The Use of Fatty Liver Grafts in Modern Allocation Systems

ORIGINAL ARTICLES FROM THE ESA PROCEEDINGS

The Use of Fatty Liver Grafts in Modern Allocation SystemsRisk Assessment by the Balance of Risk (BAR) Score

Philipp Dutkowski, MD,∗ Andrea Schlegel, MD,∗ Ksenija Slankamenac, MC,∗ Christian E. Oberkofler, MD,Rene Adam, MD,‡ Andrew K. Burroughs, MD,§ Eric Schadde, MD,∗ Beat Mullhaupt, MD,†

and Pierre-Alain Clavien, MD, PhD∗

Objective: To integrate the amount of hepatic steatosis in modern liver allo-cation models.Background: The aim of this study was to combine the 2 largest liver trans-plant databases (United States and Europe) in 1 comprehensive model topredict outcome after liver transplantation, with a novel focus on the impactof the presence of steatosis in the graft.Methods: We adjusted the balance of risk (BAR) score for its application tothe European Liver Transplant Registry (ELTR) database containing 11,942patients. All liver transplants from ELTR and United Network for Organ Shar-ing with recorded liver biopsies were then combined in one survival analysisin relation to the presence of graft micro- (n = 9,677) and macrosteatosis(n = 11,516).Results: Microsteatosis, regardless of the amount, was associated with a simi-lar relationship between mortality and BAR score as nonsteatotic livers. Low-grade macrosteatotic liver grafts (≤30% macrosteatosis) resulted in 5-yeargraft-survival rates of 60% or more up to BAR 18, comparable to nonsteatoticgrafts. However, use of moderate or severely steatotic liver grafts (>30%macrosteatosis) resulted in acceptable outcome only if the cumulative risk attransplant was low, that is, BAR score of 9 or less.Conclusions: Microsteatotic or 30% or less macrosteatotic liver grafts can beused safely up to BAR score of 18 or less, but liver grafts with more than 30%macrosteatotis should be used with risk adjustment, that is, up to BAR scoreof 9 or less.

Keywords: liver transplantation, macrosteatotic liver, microsteatotic liver,risk adjustment

(Ann Surg 2012;256: 861–869)

W e recently developed and validated a score (balance of risk,BAR score), which detected unfavorable combinations among

6 key donor and recipient factors in orthotopic liver transplantationcompared with other systems.1 However, this score, like other pre-diction scores,2–9 failed to include data on hepatic steatosis, whichis considered a crucial risk factor.10–15 Despite the large body of ev-idence of postreperfusion injury related to graft steatosis,16 clinical

From the ∗Swiss HPB and Transplant Center, Department of Surgery, UniversityHospital Zurich, Switzerland; †Department of Gastroenterology and Hepatol-ogy, University Hospital Zurich, Switzerland; ‡Hepato Biliary Center, PaulBrousse Hospital, Villejuif, Paris South University; and European Liver Trans-plant Registry (ELTR); and §The Royal Free Sheila Sherlock Liver Centre, TheRoyal Free Hospital, London; University College London (UCL); and Euro-pean Liver and Intestine Transplant Association (ELITA), Birmingham, UnitedKingdom.

The authors P.D. and A.S. contributed equally.This work was supported by a grant from the Liver and Gastrointestinal Disease

(LGID) Foundation.Disclosure: The authors declare no conflicts of interest.Reprints: Pierre-Alain Clavien, MD, PhD, Department of Surgery, University

Hospital Zurich, Raemistrasse 100, CH-8091 Zurich, Switzerland. E-mail:[email protected].

Copyright C© 2012 by Lippincott Williams & WilkinsISSN: 0003-4932/12/25605-0861DOI: 10.1097/SLA.0b013e318272dea2

outcome after orthotopic liver transplantation using steatotic livergrafts is inconsistently reported,17 with highly variable survival rates,suggesting the potential impact of additional key factors. We thereforehypothesized that increased donor hepatic steatosis is an independentrisk factor for survival after orthotopic liver transplantation if the sumof risk factors for donor, graft, and recipient is too high.

To test this hypothesis, we combined the 2 largest availabledatabases (United Network for Organ Sharing, and European LiverTransplant Registry, ELTR), in one comprehensive model, to analyzeoutcome in biopsy-proven liver graft steatosis according to risk stratadefined by the BAR scoring system. As the BAR score was devel-oped and validated only with the United Network for Organ Sharingdatabase, we first tested the predictive value of the BAR system inthe ELTR database.

