Comparative analysis of models predicting the risks of early poor outcome of deceased-donor liver transplantation: a retrospective single-center study

Rationale. The risk of early graft loss determines the specifics and plan of anesthesiological assistance, intensive therapy, and overall the feasibility of liver transplantation. Various prognostic models and criteria have become widespread abroad; however, Russian transplant centers have not yet validated them.
Objective. To evaluate the applicability and accuracy of the most common models predicting the risks of early adverse outcomes in liver transplantation from deceased donors.
Material and methods. A retrospective single-center study included data on 131 liver transplantations from deceased donors performed between May 2012 and January 2023. For each observation, DRI, SOFT, D-MELD, BAR, MEAF, L-GrAFT, and EASE indices were calculated, and compliance with an early allograft dysfunction criteria was verified. Depending on the possibility of calculating the indicators and their values relative to known cutoff points, the study groups were formed, and 1-, 3-, 6-, and 12-month graft survival rates were calculated. The forecast was compared with the actual outcomes, and sensitivity, specificity, F1-score, and C-index were calculated.
Results. When assessing the risk of 1- and 3-month graft loss, models using only preoperative parameters demonstrated relatively low prognostic significance: DRI (F1-score: 0.16; C-index: 0.54), SOFT (F1-score: 0.42; C-index: 0.64), D-MELD (F1-score: 0.30; C-index: 0.58), and BAR (F1-score: 0.23; C-index: 0.57). Postoperative indices of MEAF (F1- score: 0.44; C-index: 0.74) and L-GrAFT (F1-score: 0.32; C-index: 0.65) were applicable in 96%, those of ABC (F1-score: 0.29; C-index: 0.71) in 91%, and EASE (F1-score: 0.26; C-index: 0.80) in 89% of cases. The relative risk of 30-days graft loss in case of EAD was 5.2 (95% CI: 3.4-8.1; p<0.0001), F1-score: 0.64, and C-index: 0.84. Using locally established cutoff values for SOFT (11 points) and L-GrAFT (-0.87) scores increased their prognostic significance: F1-score: 0.46 and 0.63, C-index: 0.69 and 0.87, respectively.
Conclusion. The analyzed models can be used to assess the risks of early liver graft loss; however, their prognostic significance is not high. Developing a new model in a multicenter Russian study, as well as searching for new objective methods to assess the state of the donor liver are promising directions for future work.

1. Ivanics T, Wallace D, Abreu P, Claasen MPAW, Callaghan C, Cowling T, et al. Survival after liver transplantation: an international comparison between the United States and the United Kingdom in the years 2008–2016. Transplantation. 2022;106(7):1390–1400. PMID: 34753895 https://doi.org/10.1097/TP.0000000000003978

2. Adam R, Karam V, Cailliez V, O Grady JG, Mirza D, Cherqui D, et al. 2018 annual report of the European Liver Transplant Registry (ELTR) – 50-year evolution of liver transplantation. Transpl Int. 2018;31(12):1293– 1317. PMID: 30259574 https://doi.org/10.1111/tri.13358

3. Yang LS, Shan LL, Saxena A, Morris DL. Liver transplantation: a systematic review of long-term quality of life. Liver Int. 2014;34(9):1298–313. PMID: 24703371 https://doi.org/10.1111/liv.12553

4. Ohe H, Hoshino J, Ozawa M. Factors affecting outcomes of liver transplantation: an analysis of OPTN/UNOS database. Clin Transpl. 2011:39–53. PMID: 22755400

5. Haddad L, Cassenote AJ, Andraus W, de Martino RB, Ortega NR, Abe JM, et al. Factors associated with mortality and graft failure in liver transplants: a hierarchical approach. PLoS One. 2015;10(8):e0134874. PMID: 26274497 https://doi.org/10.1371/journal.pone.0134874

6. Johnson SR, Alexopoulos S, Curry M, Hanto DW. Primary nonfunction (PNF) in the MELD era: an SRTR database analysis. Am J Transplant. 2007;7(4):1003– 1009. PMID: 17286618 https://doi.org/10.1111/j.1600-6143.2006.01702.x

7. Lewis A, Koukoura A, Tsianos GI, Gargavanis AA, Nielsen AA, Vassiliadis E. Organ donation in the US and Europe: the supply vs demand imbalance. Transplant Rev (Orlando). 2021;35(2):100585. PMID: 33071161 https://doi.org/10.1016/j.trre.2020.100585

