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血清肌酐基线的定义不明确会影响急诊科急性肾损伤的诊断。

Ambiguous definitions for baseline serum creatinine affect acute kidney diagnosis at the emergency department.

机构信息

Central Diagnostic Laboratory, University Medical Center Utrecht, Utrecht University, Room Number G03.551, UMC Utrecht, Heidelberglaan 100, Utrecht, 3584 CX, The Netherlands.

SkylineDx, Lichtenauerlaan 40, Rotterdam, 3062 ME, The Netherlands.

出版信息

BMC Nephrol. 2021 Nov 8;22(1):371. doi: 10.1186/s12882-021-02581-x.

DOI:10.1186/s12882-021-02581-x
PMID:34749693
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8573871/
Abstract

BACKGROUND

Acute kidney injury (AKI) incidence is increasing, however AKI is often missed at the emergency department (ED). AKI diagnosis depends on changes in kidney function by comparing a serum creatinine (SCr) measurement to a baseline value. However, it remains unclear to what extent different baseline values may affect AKI diagnosis at ED.

METHODS

Routine care data from ED visits between 2012 and 2019 were extracted from the Utrecht Patient Oriented Database. We evaluated baseline definitions with criteria from the RIFLE, AKIN and KDIGO guidelines. We evaluated four baseline SCr definitions (lowest, most recent, mean, median), as well as five different time windows (up to 365 days prior to ED visit) to select a baseline and compared this to the first measured SCr at ED. As an outcome, we assessed AKI prevalence at ED.

RESULTS

We included 47,373 ED visits with both SCr-ED and SCr-BL available. Of these, 46,100 visits had a SCr-BL from the - 365/- 7 days time window. Apart from the lowest value, AKI prevalence remained similar for the other definitions when varying the time window. The lowest value with the - 365/- 7 time window resulted in the highest prevalence (21.4%). Importantly, applying the guidelines with all criteria resulted in major differences in prevalence ranging from 5.9 to 24.0%.

CONCLUSIONS

AKI prevalence varies with the use of different baseline definitions in ED patients. Clinicians, as well as researchers and developers of automatic diagnostic tools should take these considerations into account when aiming to diagnose AKI in clinical and research settings.

摘要

背景

急性肾损伤(AKI)的发病率正在上升,但在急诊科(ED)往往会漏诊 AKI。AKI 的诊断取决于通过将血清肌酐(SCr)测量值与基线值进行比较来评估肾功能的变化。然而,不同的基线值在多大程度上可能影响 ED 中 AKI 的诊断尚不清楚。

方法

从 2012 年至 2019 年的 ED 就诊的常规护理数据从乌得勒支患者导向数据库中提取。我们使用 RIFLE、AKIN 和 KDIGO 指南的标准评估了基线定义。我们评估了四个基线 SCr 定义(最低值、最近值、平均值、中位数),以及五个不同的时间窗口(ED 就诊前长达 365 天)来选择基线值,并将其与 ED 时首次测量的 SCr 进行比较。作为结果,我们评估了 ED 时 AKI 的患病率。

结果

我们纳入了 47373 例同时具有 SCr-ED 和 SCr-BL 的 ED 就诊。其中,46100 例就诊时具有来自-365/-7 天时间窗口的 SCr-BL。除了最低值外,当改变时间窗口时,其他定义的 AKI 患病率保持相似。最低值与-365/-7 时间窗口结合使用导致患病率最高(21.4%)。重要的是,应用所有标准的指南导致患病率存在显著差异,范围为 5.9%至 24.0%。

结论

ED 患者中使用不同的基线定义会导致 AKI 患病率发生变化。临床医生以及自动诊断工具的研究人员和开发人员在旨在临床和研究环境中诊断 AKI 时应考虑这些因素。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/8573871/85a043c45943/12882_2021_2581_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/8573871/882d44fe5239/12882_2021_2581_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/8573871/cb813117293b/12882_2021_2581_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/8573871/85a043c45943/12882_2021_2581_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/8573871/882d44fe5239/12882_2021_2581_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/8573871/cb813117293b/12882_2021_2581_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5c7c/8573871/85a043c45943/12882_2021_2581_Fig3_HTML.jpg

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2
Biomarker-Guided Risk Assessment for Acute Kidney Injury: Time for Clinical Implementation?基于生物标志物的急性肾损伤风险评估:是否到了临床实施的时机?
Ann Lab Med. 2021 Jan;41(1):1-15. doi: 10.3343/alm.2021.41.1.1. Epub 2020 Aug 25.
3
Diagnostics, Risk Factors, Treatment and Outcomes of Acute Kidney Injury in a New Paradigm.
Design, validation and implementation of an automated e-alert for acute kidney injury: 6-month pilot study shows increased awareness.
设计、验证和实施急性肾损伤电子警报的自动化:6 个月的试点研究表明提高了认识。
BMC Nephrol. 2023 Jul 27;24(1):222. doi: 10.1186/s12882-023-03265-4.
4
Gene signature for the prediction of the trajectories of sepsis-induced acute kidney injury.用于预测脓毒症相关性急性肾损伤轨迹的基因特征。
Crit Care. 2022 Dec 21;26(1):398. doi: 10.1186/s13054-022-04234-3.
5
Derivation and evaluation of baseline creatinine equations for hospitalized children and adolescents: the AKI baseline creatinine equation.住院儿童和青少年的基础肌酐方程的推导和评估:AKI 基础肌酐方程。
Pediatr Nephrol. 2022 Dec;37(12):3223-3233. doi: 10.1007/s00467-022-05571-9. Epub 2022 May 4.
新范式下急性肾损伤的诊断、危险因素、治疗及预后
J Clin Med. 2020 Apr 13;9(4):1104. doi: 10.3390/jcm9041104.
4
Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review.基于证据的临床决策支持系统用于危重症中三种疾病状态的预测与检测:一项系统文献综述
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5
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6
A clinically applicable approach to continuous prediction of future acute kidney injury.一种临床适用的急性肾损伤未来发生的连续预测方法。
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7
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