METHODSStudy Design and Patients

After approval by the University of Washington institutionalreview board, records of all adult recipients (≥18 years) who un-derwent a transplant in the United States for acute or chronic liverdisease from deceased (after brain death) donors were extracted froma United Network for Organ Sharing Standard Transplant Analysisand Research file from March 2002 (model for end-stage liver dis-ease) implementation in the United States through September 30,2010.

After approval of the European Liver and Intestine TransplantAssociation board, records of adult recipients the ELTR populationfrom 2007 to 2010 were included in a second data set. Our owndatabase included patients who underwent a transplant in our centerfrom January 1, 2003, to December 31, 2011 (n = 286).

In all 3 populations, we excluded partial transplants (splitand living donor liver transplantation), donation after cardiac death,and combined liver transplants. Model for end-stage liver diseasescore was calculated without inclusion of exception points (labora-tory model for end-stage liver disease). The final analysis refers to37,255 patients in the United Network for Organ Sharing database,to 11,942 patients in the ELTR database, and to 286 patients at ourcenter. Biopsy-proven steatosis was reported in increments of 5%(United Network for Organ Sharing) or 30% (ELTR). Overall, 8567(United Network for Organ Sharing) and 1907 cases (ELTR), respec-tively, showed graft microsteatotis, whereas 9177 and 3391 cases,respectively, showed graft macrosteatotis (Fig. 1).

Cases with known follow-up (patient and graft survival) andreported micro- (n = 9677) or macrosteatosis (n = 11516) (Fig. 1)were combined in a United Network for Organ Sharing-ELTR liverbiopsy database. In our own population, 249 biopsies were recordedin 286 cases (87%), with standard staining (hematoxylin and eosin).All cases were grouped in 3 categories, for example, 0 to 5% steatosis,more than 5% to 30% steatosis, and more than 30% steatosis.

Copyright © 2012 Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.

Annals of Surgery � Volume 256, Number 5, November 2012 www.annalsofsurgery.com | 861

mailto:[email protected].

Dutkowski et al Annals of Surgery � Volume 256, Number 5, November 2012

FIGURE 1. Flow chart of included patients.

STATISTICAL ANALYSISThe previously reported regression model for the BAR score1

was adjusted by stepwise backward multivariate logistic regressionfor application of an adjusted BAR score, because of the lack ofone key variable (need for life support before transplant) in theELTR database. The adjusted risk analysis (adjusted BAR score)resulted in changes of the original reported regression coefficients,1

with additional points for model for end-stage liver disease score,re-transplantation and donor age, but fewer points for cold ischemia(Table 1). In terms of comparability of the adjusted BAR versus orig-inal BAR system, all score points for each variable were adapted tothe original BAR scale of 0 to 27 (Table 1). The final model consistedof 0 or 5 points for re-transplantation, 0 to 16 points for model forend-stage liver disease score, 0 to 3 points for recipient age, 0 to 2points for donor age, and 0 to 1 points for cold ischemia.

The model was well calibrated, as indicated by comparisonof observed risk (7.2%) against predicted risk (7.2%) for groupsof patients (Hosmer Lemeshow test, P = 0.21). Internal validationconfirmed a c-statistic of 0.7, comparable to the original BAR score.1

Statistical analysis was performed using Stat 10.1 and SPSS,version 19. Univariate analysis of survival was undertaken by Logrank test, and graft loss and mortality by Fisher exact test. Multivari-ate survival analysis was performed using Cox proportional hazardmodel.

RESULTSDetermination of a Reliable Threshold for IncreasedOverall Mortality After Orthotopic LiverTransplantation

We previously reported a prediction model for patient sur-vival (balance of risk score, BAR score) after orthotopic livertransplantation,1 resulting in a score range from BAR 0 to 27, witha best cutoff for exponentially decreased patient survival above athreshold BAR score of 18. We planned to test this score in theELTR. However, because of a lack of 1 key variable of the origi-nal BAR score in the ELTR database (life support at the time oftransplant), we adjusted the score for the remaining 5 variables, forexample, recipient age, model for end-stage liver disease score, re-transplantation, cold ischemia, and donor age (see Methods section).

We applied this adjusted BAR score to the original US database (n= 37,255), and then to the ELTR database (n = 11,942). Median( interquartile range) follow-up was 742 (246–1148) days (UnitedNetwork for Organ Sharing), and 351 (86–743) days (ELTR).

