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PURPOSE: Using the Risk, Injury, Failure, Loss of kidney function, End-stage kidney disease (RIFLE), Acute Kidney Injury Network (AKIN) and Kidney Disease: Improving Global Outcomes (KDIGO) systems, the incidence of acute kidney injury (AKI) and their ability to predict in-hospital mortality in severe sepsis or septic shock was compared. MATERIALS AND METHODS: We performed a retrospective analysis of 457 critically ill patients with severe sepsis or septic shock hospitalized between January 2008 and December 2014. Multivariate logistic regression was employed to evaluate the association between the RIFLE, AKIN and KDIGO systems with in-hospital mortality. Model fit was assessed by the goodness-of-fit test and discrimination by the area under the receiver operating characteristic (AUROC) curve. Statistical significance was defined as P < 0.05. RESULTS: RIFLE (84.2%) and KDIGO (87.5%) identified more patients with AKI than AKIN (72.8%) (P < 0.001). AKI defined by AKIN and KDIGO was associated with in-hospital mortality {AKIN: adjusted odds ratio [OR] 2.3[95% confidence interval (CI) 1.3-4], P = 0.006; KDIGO: adjusted OR 2.7[95% CI 1.2-6.2], P = 0.021} while AKI defined by RIFLE was not [adjusted OR 2.0 (95% CI 1-4), P = 0.063]. The AUROC curve for in-hospital mortality was similar between the three classifications (RIFLE 0.652, P < 0.001; AKIN 0.686, P < 0.001; KDIGO 0.658, P < 0.001). CONCLUSIONS: RIFLE and KDIGO diagnosed more patients with AKI than AKIN, but the prediction ability for in-hospital mortality was similar between the three systems.
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INTRODUCTION: Whether discernible advantages in terms of sensitivity and specificity exist with Acute Kidney Injury Network (AKIN) criteria versus Risk, Injury, Failure, Loss of Kidney Function, End-stage Kidney Disease (RIFLE) criteria is currently unknown. We evaluated the incidence of acute kidney injury and compared the ability of the maximum RIFLE and of the maximum AKIN within intensive care unit hospitalization in predicting inhospital mortality of critically ill patients. METHODS: Patients admitted to the Department of Intensive Medicine of our hospital between January 2003 and December 2006 were retrospectively evaluated. Chronic kidney disease patients undergoing dialysis or renal transplant patients were excluded from the analysis. RESULTS: In total, 662 patients (mean age, 58.6 +/- 19.2 years; 392 males) were evaluated. AKIN criteria allowed the identification of more patients as having acute kidney injury (50.4% versus 43.8%, P = 0.018) and classified more patients with Stage 1 (risk in RIFLE) (21.1% versus 14.7%, P = 0.003), but no differences were observed for Stage 2 (injury in RIFLE) (10.1% versus 11%, P = 0.655) and for Stage 3 (failure in RIFLE) (19.2% versus 18.1%, P = 0.672). Mortality was significantly higher for acute kidney injury defined by any of the RIFLE criteria (41.3% versus 11%, P < 0.0001; odds ratio = 2.78, 95% confidence interval = 1.74 to 4.45, P < 0.0001) or of the AKIN criteria (39.8% versus 8.5%, P < 0.0001; odds ratio = 3.59, 95% confidence interval = 2.14 to 6.01, P < 0.0001). The area under the receiver operator characteristic curve for inhospital mortality was 0.733 for RIFLE criteria (P < 0.0001) and was 0.750 for AKIN criteria (P < 0.0001). There were no statistical differences in mortality by the acute kidney injury definition/classification criteria (P = 0.72). CONCLUSIONS: Although AKIN criteria could improve the sensitivity of the acute kidney injury diagnosis, it does not seem to improve on the ability of the RIFLE criteria in predicting inhospital mortality of critically ill patients.
