A new model to predict acute kidney injury requiring renal replacement therapy after cardiac surgery

Background: Acute 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. Methods: We 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. Results: We 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). Interpretation: We 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.