One-year mortality after orthotopic liver transplantation perBAR score point remained unchanged (less than 20%) for the originalas for the adjusted model up to BAR 18 (Fig. 2). Importantly, theadjusted BAR score appeared less selective for higher score ranges,excluding therefore more patients at a threshold of adjusted BAR 18,as compared to the original BAR 18 (5.7%, n = 2142/37,255 vs 2.9%,n = 1117/37,255), due to the lack of a key variable (life support atthe time of transplant). Above BAR 18 (original and adjusted model),mortality increased exponentially in both models in the US populationand in the ELTR population, thus supporting a best cutoff at BAR 18(Fig. 2).

Threshold for Increased Overall Morbidity AfterOrthotopic Liver Transplantation

Modern and accepted grading systems for morbidity havebeen published,18 but their availability in liver transplant databasesis scarce. In an attempt to analyze morbidity after orthotopic livertransplantation in a large set of patients, we investigated total lengthof hospital stay as a surrogate marker for overall morbidity, in rela-tion to the original and the adjusted BAR score after orthotopic livertransplantation in the US population.

Median hospital stay after orthotopic liver transplantationremained stable up to BAR 9 and increased above this with eachfurther BAR point (original and adjusted model) (Fig. 3). Forexample, median hospital stay in the US population was 9 daysfor BAR 0 to 9 (interquartile range: 7–13), 13 days for BAR>9 to 18 (interquartile range: 8–22), and 22 days for BAR >18(interquartile range: 11–33). Because of a lack of data, we could notvalidate these results with the ELTR database. However, to furtherinvestigate morbidity after orthotopic liver transplantation, we usedour own database to analyze intensive care unit stay, hospital stay,and complication rates after orthotopic liver transplantation andcorrelated them to the adjusted BAR score. Overall morbidity, basedon the Clavien-Dindo grading,18 increased from 41 to 64 and 88%,respectively, in our population for adjusted BAR 0 to 9, >9 to 18,and >18. Notably, Clavien I to III complications occurred at similar


862 | www.annalsofsurgery.com C© 2012 Lippincott Williams & Wilkins

Annals of Surgery � Volume 256, Number 5, November 2012 An Unsolved Dilemma in Liver Transplantation

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FIGURE 2. One-year mortality remains 20% or less up to a threshold of BAR 18 for adjusted and original BAR scores in the USpopulation (n = 37,255). The adjusted BAR model was validated in the ELTR population (n = 11,942) with comparable cutoff forincreasing mortality.

FIGURE 3. Median length of hospital stay increased above athreshold of BAR 9 (adjusted and original BAR scores). Up toBAR 9, median hospital stay remained 10 or fewer days.

rates despite different BAR groups (Fig. 4A), whereas Clavien IVcomplications (need for intensive care unit management, singleor multiorgan dysfunction18) were significantly different betweenadjusted BAR 0 to 9 versus >9 (Fig. 4A). Median intensive care unitand hospital stay in our population were 3 (interquartile range: 2–5)and 18 (interquartile range: 13–26) days for adjusted BAR 0 to 9, 7(interquartile range: 3–14) and 25 (interquartile range: 18–45) daysfor adjusted BAR >9 to 18, 12 (interquartile range: 4–55) and 32(interquartile range: 22–62) days for adjusted BAR >18 (Fig. 4B).Correspondingly, cost increased significantly at BAR >9 (Fig. 4C).

Overall morbidity after orthotopic liver transplantation thusappeared relatively low up to BAR 9, whereas overall mortality re-mained stable up to BAR 18 in the US population and Europeanpopulations. These data are consistent with the original descriptionof the BAR system.1

Influence of Steatosis on Graft and Patient SurvivalAfter Orthotopic Liver Transplantation?

Using these criteria for morbidity and mortality after ortho-topic liver transplantation, 3 risk groups were defined: BAR 0 to 9(low morbidity, low mortality) BAR >9 to 18 (increased morbid-ity, low mortality), and BAR >18 (high morbidity, high mortality).The influence of graft steatosis on mortality and graft loss in theserisk groups was then analyzed. To maximize the number of caseswith available information on hepatic steatosis, we combined theUS and ELTR databases with available liver biopsies (n = 9677 formicrosteatosis, n = 11,516 for macrosteatosis, Fig. 1).