8. Neuberger J. Liver allocation. Minerva Gastroenterol Dietol. 2018;64(2):170–179. PMID: 29125260 https://doi.org/10.23736/S1121-421X.17.02452-7

9. Lee E, Johnston CJC, Oniscu GC. The trials and tribulations of liver allocation. Transpl Int. 2020;33(11):1343–1352. PMID: 32722866 https://doi.org/10.1111/tri.13710

10. Gautier SV, Khomyakov SM. Organ donation and transplantation in the Russian Federation in 2021. 14th Report from the Registry of the Russian Transplant Society. Russian Journal of Transplantology and Artificial Organs. 2022;24(3):8–31. (In Russ.). https://doi.org/10.15825/1995-1191-2022-3-8-31

11. Feng S, Goodrich NP, Bragg-Gresham JL, Dykstra DM, Punch JD, DebRoy MA, et al. Characteristics associated with liver graft failure: the concept of a donor risk index. Am J Transplant. 2006;6(4):783–790. PMID: 16539636 https://doi.org/10.1111/j.1600-6143.2006.01242.x

12. Rana A, Hardy MA, Halazun KJ, Woodland DC, Ratner LE, Samstein B, 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. PMID: 18945283 https://doi.org/10.1111/j.1600-6143.2008.02400.x

13. Halldorson JB, Bakthavatsalam R, Fix O, Reyes JD, Perkins JD. D-MELD, a simple predictor of post liver transplant mortality for optimization of donor/recipient matching. Am J Transplant. 2009;9(2):318–326. PMID: 19120079 https://doi.org/10.1111/j.1600-6143.2008.02491.x

14. Dutkowski P, Oberkofler CE, Slankamenac K, Puhan MA, Schadde E, Müllhaupt B, 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. PMID: 22042468 https://doi.org/10.1097/SLA.0b013e3182365081

15. Braat AE, Blok JJ, Putter H, Adam R, Burroughs AK, Rahmel AO, et al. The Eurotransplant donor risk index in liver transplantation: ET-DRI. Am J Transplant. 2012;12(10):2789–2796. PMID: 22823098 https://doi.org/10.1111/j.1600-6143.2012.04195.x

16. Pareja E, Cortes M, Hervás D, Mir J, Valdivieso A, Castell JV, et al. A score model for the continuous grading of early allograft dysfunction severity. Liver Transpl. 2015;21(1):38–46. PMID: 25204890 https://doi.org/10.1002/lt.23990

17. Agopian VG, Harlander-Locke MP, Markovic D, Dumronggittigule W, Xia V, Kaldas FM, et al. Evaluation of early allograft function using the liver graft assessment following transplantation risk score model. JAMA Surg. 2018;153(5):436–444. PMID: 29261831 https://doi.org/10.1001/jamasurg.2017.5040

18. Avolio AW, Franco A, Schlegel A, Lai Q, Meli S, Burra P, et al. Development and validation of a comprehensive model to estimate early allograft failure among patients requiring early liver retransplant. JAMA Surg. 2020;155(12):e204095. PMID: 33112390 https://doi.org/10.1001/jamasurg.2020.4095

19. Rhu J, Kim JM, Kim K, Yoo H, Choi GS, Joh JW. Prediction model for early graft failure after liver transplantation using aspartate aminotransferase, total bilirubin and coagulation factor. Sci Rep. 2021;11(1):12909. PMID: 34145352 https://doi.org/10.1038/s41598-021-92298-6

20. Olthoff KM, Kulik L, Samstein B, Kaminski M, Abecassis M, Emond J, 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. PMID: 20677285 https://doi.org/10.1002/lt.22091

21. Lozanovski VJ, Probst P, Arefidoust A, Ramouz A, Aminizadeh E, Nikdad M, et al. Prognostic role of the Donor Risk Index, the Eurotransplant Donor Risk Index, and the Balance of Risk score on graft loss after liver transplantation. Transpl Int. 2021;34(5):778 800. PMID: 33728724 https://doi.org/10.1111/tri.13861

22. Rana A, Jie T, Porubsky M, Habib S, Rilo H, Kaplan B, et al. The survival outcomes following liver transplantation (SOFT) score: validation with contemporaneous data and stratification of high-risk cohorts. Clin Transplant. 2013;27(4):627 632. PMID: 23808891 https://doi.org/10.1111/ctr.12181

