Self-Organizing Maps Can Determine Outcome and Match Recipients and Donors at Orthotopic Liver Transplantation

Should AI allocate livers for transplant? Public attitudes and ethical considerations

BACKGROUND

Allocation of scarce organs for transplantation is ethically challenging. Artificial intelligence (AI) has been proposed to assist in liver allocation, however the ethics of this remains unexplored and the view of the public unknown. The aim of this paper was to assess public attitudes on whether AI should be used in liver allocation and how it should be implemented.

METHODS

We first introduce some potential ethical issues concerning AI in liver allocation, before analysing a pilot survey including online responses from 172 UK laypeople, recruited through Prolific Academic.

FINDINGS

Most participants found AI in liver allocation acceptable (69.2%) and would not be less likely to donate their organs if AI was used in allocation (72.7%). Respondents thought AI was more likely to be consistent and less biased compared to humans, although were concerned about the "dehumanisation of healthcare" and whether AI could consider important nuances in allocation decisions. Participants valued accuracy, impartiality, and consistency in a decision-maker, more than interpretability and empathy. Respondents were split on whether AI should be trained on previous decisions or programmed with specific objectives. Whether allocation decisions were made by transplant committee or AI, participants valued consideration of urgency, survival likelihood, life years gained, age, future medication compliance, quality of life, future alcohol use and past alcohol use. On the other hand, the majority thought the following factors were not relevant to prioritisation: past crime, future crime, future societal contribution, social disadvantage, and gender.

CONCLUSIONS

There are good reasons to use AI in liver allocation, and our sample of participants appeared to support its use. If confirmed, this support would give democratic legitimacy to the use of AI in this context and reduce the risk that donation rates could be affected negatively. Our findings on specific ethical concerns also identify potential expectations and reservations laypeople have regarding AI in this area, which can inform how AI in liver allocation could be best implemented.

Machine learning in liver transplantation: a tool for some unsolved questions?

Machine learning has recently been proposed as a useful tool in many fields of Medicine, with the aim of increasing diagnostic and prognostic accuracy. Models based on machine learning have been introduced in the setting of solid organ transplantation too, where prognosis depends on a complex, multidimensional and nonlinear relationship between variables pertaining to the donor, the recipient and the surgical procedure. In the setting of liver transplantation, machine learning models have been developed to predict pretransplant survival in patients with cirrhosis, to assess the best donor-to-recipient match during allocation processes, and to foresee postoperative complications and outcomes. This is a narrative review on the role of machine learning in the field of liver transplantation, highlighting strengths and pitfalls, and future perspectives.

Artificial neural network and bioavailability of the immunosuppression drug

PURPOSE OF REVIEW

The success of organ transplant is determined by number of demographic, clinical, immunological and genetic variables. Artificial intelligence tools, such as artificial neural networks (ANNs) or classification and regression trees (CART) can handle multiple independent variables and predict the dependent variables by deducing the complex nonlinear relationships between variables.

RECENT FINDINGS

In the last two decades, several researchers employed these tools to identify donor-recipient matching pairs, to optimize immunosuppressant doses, to predict allograft survival and to minimize adverse drug reactions. These models showed better performance characteristics than the empirical dosing strategies in terms of sensitivity, specificity, overall accuracy, or area under the curve of receiver-operating characteristic curves. The performance of the models was dependent directly on the input variables. Recent studies identified protein biomarkers and pharmacogenetic determinants of immunosuppressants as additional variables that increase the precision in prediction. Accessibility of medical records, proper follow-up of transplant cases, deep understanding of pharmacokinetic and pharmacodynamic pathways of immunosuppressant drugs coupled with genomic and proteomic markers are essential in developing an effective artificial intelligence platform for transplantation.

SUMMARY

Artificial intelligence has a greater clinical utility both in pretransplantation and posttransplantation periods to get favourable clinical outcomes, thus ensuring successful graft survival.

