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电子病历研究中的急性肾损伤研究:入门指南。

eResearch in acute kidney injury: a primer for electronic health record research.

机构信息

Division of Nephrology, Department of Pediatrics, Children's Hospital of Pittsburgh of UPMC, Pittsburgh, PA, USA.

Center for Critical Care Nephrology, UPMC and University of Pittsburgh, Pittsburgh, PA, USA.

出版信息

Nephrol Dial Transplant. 2019 Mar 1;34(3):401-407. doi: 10.1093/ndt/gfy052.

Abstract

Acute kidney injury (AKI) has a significant impact on patient morbidity and mortality as well as overall health care costs. eResearch, which integrates information technology and information management to optimize research strategies, provides a perfect platform for necessary ongoing AKI research. With the recent adoption of a widely accepted definition of AKI and near-universal use of electronic health records, eResearch is becoming an important tool in AKI research. Conducting eResearch in AKI should ideally be based on a relatively uniform methodology. This article is the first of its kind to describe a methodology for pursuing eResearch specific to AKI and includes an illustrative database example for critically ill patients. We discuss strategies for using serum creatinine and urine output in large databases to identify and stage AKI and ways to interpolate missing values and validate data. Issues specific to the pediatric population include variation in serum creatinine with growth, varied severity of illness scoring systems and medication dosage based on weight. Many of these same strategies used to optimize AKI eResearch can be applicable to real-time AKI alerts with potential integration of additional clinical variables.

摘要

急性肾损伤 (AKI) 对患者的发病率和死亡率以及整体医疗保健成本都有重大影响。eResearch 将信息技术和信息管理相结合,以优化研究策略,为必要的持续 AKI 研究提供了一个完美的平台。随着 AKI 定义的广泛采用和电子病历的近乎普遍使用,eResearch 正在成为 AKI 研究的重要工具。进行 AKI 的 eResearch 理想情况下应基于相对统一的方法。本文是此类文章中的第一篇,专门描述了一种针对 AKI 的 eResearch 方法,并包含了一个危重症患者的示例数据库。我们讨论了使用大型数据库中的血清肌酐和尿量来识别和分期 AKI 的策略,以及如何插补缺失值和验证数据。儿科人群特有的问题包括血清肌酐随生长而变化、疾病严重程度评分系统的差异以及基于体重的药物剂量。许多用于优化 AKI eResearch 的相同策略也可应用于实时 AKI 警报,并可潜在整合其他临床变量。

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