Advertisement

Computerized algorithms compared with a nephrologist's diagnosis of acute kidney injury in the emergency department

Published:December 11, 2018DOI:https://doi.org/10.1016/j.ejim.2018.11.013

      Highlights

      • Patients frequently present to the emergency department with elevated SCr.
      • Computer algorithms can identify AKI with high sensitivity and specificity.
      • AKI algorithm performance varies according to time period allowed for baseline SCr.
      • Using a longer time period for assessment of baseline SCr yields better results.
      • The best algorithms are useful for diagnosis of AKI in the emergency department.

      Abstract

      Background

      The aim of this study was to examine acute kidney injury (AKI) diagnosis based on different computerized algorithms compared with a nephrologist's diagnosis in patients visiting an emergency department (ED) of a university hospital.

      Methods

      In this retrospective study, we used electronic medical records at the University Hospital in Reykjavik to identify all patients aged ≥18 years, who presented to the ED in the year 2010 with an elevated serum creatinine (SCr) level. All SCr values were reviewed and a nephrologist determined whether AKI was present using the KDIGO SCr criteria and clinical data. Computerized algorithms based on the KDIGO SCr criteria, accounting for various time intervals for baseline SCr and changes in follow-up SCr, were constructed using the statiscal software R.

      Results

      At 53,816 ED visits, SCr was measured in 15,588 patients for a total of 21,559 measurements. Elevated SCr was observed in 2878 (18.4%) patients. Strict adherence to the KDIGO SCr criteria yielded a 79% sensitivity, 94% specificity, 68% positive predictive value (PPV) and 96% negative predictive value (NPV) for the diagnosis of AKI. Allowing for a longer time frame (>365 days) for baseline SCr, resulted in 93% sensitivity, 96% specificity, 80% PPV and 99% NPV. The algorithms which included a decrease in SCr from the index ED value yielded a sensitivity of 97% but lower specificity, 74% and 80%.

      Conclusions

      The algorithms that perform best yield excellent sensitivity and specificity and could be used to identify patients with AKI in the ED to enhance early diagnosis and treatment.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      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 European Journal of Internal Medicine
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