Using Artificial Intelligence for Predicting Survival of Individual Grafts in Liver Transplantation: A Systematic Review

The demand for liver transplantation far outstrips the supply of deceased donor organs, and so, listing and allocation decisions aim to maximize utility. Most existing methods for predicting transplant outcomes use basic methods, such as regression modeling, but newer artificial intelligence (AI) techniques have the potential to improve predictive accuracy. The aim was to perform a systematic review of studies predicting graft outcomes following deceased donor liver transplantation using AI techniques and to compare these findings to linear regression and standard predictive modeling: donor risk index (DRI), Model for End-Stage Liver Disease (MELD), and Survival Outcome Following Liver Transplantation (SOFT). After reviewing available article databases, a total of 52 articles were reviewed for inclusion. Of these articles, 9 met the inclusion criteria, which reported outcomes from 18,771 liver transplants. Artificial neural networks (ANNs) were the most commonly used methodology, being reported in 7 studies. Only 2 studies directly compared machine learning (ML) techniques to liver scoring modalities (i.e., DRI, SOFT, and balance of risk [BAR]). Both studies showed better prediction of individual organ survival with the optimal ANN model, reporting an area under the receiver operating characteristic curve (AUROC) 0.82 compared with BAR (0.62) and SOFT (0.57), and the other ANN model gave an AUC ROC of 0.84 compared with a DRI (0.68) and SOFT (0.64). AI techniques can provide high accuracy in predicting graft survival based on donors and recipient variables. When compared with the standard techniques, AI methods are dynamic and are able to be trained and validated within every population. However, the high accuracy of AI may come at a cost of losing explainability (to patients and clinicians) on how the technology works.

Use of artificial intelligence as an innovative donor-recipient matching model for liver transplantation: results from a multicenter Spanish study

BACKGROUND & AIMS

There is an increasing discrepancy between the number of potential liver graft recipients and the number of organs available. Organ allocation should follow the concept of benefit of survival, avoiding human-innate subjectivity. The aim of this study is to use artificial-neural-networks (ANNs) for donor-recipient (D-R) matching in liver transplantation (LT) and to compare its accuracy with validated scores (MELD, D-MELD, DRI, P-SOFT, SOFT, and BAR) of graft survival.

METHODS

64 donor and recipient variables from a set of 1003 LTs from a multicenter study including 11 Spanish centres were included. For each D-R pair, common statistics (simple and multiple regression models) and ANN formulae for two non-complementary probability-models of 3-month graft-survival and -loss were calculated: a positive-survival (NN-CCR) and a negative-loss (NN-MS) model. The NN models were obtained by using the Neural Net Evolutionary Programming (NNEP) algorithm. Additionally, receiver-operating-curves (ROC) were performed to validate ANNs against other scores.

RESULTS

Optimal results for NN-CCR and NN-MS models were obtained, with the best performance in predicting the probability of graft-survival (90.79%) and -loss (71.42%) for each D-R pair, significantly improving results from multiple regressions. ROC curves for 3-months graft-survival and -loss predictions were significantly more accurate for ANN than for other scores in both NN-CCR (AUROC-ANN=0.80 vs. -MELD=0.50; -D-MELD=0.54; -P-SOFT=0.54; -SOFT=0.55; -BAR=0.67 and -DRI=0.42) and NN-MS (AUROC-ANN=0.82 vs. -MELD=0.41; -D-MELD=0.47; -P-SOFT=0.43; -SOFT=0.57, -BAR=0.61 and -DRI=0.48).

CONCLUSIONS

ANNs may be considered a powerful decision-making technology for this dataset, optimizing the principles of justice, efficiency and equity. This may be a useful tool for predicting the 3-month outcome and a potential research area for future D-R matching models.

Impact of donor and recipient race on survival after hepatitis C-related liver transplantation

BACKGROUND

Both donor and recipient race impact outcomes after liver transplantation (LT), especially for hepatitis C virus (HCV). The interaction and simultaneous impact of both on patient survival is not clearly defined. The purpose of this study was to examine the impact of donor and recipient race on recipient and graft survival after HCV-related LT using the United Network for Organ Sharing database.

METHODS

A total of 16,053 recipients (75.5% white, 9.3% black, and 15.2% Hispanic) who underwent primary LT for HCV between 1998 and 2008 were included. Cox regression models were used to assess the association between recipient/donor race and patient survival.

