The Clinical Utility of Kinetic Glomerular Filtration Rate in the Assessment of Renal Function and Prediction of Outcomes Among Critically Ill Patients With Acute Kidney Injury: A Single-Center Retrospective Cohort Study

Shari Ann Atanacio, Maria Rachel Uy

Jul 2021 DOI 10.35460/2546-1621.2018-0034     Access   

Abstract

Objective: To determine the discriminatory ability of kinetic glomerular filtration rate (kGFR) to detect acute kidney injury (AKI) when compared with established GFR equations and criteria and relating it to mortality, renal replacement therapy initiation and renal recovery.

Methods: This was a retrospective analysis using data from chart review of 109 intensive care unit (ICU) patients at the University of Santo Tomas Hospital (USTH). The renal function estimates using Chronic Kidney Disease Epidemiology Collaboration (CKD-Epi), modification of diet in renal disease (MDRD), Kidney Disease Improving Global Outcomes Acute Kidney Injury (KDIGO AKI), as well as kinetic GFR equations were compared and correlated with renal and cardiovascular outcomes.

Results: The renal function assessed by kGFR, CKD-Epi, MDRD and KDIGO staging based on serum creatinine (SCr) showed no significant association with mortality outcomes. However, AKI diagnosed based on urine output (UO), and combined SCr and urine output (KDIGO) showed association with all-cause mortality. The UO detected severe stages of AKI while SCr (based on KDIGO) better identified the earlier stages of AKI. The criteria for KDIGO AKI when combined also shows mortality prediction since it joins together the effects of SCr and UO. There was a remarkable 3.5 times increase  in hemodialysis initiation (p=0.0001) and 12.89 times increase in peritoneal dialysis initiation (p=0.01) for every stage increase in the KDIGO classification. kGFR, CKD-Epi and MDRD have 5%, 6%, and 6% decrease, respectively in the odds of initiating hemodialysis. There was however, no association for peritoneal dialysis.

Conclusion: kGFR was the least able in detecting AKI and KDIGO AKI criteria remains to be the standard in identifying AKI in the critical care setting. Increase in SCr was a sensitive tool in diagnosing AKI due to its ability to detect AKI based on a small increase in SCr regardless of the baseline renal function. Decreasing UO, however, is the prognosticating variable in KDIGO AKI criteria, in that it portends higher probability of initiation of renal replacement therapy (RRT) and ultimately higher mortality when present.

  1. de Oliveira Marques F, Oliveira SA, de Lima e Souza PF, Nojoza WG, da Silva Sena M, Ferreira TM, et al. Kinetic estimated glomerular filtration rate in critically ill patients: beyond the acute kidney injury severity classification system. Crit Care [Internet]. 2017 Nov 18;21(1). Available from: http://dx.doi.org/10.1186/s13054-017-1873-0

  2. Chertow GM, Burdick E, Honour M, Bonventre JV, Bates DW. Acute Kidney Injury, Mortality, Length of Stay, and Costs in Hospitalized Patients. JASN [Internet]. 2005 Sep 21;16(11):3365–70. Available from: http://dx.doi.org/10.1681/ASN.2004090740

  3. Kellum JA, Lameire N, Aspelin P, Barsoum RS, Burdmann EA, Goldstein SL, et al. Kidney disease: Improving global outcomes (KDIGO) acute kidney injury work group. KDIGO clinical practice guideline for acute kidney injury. Kidney International Supplements. 2012 Mar;2(1):1–138. Available from: https://doi.org/10.1038/kisup.2012.1

  4. Chen S. Retooling the Creatinine Clearance Equation to Estimate Kinetic GFR when the Plasma Creatinine Is Changing Acutely. JASN [Internet]. 2013 May 23;24(6):877–88. Available from: http://dx.doi.org/10.1681/ASN.2012070653

  5. Pianta TJ, Endre ZH, Pickering JW, Buckley NA, Peake PW. Kinetic Estimation of GFR Improves Prediction of Dialysis and Recovery after Kidney Transplantation. Remuzzi G, editor. PLoS ONE [Internet]. 2015 May 4;10(5):e0125669. Available from: http://dx.doi.org/10.1371/journal.pone.0125669

  6. O’Sullivan ED, Doyle A. The clinical utility of kinetic glomerular filtration rate. Clin Kidney J [Internet]. 2016 Dec 30;sfw108. Available from: http://dx.doi.org/10.1093/ckj/sfw108

  7. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985 Oct;13(10):818-29.

  8. Bouchard J, Macedo E, Soroko S, Chertow GM, Himmelfarb J, Ikizler TA, et al. Comparison of methods for estimating glomerular filtration rate in critically ill patients with acute kidney injury. Nephrology Dialysis Transplantation [Internet]. 2009 Aug 13;25(1):102–7. Available from: http://dx.doi.org/10.1093/ndt/gfp392

  9. Shemin D, Bostom AG, Laliberty P, Dworkin LD. Residual renal function and mortality risk in hemodialysis patients. American Journal of Kidney Diseases [Internet]. 2001 Jul;38(1):85–90. Available from: http://dx.doi.org/10.1053/ajkd.2001.25198

  10. Daniel WW, Cross C. 2013. Biostatistics: a foundation for analysis in the health sciences (10th ed.). U.S.A.: Wiley.

Articles related to the one you are viewing

There are currently no results to show, please try again later

CC BY: Open Access Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/