Multivariate Cox regression analysis, including the variablesmacrosteatosis, microsteatosis, hepatocellular carcinoma (HCC), andHepatitis C demonstrated no survival differences when groups with 0to 5 and more than 5% to 30% micro- or macrosteatosis were analyzed.These 2 groups were therefore combined. Further analysis, stratifiedfor BAR risk groups, confirmed the lack of impact of microsteatosis(P = 0.34), but a significant impact in the presence of more than 30%macrosteatosis on graft survival (P = 0.008, hazard ratio = 1.33, 95%confidence interval = 1.076–1.642) and also patient survival (P =0.029, hazard ratio = 1.29, 95% confidence interval = 1.027–1.642).One-year graft survival rates were between 82% and 86% withinthe risk stratum BAR 0 to 9, regardless of the amount and type ofsteatosis (Figs. 5, 6). In BAR >9 to 18, 5-year graft survival remainedmore than 60% for mild, moderate, and severe microsteatosis (Fig.6) In contrast, livers with more than 30% macrosteatosis showedsignificantly decreased graft survival compared with livers with 30%or less macrosteatosis (1-year survival 74% vs 82%, 3-year survival59% vs 69%, 5-year survival 49% vs 55%, P = 0.01, log rank test,Fig. 5). Similar results regarding macrosteatosis were obtained inour population (Fig. 7) (median follow-up 968 (interquartile range:309–1883 days).

DISCUSSIONThis study validates the previously described “BAR” predic-

tion score model1 in the large European database. It also characterizes3 BAR risk groups and demonstrates a linear increase of morbiditythrough these risk groups, but increased mortality only with a BARscore of more than 18 points. Finally, it demonstrates, using a com-bined United Network for Organ Sharing -ELTR database, that overallmortality and graft loss increase at a threshold of BAR >9 if there




FIGURE 4. Grade IV complications (need for intensive care unit management, single or multiorgan dysfunction) and mortality(grade V) were significantly different between BAR risk groups (0–9, >9–19, >18). Hospital and intensive care unit stay increasedlinearly with increasing risk.

FIGURE 5. Multivariate Cox regression confirmed 5-year survival rates of at least 60% for grafts up to 30% macrosteatosis andrecipients up to BAR 18 (marked yellow). If grafts exceeded 30% macrosteatosis, 5-year survival decreased to 55% at BAR 0 to 9,and to 49% at BAR >9 to 18. References for threshold recommendations.19–22

FIGURE 6. Microsteatosis did not affect Kaplan-Meier graft survival in all 3 risk classes (BAR 0–9, >9–18, >18), resulting in 5-yearsurvival of 60% or more up to BAR 18 (marked yellow). References for threshold recommendations.19–22




FIGURE 7. Multivariate Cox re-gression of own data regard-ing macrosteatosis confirmed ac-ceptable survival rates up to30% macrosteatosis and BAR 18(marked yellow). In more than 30%macrosteatosis, 1-year and 3-yearsurvival were 85% and 75% atBAR 0 to 9 but decreased signifi-cantly (P < 0.01, log rank test) to65% and 59%, respectively, at BAR>9 to 18. References for thresholdrecommendations.19–22

is more than 30% macrosteatosis in a liver graft. In contrast, a lesserdegree of graft macrosteatosis and microsteatosis were not associatedwith higher mortality risk compared with nonsteatotic livers.

In light of a scarce supply of organs and the currently wide useof the model for end-stage liver disease allocation system through-out the world, transplant centers are repeatedly confronted withdifficult decisions regarding the use of marginally acceptable organs,particularly considering that model for end-stage liver disease-basedallocation policies favor allocating grafts to the sickest patients. Arecurring question is how to use “suboptimal” grafts in the highestrisk candidates. Some centers have experienced poor results in highmodel for end-stage liver disease recipients,23 whereas others havereported a clear survival benefit in intention to treat analyses.24–27

We recently developed a new risk score (BAR) to predict pa-tient survival after OLT by combining 6 key donor and recipientfactors.1 This score was shown, using the large US database, to besuperior to other prediction systems such as model for end-stageliver disease, D-MELD, Donor Risk Index (DRI), and survival out-comes following liver transplantation (SOFT) score in terms of dis-crimination of patient survival and highest specificity for predictingmortality.1 The BAR score appeared most suitable to identify poordonor/recipient matches, but with 2 important shortcomings prevent-ing its wide application. First, the score had to be validated in anotherlarge population, and second, it failed to take into account the pres-ence of steatosis in the graft, which is a frequent finding and a well-established cause of enhanced reperfusion injury.16 Information ongraft steatosis is also lacking in all other available scoring systems, in-cluding DRI,4 model for end-stage liver disease,28 D-MELD,7 SOFT,5

survival after liver transplantation score (SALT),6 sequential organfailure assessment score (SOFA).9

