暴露和结果，用于心导管检查。

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.