Kinetic estimated glomerular filtration rate in critically ill patients: beyond the acute kidney injury severity classification system

BackgroundAlthough significant advances have been achieved in acute kidney injury (AKI) research following its classification, potential pitfalls can be identified in clinical practice. The nonsteady-state (kinetic) estimated glomerular filtration rate (KeGFR) could add clinical and prognostic information in critically ill patients beyond the current AKI classification system.MethodsThis was a retrospective analysis using data from the Multiparameter Intelligent Monitoring in Intensive Care II project. The KeGFR was calculated during the first 7 days of intensive care unit (ICU) stay in 13,284 patients and was correlated with outcomes.ResultsIn general, there was not a good agreement between AKI severity and the worst achieved KeGFR. The stepwise reduction in the worst achieved KeGFR conferred an incremental risk of death, rising from 7.0% (KeGFR > 70 ml/min/1.73 m2) to 27.8% (KeGFR < 30 ml/min/1.73 m2). This stepwise increment in mortality remained in each AKI severity stage. For example, patients with AKI stage 3 who maintained KeGFR had a mortality rate of 16.5%, close to those patients with KeGFR < 30 ml/min/1.73 m2 but no AKI; otherwise, mortality increased to 40% when both AKI stage 3 and KeGFR < 30 ml/min/1.73 m2 were present. In relation to another outcome—renal replacement therapy (RRT)—patients with the worst achieved KeGFR < 30 ml/min/1.73 m2 and KDIGO stage 1/2 had a rate of RRT of less than 10%. However, this rate was 44% when both AKI stage 3 and a worst KeGFR < 30 ml/min/1.73 m2 were observed. This interaction between AKI and KeGFR was also present when looking at long-term survival.ConclusionBoth the AKI classification system and KeGFR are complementary to each other. Assessing both AKI stage and KeGFR can help to identify patients at different risk levels in clinical practice.

[1]  C. Sprung,et al.  Use of the SOFA score to assess the incidence of organ dysfunction/failure in intensive care units: results of a multicenter, prospective study. Working group on "sepsis-related problems" of the European Society of Intensive Care Medicine. , 1998, Critical care medicine.

[2]  Sheldon Chen Retooling the creatinine clearance equation to estimate kinetic GFR when the plasma creatinine is changing acutely. , 2013, Journal of the American Society of Nephrology : JASN.

[3]  R. Bellomo,et al.  Acute renal failure – definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group , 2004, Critical care.

[4]  C. Schmid,et al.  A new equation to estimate glomerular filtration rate. , 2009, Annals of internal medicine.

[5]  L. Forni,et al.  Prevention of acute kidney injury and protection of renal function in the intensive care unit: update 2017 , 2017, Intensive Care Medicine.

[6]  E. Macedo,et al.  Kidney Disease Improving Global Outcomes or creatinine kinetics criteria in acute kidney injury: a proof of concept study. , 2013, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[7]  J. Kellum,et al.  AKI in the ICU: definition, epidemiology, risk stratification, and outcomes. , 2012, Kidney international.

[8]  R. Bellomo,et al.  A comparison of the RIFLE and AKIN criteria for acute kidney injury in critically ill patients. , 2008, Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association.

[9]  J. Kellum,et al.  Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1) , 2013, Critical Care.

[10]  S. Waikar,et al.  Creatinine kinetics and the definition of acute kidney injury. , 2009, Journal of the American Society of Nephrology : JASN.

[11]  G. Clermont,et al.  Epidemiology of severe sepsis in the United States: Analysis of incidence, outcome, and associated costs of care , 2001, Critical care medicine.

[12]  G. Clermont,et al.  Classifying AKI by Urine Output versus Serum Creatinine Level. , 2015, Journal of the American Society of Nephrology : JASN.

[13]  Sudhir V. Shah,et al.  Improving outcomes from acute kidney injury: report of an initiative , 2007, Pediatric Nephrology.

[14]  David C. Christiani,et al.  Acute kidney injury subphenotypes based on creatinine trajectory identifies patients at increased risk of death , 2016, Critical Care.

[15]  S. Lemeshow,et al.  A new Simplified Acute Physiology Score (SAPS II) based on a European/North American multicenter study. , 1993, JAMA.

[16]  C. Combe,et al.  Kinetic eGFR and Novel AKI Biomarkers to Predict Renal Recovery. , 2015, Clinical journal of the American Society of Nephrology : CJASN.

[17]  T. H. Kyaw,et al.  Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database* , 2011, Critical care medicine.

[18]  A. Go,et al.  Commonly used surrogates for baseline renal function affect the classification and prognosis of acute kidney injury. , 2010, Kidney international.

[19]  Gilles Clermont,et al.  RIFLE criteria for acute kidney injury are associated with hospital mortality in critically ill patients: a cohort analysis , 2006, Critical care.