23. Schlegel A, Linecker M, Kron P, Györi G, De Oliveira ML, Müllhaupt B, et al. Risk assessment in high- and low-MELD liver transplantation. Am J Transplant. 2017;17(4):1050–1063. PMID: 27676319 https://doi.org/10.1111/ajt.14065

24. Moosburner S, Wiering L, Roschke NN, Winter A, Demir M, Gaßner JMGV, et al. Validation of risk scores for allograft failure after liver transplantation in Germany: a retrospective cohort analysis. Hepatol Commun. 2023;7(1):e0012. PMID: 36633496 https:// doi.org/10.1097/HC9.0000000000000012

25. Kamath PS, Wiesner RH, Malinchoc M, Kremers W, Therneau TM, Kosberg CL, et al. A model to predict survival in patients with end-stage liver disease. Hepatology. 2001;33(2):464–470. PMID: 11172350 https://doi.org/10.1053/jhep.2001.22172

26. Biggins SW, Kim WR, Terrault NA, Saab S, Balan V, Schiano T, et al. Evidence-based incorporation of serum sodium concentration into MELD. Gastroenterology. 2006;130(6):1652–1660. PMID: 16697729 https://doi.org/10.1053/j.gastro.2006.02.010

27. Kim WR, Mannalithara A, Heimbach JK, Kamath PS, Asrani SK, Biggins SW, et al. MELD 3.0: the model for end-stage liver disease updated for the modern era. Gastroenterology. 2021;161(6):1887–1895.e4. PMID: 34481845 https://doi.org/10.1053/j.gastro.2021.08.050

28. Asrani SK, Kim WR, Edwards EB, Larson JJ, Thabut G, Kremers WK, et al. Impact of the center on graft failure after liver transplantation. Liver Transpl. 2013;19(9):957–964. PMID: 23784730 https://doi.org/10.1002/lt.23685

29. Blok JJ, de Boer JD, Putter H, Rogiers X, Guba MO, Strassburg CP, et al. The center effect in liver transplantation in the Eurotransplant region: a retrospective database analysis. Transpl Int. 2018;31(6):610-619. PMID: 29406577 https://doi.org/10.1111/tri.13129

30. Torterolli F, Watanabe RK, Tabushi FI, Peixoto IL, Nassif PAN, Tefilli NL, et al. BAR, SOFT and DRI post-hepatic transplantation: what is the best for survival analysis? Arq Bras Cir Dig. 202111;34(1):e1576. PMID: 34133523 https://doi.org/10.1590/0102-672020210001e1576

31. Blok JJ, Putter H, Metselaar HJ, Porte RJ, Gonella F, de Jonge J, et al. Identification and validation of the predictive capacity of risk factors and models in liver transplantation over time. Transplant Direct. 2018;4(9):e382. PMID: 30234151 https://doi.org/10.1097/TXD.0000000000000822

32. Chen S, Wang T, Luo T, He S, Huang C, Jia Z et al. Prediction of graft survival post-liver transplantation by L-GrAFT risk score model, EASE score, MEAF scoring, and EAD. Front Surg. 2021;8:753056. PMID: 34869560 https:// doi.org/10.3389/fsurg.2021.753056

33. Pogrebnichenko IV. Effektivnoe ispol'zovanie pecheni mul'tiorgannogo donora dlya transplantatsii: Cand. med. sci. diss. Synopsis. Moscow; 2014. Available at: https://www.transpl.ru/images/cms/data/pdf/avtoreferat_k_diss_pogrebnichenko_v_pechat.pdf [Accessed March 22, 2023]. (In Russ.).

34. Gulyaev VA. Povyshenie effektivnosti transplantatsii pecheni putem sovershenstvovaniya tekhnologii iz"yatiya i podgotovki transplantata: Dr. med. sci. diss. Synopsis. Moscow; 2016. Available at: https://med.ru/sites/default/files/docs/Avtoref_Guliaev.pdf [Accessed March 22, 2023]. (In Russ.).

35. Gubarev KK. Optimizatsiya mezhregional'noy i mezhvedomstvennoy sistemy koordinatsii posmertnogo donorstva organov i tkaney cheloveka: Dr. med. sci. diss. Synopsis. Moscow; 2022. Available at: https://sklif.mos.ru/upload/iblock/7f9/x5xai2cmzsi40912nno2woymig9sf01t.pdf [Accessed March 22, 2023]. (In Russ.).

