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Ex Vivo Normothermic Machine Perfusion of Human Kidneys: A Scoping Review
Verbal Presentation

Verbal Presentation

3:15 pm

30 April 2026

Meeting Room M6

FREE PAPERS

Talk Descripción

Institución: Austin Health - VIC, Australia

Purpose Ex vivo normothermic machine perfusion (EVNMP) provides a near physiological environment for assessment and reconditioning of kidneys prior to transplantation. Although early studies demonstrated feasibility in salvaging marginal donor kidneys, EVNMP protocols remain heterogeneous. This scoping review aimed to characterise human kidney performance during EVNMP and identify priorities for optimisation. Methodology Systematic searches of Embase, MEDLINE, and the Cochrane Library were conducted from 1960 to January 2026. Studies applying EVNMP to human kidneys in pre-clinical and clinical settings were included. Data were extracted on perfusion systems and parameters, perfusate composition, temperature, duration, biomarkers, viability assessment strategies, and transplant outcomes. Results 29 studies were included, 25 used customised circuits and four used commercial systems. Blood-based perfusates were used in 25 studies, with non-cellular perfusates in four. Perfusion strategies varied between pressure and flow controlled approaches, with urine output ranging from 10 to 500 mL/h. Biomarkers were assessed in 20 studies alongside macroscopic appearance and renal blood flow. EVNMP duration ranged from 1–2 hours to ≥24 hours. Approximately 224 discarded kidneys were used in pre-clinical research and 245 kidneys were transplanted following EVNMP, including kidneys that may otherwise have been discarded. Conclusion EVNMP enables human kidneys to function as metabolically active organs ex vivo and has progressed from experimental research to clinical application in kidney salvage for transplantation. Discarded human kidneys have been central to technique development. However, substantial heterogeneity persists across devices, perfusates, temperatures, perfusion parameters, biomarkers, and duration. This review identifies key knowledge gaps and provides a framework to guide EVNMP optimisation and future research.
Speakers
Contributors
Authors

Dr Qi Rui Soh - , Associate Professor Bulang He -

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