Marit Kalisvaart1, Andrea Schlegel2, Ilaria Umbro3, Jubi E de Haan4, Wojciech G Polak5, Jan N IJzermans5, Darius F Mirza2, M Thamara Pr Perera2, John R Isaac2, James Ferguson2, Anna P Mitterhofer6, Jeroen de Jonge5, Paolo Muiesan7. 1. The Liver Unit, Queen Elizabeth University Hospital, Birmingham, United Kingdom; Department of Surgery, Erasmus MC University Medical Center, Rotterdam, the Netherlands. 2. The Liver Unit, Queen Elizabeth University Hospital, Birmingham, United Kingdom. 3. Department of Anatomical, Histological, Forensic Medicine and Orthopedics Sciences, Sapienza University of Rome, Rome, Italy; Department of Clinical Medicine, Nephrology Unit, Sapienza University of Rome, Rome, Italy. 4. Department of Intensive Care, Erasmus MC University Medical Center, Rotterdam, Netherlands. 5. Department of Surgery, Erasmus MC University Medical Center, Rotterdam, the Netherlands. 6. Department of Clinical Medicine, Nephrology Unit, Sapienza University of Rome, Rome, Italy. 7. The Liver Unit, Queen Elizabeth University Hospital, Birmingham, United Kingdom. Electronic address: paolo.muiesan@uhb.nhs.uk.
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.
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.
Authors: Luis Cesar Bredt; Luis Alberto Batista Peres; Michel Risso; Leandro Cavalcanti de Albuquerque Leite Barros Journal: World J Hepatol Date: 2022-03-27
Authors: Lanting Yang; Nico Gabriel; Inmaculada Hernandez; Scott M Vouri; Stephen E Kimmel; Jiang Bian; Jingchuan Guo Journal: Front Pharmacol Date: 2022-03-11 Impact factor: 5.810