36. Minina MG, Voronov DV, Tenchurina EA. Evolution of liver donation in Moscow. Movement towards expanded donor selection criteria. Russian Journal of Transplantology and Artificial Organs. 2022;24(3):102–110. (In Russ.). https:// doi.org/10.15825/1995-1191-2022-3-102-110

37. Korobka VL, Pak ES, Shapovalov AM, Kostrykin MU, Tkachev AV. Analysis of four-year management of the waiting list for liver transplantation in Rostov region: prospects for reducing mortality of candidates listed for liver transplantation. Medical Herald of the South of Russia. 2019;10(3):32–39. (In Russ.). https:// doi.org/10.21886/2219-8075-2019-10-3-32-39

38. Korobka VL, Pasechnikov VD, Pak ES, Kostrykin MY, Tkachev AV, Balin NI, et al. Delisting of liver transplant candidates following recompensation of chronic liver diseases – patient characteristics and predictors of delisting: a prospective study. Russian Journal of Transplantology and Artificial Organs. 2019;21(4):26–35. (In Russ.). https://doi.org/10.15825/1995-1191-2019-4-26-35

39. Korobka VL, Kostrykin MYu, Pak ES, Dabliz RO, Shapovalov AM. Predicting death in patients with endstage liver disease: a new model for assessing disease severity. Innovative Medicine of Kuban. 2020;(2):21–27. (In Russ.). https://doi.org/10.35401/2500-0268-2020-18-2-21-27

40. Moysyuk YG, Poptsov VN, Sushkov AI, Moysyuk LY, Malinovskaya YuO, Belskikh LV. Early liver allograft dysfunction: risk factors, clinical course and outcomes. Transplantologiya. The Russian Journal of Transplantation. 2016;(2):16–28. (In Russ.).

41. Ferrarese A, Sartori G, Orrù G, Frigo AC, Pelizzaro F, Burra P, et al. Machine learning in liver transplantation: a tool for some unsolved questions? Transpl Int. 2021;34(3):398–411. PMID: 33428298 https://doi.org/10.1111/tri.13818

42. Novruzbekov MS, Olisov OD, Magomedov KM. Patent № 2652065 C1 Russian Federation. Sposob otbora donorskogo organa dlya transplantatsii pecheni. № 2017141179. Stated November 27, 2017; published April 24, 2018. Bull. № 12. Available at: https://elibrary.ru/download/elibrary_37366995_36132028. PDF [Accessed March 22, 2023].

43. Reznik ON, Skvortsov AE, Lopota AV, Gryaznov NA, Kharlamov VV, Kireeva GS. Perfusion device for liver preservation ex vivo before transplantation: first experimental study. Russian Journal of Transplantology and Artificial Organs. 2017;19(1):35–40. (In Russ.) https://doi.org/10.15825/1995-1191-2017-1-35-40

44. Skvortsov AE, Kutenkov AA, Reznik ON. Apparatno-perfuzionnoye «ozhivleniye» izolirovannoy donorskoy pecheni ex vivo. Russian Journal of Transplantology and Artificial Organs. 2020;19(S):83–84. Available at: https://journal.transpl.ru/vtio/article/view/1220/992 [Accessed March 22, 2023]. (In Russ.).

45. Granov DA, Zherebtsov FK, Borovik VV, Tileubergenov II, Belov AD, Zhuykov VN, et al. Patent № 2765462 C1 RF. Sposob prognozirovaniya riska vozniknoveniya ranney disfunktsii transplantata trupnoy pecheni. № 2021117157. Stated June 11, 2021; published January 31, 2022. Bull. № 4. Available at: https://elibrary.ru/download/elibrary_47993013_39892098.PDF992 [Accessed March 22, 2023]. (In Russ.).

46. Sushkov AI, Voskanyan SE, Rudakov VS, Popov MV, Gubarev KK, Svetlakova DS, et al. Interstitial glucose metabolism monitoring as an additional method for objective assessment of donor liver, prediction and immediate diagnosis of early graft dysfunction. Sovrem Tekhnologii Med. 2022;14(3):28–41. (In Russ.) https://doi.org/10.17691/stm2022.14.3.04