Most allocation formulas have been derived from the largeUnited Network for Organ Sharing database, but validation in theEuropean database (ELTR) has rarely been performed. In addition,many complicated score models, such as the survival outcomes fol-lowing liver transplantation score,5 could not be tested in the ELTRdatabase because of a lack of recorded data in Europe. Accordingly,we could not apply the original BAR score to the ELTR databasebecause of a missing variable (life support at the time of transplant).Therefore, we adjusted the BAR score, resulting in a slightly changedscore point distribution for the remaining variables. Next, we testedthis adjusted BAR score in the ELTR database after validation in theUnited Network for Organ Sharing database. The adjusted BAR scorediscriminated patient survival in the United States, and in the ELTR

database, with a reliable cutoff at 18. This result is consistent withthe initial BAR score.1

An inherent shortcoming of our analysis for steatotic livergrafts relates to an objective assessment of graft steatosis in alldatabases. We have no information on staining procedures of reg-istered biopsies and variability of interpretation is likely, in lightof previous reports, which demonstrate inconsistency among expertpathologists in qualitatively and quantitatively interpreting hepaticsteatosis.29 To minimize this, we purposely collected the maximumavailable information on recorded liver biopsies by combining theUnited Network for Organ Sharing and ELTR databases. In addition,we included a control group by analyzing our own population with ahigh percentage of standardized liver graft biopsies.

Risk subgroups were defined by the observation of increasedmorbidity and hospital stay above a BAR score of 9 and increasedoverall mortality above a score of 18. This led to the selection of 3risk strata: adjusted BAR 0 to 9, 10 to 18, and >18. Among these riskstrata, graft microsteatosis did not impact patient or graft survival.In contrast, moderate or severe macrosteatosis (>30%) resulted indecreased survival compared to lesser degrees of macrosteatosis. As-suming 5-year survival rates of at least 60%, which have been pro-posed to outweigh the harm to other patients on the waiting list,19,20

transplant surgeons could therefore accept microsteatotic liver grafts,regardless of the degree, for every recipient up to BAR 18. Simi-larly, macrosteatotic liver grafts could be safely implanted, if theydo not exceed 30% of macrosteatosis. Transplantation of more than30% macrosteatotic liver grafts, however, appears justified only atBAR 0 to 9, because in this group, 1-year (82%) and 3-year sur-vival rates (69%) were comparable to outcomes with less degrees ofmacrosteatosis.

These results fit well with the strategy intuitively applied bymany transplant surgeons to use steatotic liver grafts only for candi-dates in relatively good clinical condition needing an orthotopic livertransplantation (eg, cirrhotic patients with hepatocellular carcinomahaving model for end-stage liver disease scores of <25), and avoidingusing them for recipients with fulminant liver failure or in the settingof re-transplantation.30–32 This strategy is based on the rationale thathealthier recipients could better tolerate a poor initial graft functionor major postoperative complications.33–35 Underlining this thesis,we have previously reported a series of 20 patients enjoying anexcellent outcome despite the use of steatotic grafts, in great partrelated to the selection of recipients presenting with a low modelfor end-stage liver disease score.36 This strategy was possible only




because the allocation policy in Switzerland was center-based at thattime, that is, before the introduction of model for end-stage liverdisease allocation. In this study, we confirm the importance of thebest possible match (or balance) in a large database and our owndata, demonstrating acceptable graft survival in spite of orthotopicliver transplantation of more than 30% macrosteatotic livers, if otherkey recipient and donor factors are kept at low risk (BAR ≤9).

Another recent study, arising also from the United Network forOrgan Sharing data including 5051 patients receiving a steatotic liversimilarly showed that the presence of more than 30% macrosteatosisis an independent risk factor associated with reduced 1-year graftsurvival.37 It was suggested that donor livers with more than 30%macrosteatosis may be successfully used, if 3 other donor or graft riskfactors are considered (eg, donor age < 40 years, cold ischemia < 5hours, and no donation after cardiac death).37 Here, we report a morecomprehensive calculation which can be easily performed beforedeciding whether or not to accept a liver graft for transplantation(www.barscore.org).

Whether and to what extent risk adjustment is also necessaryin cases with additional donor warm ischemia, such as in donationafter cardiac death, remains to be addressed by further studies. In apreliminary investigation using the United Network for Organ Sharingdatabase, we noted that patient survival in donation after cardiacdeath liver grafts also decreases significantly above a BAR score 9,compared with a score above 18 in grafts donated after brain death.The risk adjustment using the BAR score parallel the data obtainedwith the presence of moderate steatosis in the liver graft.

As the United Network for Organ Sharing and ELTR data areclearly limited on severely steatotic livers, we could not further test theimpact of BAR risk groups on survival in high-grade macrosteatosis(>60%). Our current results suggest that an even lower BAR thresholdmight be necessary in such cases.

