Development of Risk Assessment Criteria for Severe Ischemia-Reperfusion Injury in Liver Transplantation in the Context of Subsequent Perfusion Rehabilitation

Background. Ischemia-reperfusion injury (IRI) remains a significant complication following liver transplantation, associated with early allograft dysfunction and worse outcomes. Predicting the risk of IRI could guide the selection of organ preservation strategies and posttransplant management.

Objective. To develop and validate a clinical risk scoring system for predicting severe IRI in liver transplantation.

Materials and Methods. A retrospective analysis of 1,238 liver transplantations performed at the N.V. Sklifosovsky Research Institute for Emergency Medicine from 2000 to 2025 was conducted. Severe IRI was defined as peak transaminase levels >1,000 IU/L within the first 7 postoperative days. Donor, procedural, and recipient factors were evaluated using multivariable logistic regression to construct a simplified risk scoring system.

Results. Complete data were available for 827 patients, among whom 312 cases of severe IRI were recorded, accounting for 37.7% of the cohort. Independent predictors included donor age with an odds ratio of 1.04 per year, cold ischemia time with an odds ratio of 1.17 per hour, and portal vein thrombosis with an odds ratio of 1.52. The developed IRI Risk Index (IRI Score), ranging from 0 to 9 points, stratified patients into low-risk (0–2 points) with an IRI rate of 35.6%, intermediate-risk (3–5 points) with a rate of 42.1%, and high-risk (≥6 points) with a rate of 52.2%. The area under the ROC curve was 0.555 (p<0.001). High-risk patients comprised 2.8% of the cohort.

Conclusion. The IRI Score enables early identification of patients at elevated risk for severe IRI and provides a specific algorithm for decision-making regarding the use of machine perfusion. The simplicity of the index and its reliance on pre-transplant variables make it practical for clinical application.

1. Zhai Y, Petrowsky H, Hong JC, Busuttil RW, Kupiec-Weglinski JW. Ischaemia-reperfusion injury in liver transplantation—from bench to bedside. Nat Rev Gastroenterol Hepatol. 2013;10(2):79-89. https://doi.org/10.1038/nrgastro.2012.225

2. Busuttil RW, Tanaka K. The utility of marginal donors in liver transplantation. Liver Transpl. 2003;9(7):651-663. https://doi.org/10.1053/jlts.2003.50105

3. Monbaliu D, Brassil J. Machine perfusion of the liver: past, present and future. Curr Opin Organ Transplant. 2010;15(2):160-166. https://doi.org/10.1097/MOT.0b013e328337342b

4. Olthoff KM, Kulik L, Samstein B, et al. Validation of a current definition of early allograft dysfunction in liver transplant recipients and analysis of risk factors. Liver Transpl. 2010;16(8):943-949. https://doi.org/10.1002/lt.22091

5. Feng S, Goodrich NP, Bragg-Gresham JL, et al. Characteristics associated with liver graft failure: the concept of a donor risk index. Am J Transplant. 2006;6(4):783-790. https://doi.org/10.1111/j.1600-6143.2006.01242.x

6. Dutkowski P, Polak WG, Muiesan P, et al. First comparison of hypothermic oxygenated perfusion versus static cold storage of human donation after cardiac death liver transplants: an international-matched case analysis. Ann Surg. 2015;262(5):764-771. https://doi.org/10.1097/SLA.0000000000001473

7. Nasralla D, Coussios CC, Mergental H, et al. A randomized trial of normothermic preservation in liver transplantation. Nature. 2018;557(7703):50-56. https://doi.org/10.1038/s41586-018-0047-9

8. Briceño J, Marchal T, Padillo J, Solórzano G, Pera C. Influence of marginal donors on liver preservation injury. Transplantation. 2002;74(4):522-526. https://doi.org/10.1097/00007890-200208270-00015

9. Jay CL, Lyons JM, Odenwald MA, Saab S, Levin MS, Skaro AI. The impact of marginal liver donors on the recipient: an evidence-based approach to the assessment of risk. Curr Opin Organ Transplant. 2013;18(2):153-159. https://doi.org/10.1097/MOT.0b013e32835f0130

