The AKI-Predict-Score: a new prediction model for acute kidney injury after liver transplantation

Abstract

Background: Acute kidney injury (AKI) is frequently observed after liver transplantation and associated with impaired long-term outcomes. Although multiple risk factors have been identified, the cumulative impact on development of AKI remains unknown. Our aim was therefore to design a new model to predict the frequency of severe AKI after liver transplantation. Methods: A risk analysis was performed in all patients undergoing primary liver transplantation in two centers (2007–2015; n = 1230). AKI was defined following KDIGO criteria. A new risk score to predict severe AKI (stage 2 & 3) was calculated based on weight of the factors in a multivariable regression analysis according to the Framingham-scheme. Results: Overall, 34% of the recipients developed severe AKI, including 18% requiring renal replacement therapy. Five factors were identified as strongest predictors for severe AKI: donor BMI, DCD graft, recipient BMI, fresh frozen plasma transfusion, and graft implantation time, leading to 0–25 score points with an AUC of 0.7 in the new AKI-Predict-Score (Fig. 1). Three risk classes were identified: low-risk (0–10 points), intermediate-risk (11–20 points) and high-risk with >20 points. In addition, a score of >20 points correlated with impaired long-term graft survival and more postoperative complications assessed with the Comprehensive Complication Index. Conclusion: The AKI-Predict-Score is a new and reliable instrument to identify recipients at risk for severe post-transplant AKI. This score is readily available at end of the transplant procedure. This model offers therefore a great potential to decide which renal protective strategies might be implemented directly after liver transplant.