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

Abstract

Background: Acute kidney injury (AKI) is a frequent complication after liver transplantation. Although numerous risk factors for AKI have been identified, their cumulative impact remains unclear. Our aim was therefore to design a new model to predict post-transplant AKI.

Methods: Risk analysis was performed in patients undergoing liver transplantation in two centres (n = 1230). A model to predict severe AKI was calculated, based on weight of donor and recipient risk factors in a multivariable regression analysis according to the Framingham risk-scheme.

Results: Overall, 34% developed severe AKI, including 18% requiring postoperative renal replacement therapy (RRT). Five factors were identified as strongest predictors: donor and recipient BMI, DCD grafts, FFP requirements, and recipient warm ischemia time, leading to a range of 0-25 score points with an AUC of 0.70. Three risk classes were identified: low, intermediate and high-risk. Severe AKI was less frequently observed if recipients with an intermediate or high-risk were treated with a renal-sparing immunosuppression regimen (29 vs. 45%; p = 0.007).

Conclusion: The AKI Prediction Score is a new instrument to identify recipients at risk for severe post-transplant AKI. This score is readily available at end of the transplant procedure, as a tool to timely decide on the use of kidney-sparing immunosuppression and early RRT.