10. Vodkin I, Kuo A. Extended criteria donors in liver transplantation. Clin Liver Dis. 2017;21(2):289-301. https://doi.org/10.1016/j.cld.2016.12.004

11. Dutkowski P, Schlegel A, de Oliveira M, Müllhaupt B, Neff F, Clavien PA. HOPE for human liver grafts obtained from donors after cardiac death. J Hepatol. 2014;60(4):765-772. https://doi.org/10.1016/j.jhep.2013.11.023

12. van Rijn R, Karimian N, Matton APM, et al. Dual hypothermic oxygenated machine perfusion in liver transplants donated after circulatory death. Br J Surg. 2017;104(7):907-917. https://doi.org/10.1002/bjs.10515

13. Mergental H, Laing RW, Kirkham AJ, et al. Transplantation of declined liver allografts following normothermic ex-situ evaluation. Am J Transplant. 2016;16(11):3235-3245. https://doi.org/10.1111/ajt.13875

14. Czigany Z, Pratschke J, Fronek J, et al. Hypothermic oxygenated machine perfusion reduces early allograft injury and improves posttransplant outcomes in extended criteria donation liver transplantation from donation after brain death: results from a multicenter randomized controlled trial (HOPE ECD-DBD). Ann Surg. 2021;274(5):705-712. https://doi.org/10.1097/SLA.0000000000005110

15. Guarrera JV, Henry SD, Samstein B, et al. Hypothermic machine preservation in human liver transplantation: the first clinical series. Am J Transplant. 2010;10(2):372-381. https://doi.org/10.1111/j.1600-6143.2009.02932.x

16. Croome KP, Matheson PJ, Hallbeck MS. Cost-effectiveness of hypothermic machine perfusion preservation in liver transplantation: a decision analytic model. Clin Transplant. 2021;35(6):e14283. https://doi.org/10.1111/ctr.14283

17. Rana A, Hardy MA, Halazun KJ, et al. Survival outcomes following liver transplantation (SOFT) score: a novel method to predict patient survival following liver transplantation. Am J Transplant. 2008;8(12):2537-2546. https://doi.org/10.1111/j.1600-6143.2008.02400.x

18. Dutkowski P, Oberkofler CE, Slankamenac K, et al. Are there better guidelines for allocation in liver transplantation? A novel score targeting justice and utility in the model for end-stage liver disease era. Ann Surg. 2011;254(5):745-753. https://doi.org/10.1097/SLA.0b013e3182365081

19. Angelico R, Perera MTPR, Manzia TM, Parente A, Grimm JC, Spada M. Donors after circulatory death in liver transplantation and kidney transplantation. Curr Opin Organ Transplant. 2021;26(2):191-199. https://doi.org/10.1097/MOT.0000000000000853

20. Hoyer DP, Paul A, Gallinat A, et al. Donor information based prediction of early allograft dysfunction and outcome in liver transplantation. Liver Int. 2015;35(1):156-163. https://doi.org/10.1111/liv.12443

21. Briceno J, Ciria R, de la Mata M, Rufián S, López-Cillero P. Prediction of graft dysfunction based on extended criteria donors in the model for end-stage liver disease score era. Transplantation. 2010;90(5):530-539. https://doi.org/10.1097/TP.0b013e3181e86b11

22. Schlegel A, Muller X, Kalisvaart M, et al. Outcomes of DCD liver transplantation using organs treated by hypothermic oxygenated perfusion before implantation. J Hepatol. 2019;70(1):50-57. https://doi.org/10.1016/j.jhep.2018.10.005

23. Boteon YL, Laing RW, Schlegel A, et al. The impact on the bioenergetic status and oxidative-mediated tissue injury of a combined protocol of hypothermic and normothermic machine perfusion using an acellular haemoglobin-based oxygen carrier: the cold-to-warm machine perfusion of the liver. PLoS One. 2019;14(10):e0224066. https://doi.org/10.1371/journal.pone.0224066

