Aylward Ryan, Casula Anna, Tiffin Nicki, Ben-Shlomo Yoav, Rayner Brian, Birnie Kate, Caskey Fergus John
Bristol Medical School, Population Health Sciences, University of Bristol, First Floor, 5 Tyndall Avenue, Bristol, BS8 1UD, UK.
Division of Nephrology and Hypertension, Department of Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
J Nephrol. 2024 Nov;37(8):2317-2325. doi: 10.1007/s40620-024-02030-6. Epub 2024 Aug 4.
National Health Services (NHS) England mandates that an acute kidney injury (AKI) detection algorithm be embedded in laboratories. We evaluated the implementation of the algorithm and the consistency of alerts submitted to the United Kingdom Renal Registry (UKRR).
Code was developed to simulate the syntax of the AKI detection algorithm, executed on data from local laboratories submitted to the UKRR, including alerts and serum creatinine (SCr) results spanning 15 months before and after the alert submission. Acute kidney injury alerts were categorized into stages 0/1/2/3. Inter-rater agreement (Gwet's AC1) was used to compare local and centrally derived alerts at individual laboratory and commercial laboratory information management system (LIMS) levels, penalizing extreme disagreements.
The analysis included 9,096,667 SCr results from 29 labs (475,634 patients; median age 72 years, 47% female) between algorithm activation and data extraction (September 30, 2020). Laboratories and the central simulation generated 1,579,633 and 1,646,850 non-zero AKI alerts, respectively. Agreement was high within known laboratory information management system providers (0.97-0.98) but varied across individual laboratories (overall range 0.17-0.98, 0.17-0.23 in three). Agreement tended to be lower (Gwet's AC1 0.88) with the highest baseline SCr quartile (median 164 μmol/L).
Overall, alerts submitted to the UKRR are a valid source of AKI surveillance but there are concerns about inconsistent laboratory practices, incomplete adoption of the NHSE algorithm code, alert suppression, and variable interpretation of guidelines. Future efforts should audit and support laboratories with low agreement rates, and explore reasons for lower agreement in individuals with pre-existing CKD.
英国国家医疗服务体系(NHS)要求实验室采用急性肾损伤(AKI)检测算法。我们评估了该算法的实施情况以及提交给英国肾脏登记处(UKRR)的警报的一致性。
开发代码以模拟AKI检测算法的语法,对提交给UKRR的本地实验室数据执行该代码,包括警报以及警报提交前后15个月的血清肌酐(SCr)结果。急性肾损伤警报分为0/1/2/3期。使用评分者间一致性(Gwet's AC1)在个体实验室和商业实验室信息管理系统(LIMS)层面比较本地生成和中央生成的警报,对极端不一致情况进行惩罚。
分析纳入了算法激活至数据提取期间(2020年9月30日)29个实验室的9,096,667条SCr结果(475,634名患者;中位年龄72岁,47%为女性)。实验室和中央模拟分别生成了1,579,633条和1,646,850条非零AKI警报。在已知的实验室信息管理系统供应商中一致性较高(0.97 - 0.98),但在各个实验室之间存在差异(总体范围0.17 - 0.98,其中三个实验室为0.17 - 0.23)。与基线SCr四分位数最高组(中位数164μmol/L)的一致性往往较低(Gwet's AC1为0.88)。
总体而言,提交给UKRR的警报是AKI监测的有效来源,但存在对实验室操作不一致、未完全采用NHSE算法代码、警报抑制以及指南解读不一致的担忧。未来应审核并支持一致性率较低的实验室,并探究已有慢性肾脏病个体一致性较低的原因。