National Surveillance of Acute Kidney Injury Following Cardiac Catheterization

心导管插入术后急性肾损伤的全国监测

• 批准号: 8597962
• 负责人: MICHAEL E. MATHENY
• 金额: --
• 依托单位: VETERANS HEALTH ADMINISTRATION
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2015-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8597962
• 关键词: Acute Renal Failure with Renal Papillary Necrosis; Address; Adult; Adverse event; Area; Back; Calibration; Cardiac Catheterization Procedures; Cardiology; Cardiovascular Diseases; Cardiovascular system; Caring; Catheterization; Chronic; Chronic Kidney Failure; Clinical; Cohort Studies; Consensus; Coupled; Data; Data Collection; Data Element; Detection; Development; Diabetes Mellitus; Diagnostic; Dialysis procedure; Discrimination; Electronic Health Record; Evaluation; Event; Family; Feedback; Health; Health Care Costs; Individual; Informatics; Institution; Intervention; Interview; Kidney; Kidney Diseases; Laboratories; Lasso; Lead; Learning; Logistic Regressions; Machine Learning; Manuals; Medical; Medical center; Modeling; Natural Language Processing; New England; Outcome; Patient Care; Patients; Performance; Pharmaceutical Preparations; Probability; Procedures; Process; Program Reviews; Quality of life; Recommendation; Recovery; Registries; Relative (related person); Reporting; Research Infrastructure; Review Literature; Risk; Risk Factors; Risk Management; Self Care; Severities; Signal Transduction; Staging; Stratification; Structure; Surveillance Methods; Survival Rate; System; Techniques; Testing; Text; Therapeutic; Time; United States Department of Veterans Affairs; Validation; Variant; Veterans; Work; adverse outcome; base; clinical care; clinical practice; cohort; cost; data registry; experience; high risk; improved; mortality; national surveillance; novel; patient population; percutaneous coronary intervention; prevent; programs; prospective; tool

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.

描述（由申请人提供）： 心脏导管插入术是急性肾脏损伤（AKI）发展的重要医学诊断或治疗风险。该风险因患者的术前医疗状况以及手术后或之后的暴露而异。程序后AKI发生在1％至31％的患者中，具体取决于所研究的队列，并与30％的一年死亡率有关。为了进一步的结果，AKI增加了慢性肾脏疾病的风险，这可能导致透析，心血管不良后果增加，生活质量的降低和显着 个人和医疗保健费用。心脏导管插入术是高风险，仔细观察，并且是间歇性的临床护理窗口，防止AKI的发生将对退伍军人的健康和VA护理费用产生重大影响，估计每位患者约为7,500美元。但是，自动结果监视并未得到广泛执行，VA目前没有信息学工具来对每年40,000名退伍军人进行此程序进行此监视。 该项目的总体目的是开发基础设施和工具，以在心脏导管插入后实时自动化不良事件监视，以在心脏导管后实时自动化事件监视，并在后术后AKI的用例中证明工具的实用性。更具体地说，我们将使用交互式学习技术开发和验证近实时的自然语言处理（NLP）工具，以提取与AKI相关但收集到AKI但被收集到结构化数据中的信息，2）开发和验证一个可靠的Logistic回归预测模型，用于AKI的AKI，在心脏exation插管后，用于在高风险患者和3）的标识中使用AKI，以及3）。 使用新型监视方法接受心脏导管插入的退伍军人之间的机构护理变异的自动回顾性和前瞻性分析。 该建议将分析来自VA心血管评估，报告和跟踪导管实验室（CART-CL）自愿临床注册表和2009年至2015年的自愿性临床注册表和电子健康记录系统（CPRS）的回顾性和前瞻性队列数据。所有在此期间接受VA中接受VA的成年患者的自愿临床注册表和电子健康记录系统（CPRS）。所有变量将是 从CART-CL和CPR的结构化数据元素中提取，NLP几乎实时从非结构化数据中提取风险因素。风险因素将通过回顾性信号评估期间的全面文献综述，专家共识和发现来确定风险因素，并通过使用Lasso回归变量选择技术选择。将为每个急性肾脏损伤网络AKI阶段开发逻辑回归模型，并在内部通过自举验证，并通过新英格兰北部心血管疾病研究组经皮冠状动脉干预注册表进行外部验证。将使用最大化的顺序概率比测试和贝叶斯分层逻辑回归进行机构监视分析。最强的机构异常值将对经历结果的患者病例进行手动案例审查，以确定关键的临床护理变化。将建立一个由购物车和VA介入心脏病学领导者组成的治理委员会，以监督检测到的信号，以识别和反馈给各个机构。 该提案将改善退伍军人的照顾。这项工作有可能发现与AKI相关的新风险因素，为在TE手术之前识别高风险患者的稳健风险分层工具，并允许检测机构异常值和临床护理过程变化，这与AKI风险增加有关，该风险可能会增加，这些风险可能是可通过质量改进的干预措施加剧的。最后，信息学基础设施和NLP的开发有可能在AKI以外的各种暴露和结果中应用，以进行心脏导管插入术。

项目成果

Validated contemporary risk model of acute kidney injury in patients undergoing percutaneous coronary interventions: insights from the National Cardiovascular Data Registry Cath-PCI Registry.

• DOI: 10.1161/jaha.114.001380
• 发表时间: 2014-12
• 期刊: Journal of the American Heart Association
• 影响因子: 5.4
• 作者: Tsai TT;Patel UD;Chang TI;Kennedy KF;Masoudi FA;Matheny ME;Kosiborod M;Amin AP;Weintraub WS;Curtis JP;Messenger JC;Rumsfeld JS;Spertus JA
• 通讯作者: Spertus JA

Acute Kidney Injury Risk Prediction in Patients Undergoing Coronary Angiography in a National Veterans Health Administration Cohort With External Validation.

• DOI: 10.1161/jaha.115.002136
• 发表时间: 2015-12-11
• 期刊: Journal of the American Heart Association
• 影响因子: 5.4
• 作者: Brown JR;MacKenzie TA;Maddox TM;Fly J;Tsai TT;Plomondon ME;Nielson CD;Siew ED;Resnic FS;Baker CR;Rumsfeld JS;Matheny ME
• 通讯作者: Matheny ME