• Ayodele Odutayo, Neill K J Adhikari, James Barton, Karen E A Burns, Jan O Friedrich, David Klein, Stephen Lapinsky, Sasha Litwin, Aleksander Meret, Rahim Moineddin, Bonnie Richardson, Robert Richardson, Alina Zaltzman, Michelle Hladunewich, and Ron Wald.
• Division of Nephrology, Sunnybrook Health Sciences Centre, Toronto, ON, Canada.
• Can J Anaesth. 2012 Oct 1;59(10):934-42.

PurposeWe undertook this study to characterize the epidemiology of acute kidney injury (AKI) in Canadian critical care units. We aimed to identify predictors of mortality for patients diagnosed with AKI.MethodsWe conducted a prospective cohort study of consecutive patients admitted to critical care units at five Canadian hospitals over a 30-day period. Each patient was followed until hospital discharge or for a maximum of 30 days. The serum creatinine criteria for the Acute Kidney Injury Network (AKIN-SCr) system were used to identify, classify, and characterize patients who developed AKI. We used multivariable logistic regression to predict 30-day mortality among patients with AKI.ResultsWe identified 603 patients, 161 (26.7%) of whom developed AKI. Compared to patients without AKI, those with AKI were more likely to die (29.2% vs 8.6%, P < 0.001). The risk of death increased with increasing AKIN-SCr stage (P < 0.001). In all, 19 patients (11.8% of those with AKI) commenced dialysis a median of one day (interquartile range, one to two days) after AKI diagnosis. At AKI diagnosis, the blood urea nitrogen (BUN) level (adjusted odds ratio [OR] 1.68, 95% confidence interval [CI] 1.01 to 2.79/10 mmol·L(-1)) and serum bicarbonate (adjusted OR 0.88, 95% CI 0.81 to 0.95/1 mmol·L(-1)) were associated with 30-day mortality and predicted death with an area under the receiver-operating characteristic curve of 0.79 (95% CI 0.71 to 0.86).ConclusionsAcute kidney injury is a common complication of critical illness in Canada. The development of even the mildest stage of AKI is associated with a substantially higher risk of death. At AKI diagnosis, routine clinical data may be helpful for predicting adverse outcomes.

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