• Neesh Pannu, Michelle Graham, Scott Klarenbach, Steven Meyer, Teresa Kieser, Brenda Hemmelgarn, Feng Ye, Matthew James, and APPROACH Investigators and the Alberta Kidney Disease Network.
• Department of Medicine (Pannu, Graham, Klarenbach, Ye), Division of Critical Care Medicine (Pannu), Division of Cardiac Surgery, Department of Surgery (Meyer), University of Alberta, Edmonton, Alta.; Division of Cardiac Surgery, Department of Surgery (Kieser), Department of Medicine (Hemmelgarn, James), Department of Community Health Sciences (Hemmelgarn, James), University of Calgary, Calgary, Alta.; Institute of Health Economics (Klarenbach), Edmonton, Alta. npannu@ualberta.ca.
• CMAJ. 2016 Oct 18; 188 (15): 1076-1083.

BackgroundAcute kidney injury after cardiac surgery is associated with adverse in-hospital and long-term outcomes. Novel risk factors for acute kidney injury have been identified, but it is unknown whether their incorporation into risk models substantially improves prediction of postoperative acute kidney injury requiring renal replacement therapy.MethodsWe developed and validated a risk prediction model for acute kidney injury requiring renal replacement therapy within 14 days after cardiac surgery. We used demographic, and preoperative clinical and laboratory data from 2 independent cohorts of adults who underwent cardiac surgery (excluding transplantation) between Jan. 1, 2004, and Mar. 31, 2009. We developed the risk prediction model using multivariable logistic regression and compared it with existing models based on the C statistic, Hosmer-Lemeshow goodness-of-fit test and Net Reclassification Improvement index.ResultsWe identified 8 independent predictors of acute kidney injury requiring renal replacement therapy in the derivation model (adjusted odds ratio, 95% confidence interval [CI]): congestive heart failure (3.03, 2.00-4.58), Canadian Cardiovascular Society angina class III or higher (1.66, 1.15-2.40), diabetes mellitus (1.61, 1.12-2.31), baseline estimated glomerular filtration rate (0.96, 0.95-0.97), increasing hemoglobin concentration (0.85, 0.77-0.93), proteinuria (1.65, 1.07-2.54), coronary artery bypass graft (CABG) plus valve surgery (v. CABG only, 1.25, 0.64-2.43), other cardiac procedure (v. CABG only, 3.11, 2.12-4.58) and emergent status for surgery booking (4.63, 2.61-8.21). The 8-variable risk prediction model had excellent performance characteristics in the validation cohort (C statistic 0.83, 95% CI 0.79-0.86). The net reclassification improvement with the prediction model was 13.9% (p < 0.001) compared with the best existing risk prediction model (Cleveland Clinic Score).InterpretationWe have developed and validated a practical and accurate risk prediction model for acute kidney injury requiring renal replacement therapy after cardiac surgery based on routinely available preoperative clinical and laboratory data. The prediction model can be easily applied at the bedside and provides a simple and interpretable estimation of risk.© 2016 Canadian Medical Association or its licensors.

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