A comparison of different diagnostic criteria of acute kidney injury in critically ill patients

Xuying Luo, Li Jiang, Bin Du, Ying Wen, Meiping Wang, Xiuming Xi, Beijing Acute Kidney Injury Trial (BAKIT) workgroup, Xuying Luo, Li Jiang, Bin Du, Ying Wen, Meiping Wang, Xiuming Xi, Beijing Acute Kidney Injury Trial (BAKIT) workgroup

Abstract

Introduction: Recently, the Kidney Disease: Improving Global Outcomes (KDIGO) proposed a new definition and classification of acute kidney injury (AKI) on the basis of the RIFLE (Risk, Injury, Failure, Loss of kidney function, and End-stage renal failure) and AKIN (Acute Kidney Injury Network) criteria, but comparisons of the three criteria in critically ill patients are rare.

Methods: We prospectively analyzed a clinical database of 3,107 adult patients who were consecutively admitted to one of 30 intensive care units of 28 tertiary hospitals in Beijing from 1 March to 31 August 2012. AKI was defined by the RIFLE, AKIN, and KDIGO criteria. Receiver operating curves were used to compare the predictive ability for mortality, and logistic regression analysis was used for the calculation of odds ratios and 95% confidence intervals.

Results: The rates of incidence of AKI using the RIFLE, AKIN, and KDIGO criteria were 46.9%, 38.4%, and 51%, respectively. KDIGO identified more patients than did RIFLE (51% versus 46.9%, P = 0.001) and AKIN (51% versus 38.4%, P <0.001). Compared with patients without AKI, in-hospital mortality was significantly higher for those diagnosed as AKI by using the RIFLE (27.8% versus 7%, P <0.001), AKIN (32.2% versus 7.1%, P <0.001), and KDIGO (27.4% versus 5.6%, P <0.001) criteria, respectively. There was no difference in AKI-related mortality between RIFLE and KDIGO (27.8% versus 27.4%, P = 0.815), but there was significant difference between AKIN and KDIGO (32.2% versus 27.4%, P = 0.006). The areas under the receiver operator characteristic curve for in-hospital mortality were 0.738 (P <0.001) for RIFLE, 0.746 (P <0.001) for AKIN, and 0.757 (P <0.001) for KDIGO. KDIGO was more predictive than RIFLE for in-hospital mortality (P <0.001), but there was no difference between KDIGO and AKIN (P = 0.12).

Conclusions: A higher incidence of AKI was diagnosed according to KDIGO criteria. Patients diagnosed as AKI had a significantly higher in-hospital mortality than non-AKI patients, no matter which criteria were used. Compared with the RIFLE criteria, KDIGO was more predictive for in-hospital mortality, but there was no significant difference between AKIN and KDIGO.

Figures

Figure 1
Figure 1
Area under the curves for RIFLE, AKIN, and KDIGO classification schemes comparing the predictive ability of RIFLE, AKIN, and KDIGO classification schemes for in-hospital mortality. AKIN, Acute Kidney Injury Network; KDIGO, Kidney Disease: Improving Global Outcomes; RIFLE, Risk, Injury, Failure, Loss of Kidney Function, and End-stage Kidney Disease; ROC, receiver operating characteristic. RIFLE: Area Under the Curve 0.738 (95% CI 0.713-0.762, P

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