In conclusion, an appropriate balance between donor age,graft macrosteatosis, graft ischemia time, recipient model for end-stage liver disease score, recipient age, and higher operative risk(re-transplantation) is crucial to outcomes after orthotopic liver trans-plantation. For survival prediction, we recommend to also considerlife support as a key variable, as the original BAR model offers ahigher specificity than the adjusted model. We suggest that less than30% macrosteatosis can be used without risk adjustment up to a BARscore of 18, but more than 30% macrosteatosis should call for cautionand should be accepted only with a BAR score of 9 or less.

ACKNOWLEDGMENTSThe data reported here have been supplied partially by the

United Network for Organ Sharing as the contractor for the OrganProcurement and Transplantation Network and by the ELTR. The in-terpretation and reporting of these data are the responsibility of theauthors and in no way should be seen as an official policy of or inter-pretation by the Organ Procurement and Transplantation Network,by the US Government or by the ELTR.

REFERENCES1. Dutkowski P, Oberkofler CE, Slankamenac K, et al. Are there better guidelines

for allocation in liver transplantation? A novel score targeting justice and utilityin the model for end-stage liver disease era. Ann Surg. 2011;254:745–753;discussion 753.

2. Desai NM, Mange KC, Crawford MD, et al. Predicting outcome af-ter liver transplantation: utility of the model for end-stage liver diseaseand a newly derived discrimination function. Transplantation. 2004;77:99–106.

3. Brown RS, Jr, Kumar KS, Russo MW, et al. Model for end-stage liver dis-ease and Child-Turcotte-Pugh score as predictors of pretransplantation diseaseseverity, posttransplantation outcome, and resource utilization in United Net-work for Organ Sharing status 2A patients. Liver Transpl. 2002;8:278–284.

4. Feng S, Goodrich NP, Bragg-Gresham JL, et al. Characteristics associatedwith liver graft failure: the concept of a donor risk index. Am J Transplant.2006;6:783–790.

5. Rana A, Hardy MA, Halazun KJ, et al. Survival outcomes following liver trans-plantation (SOFT) score: a novel method to predict patient survival followingliver transplantation. Am J Transplant. 2008;8:2537–2546.

6. Weismuller TJ, Prokein J, Becker T, et al. Prediction of survival after liver trans-plantation by pre-transplant parameters. Scand J Gastroenterol. 2008;43:736–746.

7. Halldorson JB, Bakthavatsalam R, Fix O, et al. D-MELD, a simple predictorof post liver transplant mortality for optimization of donor/recipient matching.Am J Transplant. 2009;9:318–326.

8. Ghobrial RM, Gornbein J, Steadman R, et al. Pretransplant model to predictposttransplant survival in liver transplant patients. Ann Surg. 2002;236:315–322; discussion 322–323.

9. Schrem H, Reichert M, Reichert B, et al. Value of the SOFA score as a predictivemodel for short-term survival in high-risk liver transplant recipients with a pre-transplant labMELD score ≥30. Langenbecks Arch Surg. 2012;397:717–726.

10. El-Badry AM, Graf R, Clavien PA. Omega 3–Omega 6: what is right for theliver? J Hepatol. 2007;47:718–725.

11. Pessayre D, Fromenty B. NASH: a mitochondrial disease. J Hepatol.2005;42:928–940.

12. McCormack L, Clavien PA. Understanding the meaning of fat in the liver.Liver Transpl. 2005;11:137–139.

13. Selzner M, Clavien PA. Fatty liver in liver transplantation and surgery. SeminLiver Dis. 2001;21:105–113.

14. Donnelly KL, Smith CI, Schwarzenberg SJ, et al. Sources of fatty acids storedin liver and secreted via lipoproteins in patients with nonalcoholic fatty liverdisease. J Clin Invest. 2005;115:1343–1351.

15. Silva MA. Putting objectivity into assessment of steatosis. Transplantation.2009;88:620–621.

16. Nocito A, El-Badry AM, Clavien PA. When is steatosis too much for trans-plantation? J Hepatol. 2006;45:494–499.

17. McCormack L, Dutkowski P, El-Badry AM, et al. Liver transplantation usingfatty livers: always feasible? J Hepatol. 2011;54:1055–1062.

18. Dindo D, Demartines N, Clavien PA. Classification of surgical complications:a new proposal with evaluation in a cohort of 6336 patients and results of asurvey. Ann Surg. 2004;240:205–213.

19. Volk ML, Vijan S, Marrero JA. A novel model measuring the harm of trans-planting hepatocellular carcinoma exceeding Milan criteria. Am J Transplant.2008;8:839–846.

