Models for predicting risk of acute kidney injury after liver surgery

Research output: Contribution to journalArticlepeer-review


Colleges, School and Institutes

External organisations

  • Centre for Liver Research and National Institute for Health Research (NIHR) Birmingham Biomedical Research Centre, Institute of Immunology & Immunotherapy, University of Birmingham, UK


Background & Aims: Acute kidney injury (AKI) is a serious complication of liver surgery and associated with significant morbidity and mortality. The incidence of AKI following hepatic surgery can be as high as 94%, with highest rates seen following orthotopic liver transplantation, particularly when extended criteria grafts are used. Strategies to identify patients at risk of AKI may enable early interventions to prevent or minimise AKI. Methods: A systematic review was conducted using PubMed, Medline, Cochrane and Google Scholar databases for literature reporting models predicting risk of AKI following liver surgery. All were scrutinised for model variables, performance of the models, and validation strategies in order to identify key factors associated with increased risk. Results: From an initial pool of 1432 results, seven articles were identified which reported risk prediction models for AKI. These included articles using either an equation-based model or point-based system for risk prediction and two studies were clinically validated. Whilst predictive variables varied from study to study, factors relating to liver function (MELD), cardiovascular integrity and extent of surgical blood loss were important for determining risk. Conclusions: This study has identified key discriminating variables that show promise in predicting risk of AKI in patients undergoing hepatic surgery. However it is important to note that a robust risk prediction model derived from a large prospective cohort study, recruiting patients from multiple centres who experience specific types of hepatobiliary intervention is currently lacking. Thus further studies are required to develop a robust model that can be applicable across multiple patient populations with different underlying aetiologies.


Original languageEnglish
Article number007
JournalOBM Hepatology and Gastroenterology
Issue number2
Publication statusPublished - 16 May 2018


  • hepatic, renal, acute injury, prediction, model