Advertisement

Derivation of a prediction model for emergency department acute kidney injury

Published:December 12, 2020DOI:https://doi.org/10.1016/j.ajem.2020.12.017

      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

      To read this article in full you will need to make a payment
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to The American Journal of Emergency Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Zeng X.
        • McMahon G.M.
        • Brunelli S.M.
        • Bates D.W.
        • Waikar S.S.
        Incidence, outcomes, and comparisons across definitions of AKI in hospitalized individuals.
        Clin J Am Soc Nephrol. 2014; 9: 12-20https://doi.org/10.2215/CJN.02730313
        • Thomas M.E.
        • Blaine C.
        • Dawnay A.
        • Devonald M.A.
        • Ftouh S.
        • Laing C.
        • et al.
        The definition of acute kidney injury and its use in practice.
        Kidney Int. 2015; 87: 62-73https://doi.org/10.1038/ki.2014.328
        • Lafrance J.P.
        • Miller D.R.
        Acute kidney injury associates with increased long-term mortality.
        J Am Soc Nephrol. 2010; 21 (ASN.2009060636 [pii]): 345-352
        • Lameire N.
        • Van Biesen W.
        • Vanholder R.
        Acute renal failure.
        Lancet. 2005; 365 (S0140-6736(05)17831-3 [pii]): 417-430
        • Foxwell D.A.
        • Pradhan S.
        • Zouwail S.
        • Rainer T.H.
        • Phillips A.O.
        Epidemiology of emergency department acute kidney injury.
        Nephrology (Carlton). 2020; 25: 457-466https://doi.org/10.1111/nep.13672
      1. National Confidential Enquiry into Patient Outcome and Death [NCEPOD] Report Acute Kidney Injury: Adding Insult to Injury. 2009. available at www.ncepod.org.uk

        • National Institute for Health and Care Excellence. Acute kidney injury: prevention, detection and management, London
        (Available)
        at
        Date: 2019
        • Holmes J.
        • Rainer T.
        • Geen J.
        • Roberts G.
        • May K.
        • Wilson N.
        • et al.
        Acute kidney injury in the era of the AKI E-alert.
        Clin J Am Soc Nephrol. 2016; 11: 2123-2131https://doi.org/10.2215/CJN.05170516
        • Holmes J.
        • Roberts G.
        • Geen J.
        • Dodd A.
        • Selby N.M.
        • Lewington A.
        • et al.
        Utility of electronic AKI alerts in intensive care: a national multicentre cohort study.
        J Crit Care. 2018; 44: 185-190https://doi.org/10.1016/j.jcrc.2017.10.024
      2. Phillips D, Young O, Holmes J, Allen LA, Roberts G, Geen J, et al. Seasonal pattern of incidence and outcome of Acute Kidney Injury: A national study of Welsh AKI electronic alerts. Int J Clin Pract. 2017;71(9)doi:https://doi.org/10.1111/ijcp.13000.

        • Wilson F.P.
        • Shashaty M.
        • Testani J.
        • Aqeel I.
        • Borovskiy Y.
        • Ellenberg S.S.
        • et al.
        Automated, electronic alerts for acute kidney injury: a single-blind, parallel-group, randomised controlled trial.
        Lancet. 2015; 385 (S0140-6736(15)60266-5 [pii]): 1966-1974
        • Levey A.S.
        • Stevens L.A.
        • Schmid C.H.
        • Zhang Y.L.
        • Castro 3rd, A.F.
        • Feldman H.I.
        • et al.
        A new equation to estimate glomerular filtration rate.
        Ann Intern Med. 2009; 150: 604-612
        • Wonnacott A.
        • Meran S.
        • Amphlett B.
        • Talabani B.
        • Phillips A.
        Epidemiology and outcomes in community-acquired versus hospital-acquired AKI.
        Clin J Am Soc Nephrol. 2014; 9 (CJN.07920713 [pii]): 1007-1014
        • Holmes J.
        • Geen J.
        • Phillips B.
        • Williams J.D.
        • Phillips A.O.
        • Welsh A.K.I.S.G.
        Community acquired acute kidney injury: findings from a large population cohort.
        QJM. 2017; 110: 741-746https://doi.org/10.1093/qjmed/hcx151