Acute Kidney Injury following Liver Transplantation: A Systematic Review of Published Predictive Models

Author:

Caragata R.1,Wyssusek K. H.1,Kruger P.2

Affiliation:

1. Department of Anaesthesia, Princess Alexandra Hospital, Brisbane, Queensland, and University of Queensland, School of Medicine, Brisbane, Queensland

2. Department of Intensive Care Medicine, Princess Alexandra Hospital, Brisbane, Queensland, and University of Queensland, School of Medicine, Brisbane, Queensland

Abstract

Summary Acute kidney injury (AKI) is a frequent postoperative complication amongst liver transplant recipients and is associated with increased morbidity and mortality. This systematic review analysed the existing predictive models, in order to solidify current understanding. Articles were selected for inclusion if they described the primary development of a clinical prediction model (either an algorithm or risk score) to predict AKI post liver transplantation. The database search yielded a total of seven studies describing the primary development of a prediction model or risk score for the development of AKI following liver transplantation. The models span thirteen years of clinical research and highlight a gradual change in the definitions of AKI, emphasising the need to employ standardised definitions for subsequent studies. Collectively, the models identify a diverse range of predictive factors with several common trends. They emphasise the impact of preoperative renal dysfunction, liver disease severity and aetiology, metabolic risk factors as well as intraoperative variables including measures of haemodynamic instability and graft quality. Although several of the models address postoperative parameters, their utility in predictive modelling seems to be of questionable relevance. The common risk factors identified within this systematic review provide a minimum list of variables, which future studies should address. Research in this area would benefit from prospective, multisite studies with larger cohorts as well as the subsequent internal and external validation of predictive models. Ultimately, the ability to identify patients at high risk of post-transplant AKI may enable early intervention and perhaps prevention.

Publisher

SAGE Publications

Subject

Anesthesiology and Pain Medicine,Critical Care and Intensive Care Medicine

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