Highlights
- •Despite a call for the development of pragmatic risk assessment tools for AKI at the hospital front currently there is no accepted tool
- •We describe a simple, practical risk score for the predicting the probability of ED-AKI at triage
- •The score is based on age, gender, chronic kidney disease and other comorbidities.
Abstract
Background and objective
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.
Results
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%.
Conclusion
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.
Keywords
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Article Info
Publication History
Published online: December 12, 2020
Accepted:
December 9,
2020
Received in revised form:
December 8,
2020
Received:
October 20,
2020
Identification
Copyright
© 2020 Elsevier Inc. All rights reserved.