        • Kidney Disease: Improving Global Outcomes (KDIGO) AKI Work Group
        KDIGO clinical practice guideline for acute kidney injury.
        Kidney Int Suppl. 2012; 2: 1-138
        • Ali T.
        • Khan I.
        • Simpson W.
        • Prescott G.
        • Townend J.
        • Smith W.
        • et al.
        Incidence and outcomes in acute kidney injury: a comprehensive population-based study.
        J Am Soc Nephrol. 2007; 18: 1292-1298
        • Bagshaw S.M.
        • George C.
        • Dinu I.
        • Bellomo R.
        A multi-centre evaluation of the RIFLE criteria for early acute kidney injury in critically ill patients.
        Nephrol Dial Transplant. 2008; 23: 1203-1210
        • Lopes J.A.
        • Fernandes P.
        • Jorge S.
        • Goncalves S.
        • Alvarez A.
        • Costa E Silva Z.
        • et al.
        Acute kidney injury in intensive care unit patients: a comparison between the RIFLE and the Acute Kidney Injury Network classifications.
        Crit Care. 2008; 12: R110
        • Ostermann M.
        • Chang R.W.S.
        Acute kidney injury in the intensive care unit according to RIFLE.
        Crit Care Med. 2007; 35: 1837-1843
        • Ricci Z.
        • Cruz D.
        • Ronco C.
        The RIFLE criteria and mortality in acute kidney injury: a systematic review.
        Kidney Int. 2008; 73: 538-546
        • Thakar C.V.
        • Christianson A.
        • Freyberg R.
        • Almenoff P.
        • Render M.L.
        Incidence and outcomes of acute kidney injury in intensive care units: a Veterans Administration study.
        Crit Care Med. 2009; 37: 2552-2558
        • Uchino S.
        • Bellomo R.
        • Goldsmith D.
        • Bates S.
        • Ronco C.
        An assessment of the RIFLE criteria for acute renal failure in hospitalized patients.
        Crit Care Med. 2006; 34: 1913-1917
        • James M.
        • Pannu N.
        Methodological considerations for observational studies of acute kidney injury using existing data sources.
        J Nephrol. 2009; 22: 295-305
        • Siew E.D.
        • Matheny M.E.
        • Ikizler T.A.
        • Lewis J.B.
        • Miller R.A.
        • Waitman L.R.
        • et al.
        Commonly used surrogates for baseline renal function affect the classification and prognosis of acute kidney injury.
        Kidney Int. 2010; 77: 536-542
        • Siew E.D.
        • Ikizler T.A.
        • Matheny M.E.
        • Shi Y.P.
        • Schildcrout J.S.
        • Danciu I.
        • et al.
        Estimating baseline kidney function in hospitalized patients with impaired kidney function.
        Clin J Am Soc Nephrol. 2012; 7: 712-719
        • Lafrance J.P.
        • Miller D.R.
        Defining acute kidney injury in database studies: the effects of varying the baseline kidney function assessment period and considering CKD status.
        Am J Kidney Dis. 2010; 56: 651-660
        • Selby N.M.
        Electronic alerts for acute kidney injury.
        Curr Opin Nephrol Hypertens. 2013; 22: 637-642
        • Selby N.M.
        • Hill R.
        • Fluck R.J.
        • NHS England 'Think Kidneys' AKI Programme
        Standardizing the early identification of AKI: The NHS England National Patient Safety Alert.
        Nephron. 2015; 131: 113-117
        • Sawhney S.
        • Fluck N.
        • Marks A.
        • Prescott G.
        • Simpson W.
        • Tomlinson L.
        • et al.
        Acute kidney injury-how does automated detection perform?.
        Nephrol Dial Transplant. 2015; 30: 1853-1861
        • Grams M.E.
        • Waikar S.S.
        • MacMahon B.
        • Whelton S.
        • Ballew S.H.
        • Coresh J.
        Performance and limitations of administrative data in the identification of AKI.
        Clin J Am Soc Nephrol. 2014; 9: 682-689
        • Zavada J.
        • Hoste E.
        • Cartin-Ceba R.
        • Calzavacca P.
        • Gajic O.
        • Clermont G.
        • et al.
        A comparison of three methods to estimate baseline creatinine for RIFLE classification.
        Nephrol Dial Transplant. 2010; 25: 3911-3918
        • Beddhu S.
        • Samore M.H.
        • Roberts M.S.
        • Stoddard G.J.
        • Pappas L.M.
        • Cheung A.K.
        Creatinine production, nutrition, and glomerular filtration rate estimation.
        J Am Soc Nephrol. 2003; 14: 1000-1005
        • Froissart M.
        • Rossert J.
        • Jacquot C.
        • Paillard M.
        • Houillier P.
        Predictive performance of the Modification of Diet in Renal Disease and Cockcroft-Gault equations for estimating renal function.
        J Am Soc Nephrol. 2005; 16: 763-773
        • Poggio E.D.
        • Wang X.
        • Greene T.
        • Van Lente F.
        • Hall P.M.
        Performance of the Modification of Diet in Renal Disease and Cockcroft-Gault equations in the estimation of GFR in health and in chronic kidney disease.
        J Am Soc Nephrol. 2005; 16: 459-466
        • Porter C.J.
        • Juurlink I.
        • Bisset L.H.
        • Bavakunji R.
        • Mehta R.L.
        • Devonald M.A.J.
        A real-time electronic alert to improve detection of acute kidney injury in a large teaching hospital.
        Nephrol Dial Transplant. 2014; 29: 1888-1893
        • Selby N.M.
        • Crowley L.
        • Fluck R.J.
        • McIntyre C.W.
        • Monaghan J.
        • Lawson N.
        • et al.
        Use of electronic results reporting to diagnose and monitor AKI in hospitalized patients.
        Clin J Am Soc Nephrol. 2012; 7: 533-540
        • 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: 1966-1974