20. Samuel D, Colombo M, El-Serag H, et al. Toward optimizing the indicationsfor orthotopic liver transplantation in hepatocellular carcinoma. Liver Transpl.2011;17:S6–S13.

21. Clavien PA, Lesurtel M, Bossuyt PM, et al. Recommendations for liver trans-plantation for hepatocellular carcinoma: an international consensus conferencereport. Lancet Oncol. 2012;13:e11–e22.

22. National Specialist Commissioning Advisory Group (NSCAG). Introduction.In: National Liver Transplant Standards. Whitehall, London: The Departmentof Health; 2005.

23. Weismuller TJ, Fikatas P, Schmidt J, et al. Multicentric evaluation of modelfor end-stage liver disease-based allocation and survival after liver trans-plantation in Germany—limitations of the ‘sickest first’-concept. Transpl Int.2011;24:91–99.

24. Dutkowski P, Oberkofler CE, Bechir M, et al. The model for end-stage liver dis-ease allocation system for liver transplantation saves lives, but increases mor-bidity and cost: a prospective outcome analysis. Liver Transpl. 2011;17:674–684.

25. Schaubel DE, Guidinger MK, Biggins SW, et al. Survival benefit-baseddeceased-donor liver allocation. Am J Transplant. 2009;9:970–981.

26. Schaubel DE, Sima CS, Goodrich NP, et al. The survival benefit of deceaseddonor liver transplantation as a function of candidate disease severity and donorquality. Am J Transplant. 2008;8:419–425.

27. Merion RM, Schaubel DE, Dykstra DM, et al. The survival benefit of livertransplantation. Am J Transplant. 2005;5:307–313.

28. Wiesner R, Edwards E, Freeman R, et al. Model for end-stage liver disease(MELD) and allocation of donor livers. Gastroenterology. 2003;124:91–96.

29. El-Badry AM, Breitenstein S, Jochum W, et al. Assessment of hepatic steatosisby expert pathologists: the end of a gold standard. Ann Surg. 2009;250:691–697.

30. Lo IJ, Lefkowitch JH, Feirt N, et al. Utility of liver allograft biopsy obtainedat procurement. Liver Transpl. 2008;14:639–646.

31. Imber CJ, St Peter SD, Handa A, et al. Hepatic steatosis and its relationship totransplantation. Liver Transpl. 2002;8:415–423.



http://www.barscore.org


32. Selzner N, Selzner M, Jochum W, et al. Mouse livers with macrosteatosis aremore susceptible to normothermic ischemic injury than those with microsteato-sis. J Hepatol. 2006;44:694–701.

33. El-Badry AM, Moritz W, Contaldo C, et al. Prevention of reperfusion injuryand microcirculatory failure in macrosteatotic mouse liver by omega-3 fattyacids. Hepatology. 2007;45:855–863.

34. Ijaz S, Winslet MC, Seifalian AM. The effect of consecutively larger dosesof L-arginine on hepatic microcirculation and tissue oxygenation in hepaticsteatosis. Microvasc Res. 2009;78:206–211.

35. Shirabe K, Kin S, Shinagawa Y, et al. Inhibition of thromboxane A2 activityduring warm ischemia of the liver. J Surg Res. 1996;61:103–107.

36. McCormack L, Petrowsky H, Jochum W, et al. Use of severely steatotic grafts inliver transplantation: a matched case-control study. Ann Surg. 2007;246:940–946; discussion 946–948.

37. Spitzer AL, Lao OB, Dick AA, et al. The biopsied donor liver: incorporatingmacrosteatosis into high-risk donor assessment. Liver Transpl. 2010;16:874–884.

DISCUSSANTS

J. Lerut (Brussels, Belgium):Thank you very much for your presentation which contains

very useful information. You may remember that your group advo-cated some years ago before this same audience that very steatoticlivers could be used without major restrictions. You may also remem-ber that there was a lot of criticism and doubt about this and in factthe results of your study confirm that these criticisms were correct.You showed us today in a very eloquent way that the sound clinicaljudgment of most liver transplant surgeons not to implant severelysteatotic livers in very sick patients can now be transformed in arelatively simple scoring system. We all know from experience thatthe implantation of a steatotic liver in a sick recipient is not onlydangerous for the patient but also that this combination enormouslyraises the cost of the intervention. In our hospital, we were able toshow that such combination tripled the length of hospital stay and thecosts were raised at least 5 times.

