Institute of Nephrology, University Hospital of Wales, Heath Park, Cardiff, CF14 4XN, UK.
Medical Biochemistry Department, University Hospital of Wales, Heath Park, Cardiff, CF14 4XN, UK; Medical Biochemistry Department, School of Medicine, Alexandria University, Egypt.
Am J Emerg Med. 2021 Feb;40:64-69. doi: 10.1016/j.ajem.2020.12.017. Epub 2020 Dec 13.
Quality management of Acute Kidney Injury (AKI) is dependent on early detection, which is currently deemed to be suboptimal. The aim of this study was to identify combinations of variables associated with AKI and to derive a prediction tool for detecting patients attending the emergency department (ED) or hospital with AKI (ED-AKI).
DESIGN, SETTING, PARTICIPANTS AND MEASUREMENTS: This retrospective observational study was conducted in the ED of a tertiary university hospital in Wales. Between April and August 2016 20,421 adult patients attended the ED of a University Hospital in Wales and had a serum creatinine measurement. Using an electronic AKI reporting system, 548 incident adult ED-AKI patients were identified and compared to a randomly selected cohort of adult non-AKI ED patients (n = 571). A prediction model for AKI was derived and subsequently internally validated using bootstrapping. The primary outcome measure was the number of patients with ED-AKI.
In 1119 subjects, 27 variables were evaluated. Four ED-AKI models were generated with C-statistics ranging from 0.800 to 0.765. The simplest and most practical multivariate model (model 3) included eight variables that could all be assessed at ED arrival. A 31-point score was derived where 0 is minimal risk of ED-AKI. The model discrimination was adequate (C-statistic 0.793) and calibration was good (Hosmer & Lomeshow test 27.4). ED-AKI could be ruled out with a score of <2.5 (sensitivity 95%). Internal validation using bootstrapping yielded an optimal Youden index of 0.49 with sensitivity of 80% and specificity of 68%.
A risk-stratification model for ED-AKI has been derived and internally validated. The discrimination of this model is objective and adequate. It requires refinement and external validation in more generalisable settings.
急性肾损伤（AKI）的质量管理依赖于早期检测，但目前这方面做得并不理想。本研究旨在确定与 AKI 相关的变量组合，并为检测因 AKI 而就诊于急诊科（ED）或医院的患者开发一种预测工具（ED-AKI）。
设计、地点、参与者和测量：这是一项在威尔士一所三级大学医院 ED 进行的回顾性观察性研究。2016 年 4 月至 8 月，20421 名成年患者在威尔士一所大学医院的 ED 就诊，并进行了血清肌酐测量。使用电子 AKI 报告系统，确定了 548 例新发成年 ED-AKI 患者，并与随机选择的成年非 AKI ED 患者队列（n=571）进行比较。使用 bootstrap 方法推导 AKI 预测模型，并对其进行内部验证。主要结局指标为 ED-AKI 患者人数。
在 1119 例患者中，评估了 27 个变量。生成了 4 个 ED-AKI 模型，C 统计量范围为 0.800 至 0.765。最简单实用的多变量模型（模型 3）包含 8 个在 ED 就诊时均可评估的变量。得出了一个 31 分的评分系统，其中 0 分代表 ED-AKI 风险最低。该模型的区分度良好（C 统计量 0.793），校准度良好（Hosmer & Lemeshow 检验 27.4）。评分<2.5 可排除 ED-AKI（敏感性 95%）。使用 bootstrap 进行内部验证得出，最佳 Youden 指数为 0.49，敏感性为 80%，特异性为 68%。
已经开发并内部验证了 ED-AKI 的风险分层模型。该模型的区分度客观且充分。它需要在更具普遍性的环境中进行细化和外部验证。