24. Patrono D, Surra A, Catalano G, et al. Hypothermic oxygenated machine perfusion of liver grafts from brain-dead donors. Sci Rep. 2019;9(1):9337. https://doi.org/10.1038/s41598-019-45843-3

25. Hann A, Lembach H, McKay SC, et al. Normothermic machine perfusion of the liver: current status and future directions. Transplantation. 2022;106(1):e62-e73. https://doi.org/10.1097/TP.0000000000003850

26. Yerdel MA, Gunson B, Mirza D, et al. Portal vein thrombosis in adults undergoing liver transplantation: risk factors, screening, management, and outcome. Transplantation. 2000;69(9):1873-1881. https://doi.org/10.1097/00007890-200005150-00023

27. Selzner M, Rüdiger HA, Graf R, Clavien PA. Protective strategies against ischemic injury of the liver. Gastroenterology. 2003;125(3):917-936. https://doi.org/10.1016/s0016-5085(03)01048-5

28. Eltzschig HK, Eckle T. Ischemia and reperfusion—from mechanism to translation. Nat Med. 2011;17(11):1391-1401. https://doi.org/10.1038/nm.2507

29. Peralta C, Jiménez-Castro MB, Gracia-Sancho J. Hepatic ischemia and reperfusion injury: effects on the liver sinusoidal milieu. J Hepatol. 2013;59(5):1094-1106. https://doi.org/10.1016/j.jhep.2013.06.017

30. Kornberg A, Küpper B, Hommann M, et al. Patients with non-alcoholic fatty liver disease after liver transplantation – the FIS-Keck experience. PLoS One. 2014;9(12):e114029. https://doi.org/10.1371/journal.pone.0114029

31. Kleiner DE, Brunt EM, Van Natta M, et al. Design and validation of a histological scoring system for nonalcoholic fatty liver disease. Hepatology. 2005;41(6):1313-1321. https://doi.org/10.1002/hep.20701

32. Clavien PA, Petrowsky H, DeOliveira ML, Graf R. Strategies for safer liver surgery and partial liver transplantation. N Engl J Med. 2007;356(15):1545-1559. https://doi.org/10.1056/NEJMra065156

33. Mukhopadhyay A, Weaver JD, Hann A, et al. Donor liver steatosis influences hepatic artery flow and early graft outcomes after transplantation. HPB (Oxford). 2021;23(8):1149-1158. https://doi.org/10.1016/j.hpb.2020.12.003

34. Cucchetti A, Vitale A, Cescon M, et al. Can machine learning models predict long-term outcomes in patients with hepatocellular carcinoma after liver transplantation? Am J Transplant. 2020;20(12):3535-3544. https://doi.org/10.1111/ajt.16154

35. Lau L, Kankanige Y, Rubinstein B, et al. Machine-learning algorithms predict graft failure after liver transplantation. Transplantation. 2017;101(4):e125-e132. https://doi.org/10.1097/TP.0000000000001600

36. Briceño J, Cruz-Ramírez M, Prieto M, et al. Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study. J Hepatol. 2014;61(5):1020-1028. https://doi.org/10.1016/j.jhep.2014.05.039

37. Yoon J, Tran T, Singh S, et al. Development of machine learning models for the prediction of early allograft dysfunction in liver transplantation. Liver Transpl. 2020;26(11):1436-1449. https://doi.org/10.1002/lt.25837

38. Khosravi B, Pourahmad S, Bahreini A, et al. Deep learning enables the detection of hepatic steatosis in liver transplantation. Sci Rep. 2020;10(1):13555. https://doi.org/10.1038/s41598-020-70053-1

39. Roy S, Whitehead TD, Quirk JD, et al. Optimal co-clinical radiomics: sensitivity of radiomic features to tumour volume, image noise and resolution in co-clinical T1-weighted and T2-weighted magnetic resonance imaging. EBioMedicine. 2020;59:102963. https://doi.org/10.1016/j.ebiom.2020.102963

40. Campanella G, Hanna MG, Geneslaw L, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301-1309. https://doi.org/10.1038/s41591-019-0508-1