The BAR score is indeed a simple means of allocating liverallografts in a justified way taking into account not only morbidityand mortality, which is of course important, but what is even moreimportant is the length of the intensive care unit and the hospitalstays are also taken into consideration. It means, and I think this is themain merit of this study, that your analysis takes into account, whatI consider today the most important feature in liver transplantation,how to economically justify this intervention. Indeed, we cannot goon transplanting patients, which costs a lot of money and finally, asyou showed very well, have an almost zero result at 1 year.

I have several questions for you. My first question relates to thecentral pathology reading to quantify the degree of allograft steatosis.Do you think we should impose on organ allocation organizations notonly to collect biopsy results of such allografts but, what is moreimportant, to organize their central reading? I indeed think this is anessential step to take.

My second question is a more practical one. Most of us receivea call for an adult liver transplantation during the night and it is notalways so easy to decide on the scoring system to choose for eachrecipient. Do you think it would be possible to change the scoringsystem by reintroducing the factor that you in fact have eliminated,namely the life support? Why didn’t you use in your model for theUnited Network for Organ Sharing state of the liver recipients insteadof adjusting the BAR score? Do you think you can have the sameresults? This would be really practical as it could allow one to allocatethe graft within some minutes at midnight. This could have been aneasy way to use the score in daily clinical practice.

My third question relates to the factor re-transplantation. Whydo you include in all your scores the re-transplantation? I don’t un-derstand this because re-transplantation is not only a very differentoperation but, moreover, a re-transplant done at day 1 or 2 is com-pletely different from a re-transplant done after 6 months. Do you

think if you take the factor of re-transplantation out of the BARscore that you could even strengthen this score in relation to primarytransplant patients?

In conclusion, I congratulate you for your very important work.I think it is really another big step forward to, what I always call, thedonor model for end-stage liver disease and the recipient model forend-stage liver disease equation, an equation that is essential to makethis intervention economically justifiable.

Response from P. Dutkowski (Zurich, Switzerland)Thank you very much Professor Lerut for these comments and

your questions. I may start with the last question regarding the variablere-transplantation. On the basis of the logistic regression model, thisis the strongest factor of all 6 parameters contributing to the BARscore. Taking out re-transplantation is therefore not recommendedand would decrease the strength of the model.

Your second question refers to the lack of the variable lifesupport in the ELTR database. You suggest replacing life supportby the United Network for Organ Sharing status, which is avail-able in the ELTR and United Network for Organ Sharing data.However, in previous analysis, we analyzed United Network forOrgan Sharing status in comparison with life support and foundUnited Network for Organ Sharing status a rather weak parame-ter. For example, a 6-variable model, including the parameter ofthe United Network for Organ Sharing status was less predictivethan a 5-variable model without life support or the United Net-work for Organ Sharing status. Therefore, for further testing of theBAR score in the ELTR we decided to use its 5-variable adjustedversion.

Your first question was on the lack of standardization in assess-ment of liver biopsies. I agree with this criticism but would like tomention 3 important points. First, previous work from our group hasshown indeed inconsistent assessment of hepatic steatosis among ex-pert pathologists but the highest variation was documented for mildsteatosis, which is unlikely to impact survival. Second, we presenthere the largest data set on recorded liver biopsies currently available,with separate analysis for the type of steatosis. Third, testing of BARin relation to hepatic fat in our population showed similar results.All together, we believe that these arguments favor the validity of thepresented data.

DISCUSSANTSM. Krawczyk (Warsaw, Poland):

I appreciate your study very much but for you it is importantto bear in mind that we have a recipient which is in a very difficultsituation and sometimes it is necessary to accept a liver that is farfrom perfect. So, in this situation, the score is important but if youhave only one liver and the recipient is in a critical status, you willhave to decide to do the operation. In our department, we transplanted140 livers last year alone—a considerable number. And in 30% ofcases, a fatty liver was accepted because our patients were in a criticalsituation. So, the score is important but the recipient is more importantthan the score.

Response from P. Dutkowski (Zurich, Switzerland):Thank you very much, Professor Krawczyk, for your com-

ments. I would like to point out that our study shows how importantthe type of steatosis would be for the decision of whether or not to




accept a graft for a high-risk recipient. For example, more than 30%macrosteatosis would clearly impact graft survival in sick recipients,however, that will not be the case in more than 30% microsteatosis. Inaddition, even implantation of high-grade macrosteatotic grafts willresult in good outcomes, if the sum of risk factors is low, for example,

at a BAR score of less than or equal to 9. Certainly, it depends also onyour waiting list, whether or not to accept 5-year survival rates of lessthan 60%. However, in the era of scarce organ supply, most centersare forced to set cutoffs to say no to certain risk combinations. Thankyou very much again.



The Use of Fatty Liver Grafts in Modern Allocation Systems

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