RESULTS

A significant interaction between donor and recipient race was noted (P=0.01). Black recipients with white donors had a higher risk of patient mortality (adjusted hazard ratio, 1.66; 95% confidence interval, 1.47-1.87) compared with that of white recipients with white donors. In contrast, the pairing of Hispanic recipients with black donors was associated with a lower risk of recipient mortality compared with that of white recipients with white donors (adjusted hazard ratio, 0.64; 95% confidence interval, 0.46-0.87). Similar results were noted for graft failure.

CONCLUSION

In conclusion, the impact of donor and recipient race on patient survival varies substantially by the matching of recipient/donor race.

Algorithm to determine the outcome of patients with acute liver failure: a data-mining analysis using decision trees

BACKGROUND

We established algorithms to predict the prognosis of acute liver failure (ALF) patients through a data-mining analysis, in order to improve the indication criteria for liver transplantation.

METHODS

The subjects were 1,022 ALF patients seen between 1998 and 2007 and enrolled in a nationwide survey. Patients older than 65 years, and those who had undergone liver transplantation and received blood products before the onset of hepatic encephalopathy were excluded. Two data sets were used: patients seen between 1998 and 2003 (n=698), whose data were used for the formation of the algorithm, and those seen between 2004 and 2007 (n=324), whose data were used for the validation of the algorithm. Data on a total of 73 items, at the onset of encephalopathy and 5 days later, were collected from 371 of the 698 patients seen between 1998 and 2003, and their outcome was analyzed to establish decision trees. The obtained algorithm was validated using the data of 160 of the 324 patients seen between 2004 and 2007.

RESULTS

The outcome of the patients at the onset of encephalopathy was predicted through 5 items, and the patients were classified into 6 categories with mortality rates between 23% and89%. When the prognosis of the patients in the categories with mortality rates greater than 50% was predicted as "death", the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the algorithm were 79, 78, 81, 83, and 75%, respectively. Similar high values were obtained when the algorithm was employed in the patients for validation. The outcome of the patients 5 days after the onset of encephalopathy was predicted through 7 items, and a similar high accuracy was found for both sets of patients.

CONCLUSIONS

Novel algorithms for predicting the outcome of ALF patients may be useful to determine the indication for liver transplantation.

Novel classification of acute liver failure through clustering using a self-organizing map: usefulness for prediction of the outcome

BACKGROUND

Patients with acute liver failure are classified according to the interval between the onset of hepatitis symptoms and the development of hepatic encephalopathy. We examined the validity of such classifications.

METHODS

The subjects were 1,022 patients enrolled in a nationwide survey in Japan. The intervals between the onset of the hepatitis symptoms and the development of encephalopathy were 10 days or less in 472 patients (group-A), between 11 and 56 days in 468 patients (group-B), and longer than 56 days in 82 patients (group-C). Data on a total of 104 items collected from the patients were subjected to clustering using a self-organizing map.

RESULTS

The patients were classified into three clusters. The first cluster consisted of 411 patients (group-A: 57%, group-B: 39%, group-C: 4%). Their incidence of complications was low; 34% underwent liver transplantation (LT), and their survival rate was 90%, while 94% of those treated without transplant were rescued. The second cluster consisted of 320 patients (21, 65, and 14% groups A, B, and C, respectively), who showed a high incidence of complications; the survival rate was 7% in the patients treated conservatively without LT. Sixteen percent underwent LT and survival rate of these patients was 52%. There was a third cluster, of 291 patients (59, 34, and 7% groups A, B, and C, respectively). Without LT, 81% of the patients died. Seven percent were treated by LT and their survival rate was 60%.

CONCLUSIONS

Clustering revealed that patients with acute liver failure could be classified into three clusters independent of the interval between the onset of disease symptoms and the development of encephalopathy. This technique may be useful, since the outcomes of the patients differed markedly among the clusters.

The biopsied donor liver: incorporating macrosteatosis into high-risk donor assessment

To expand the donor liver pool, ways are sought to better define the limits of marginally transplantable organs. The Donor Risk Index (DRI) lists 7 donor characteristics, together with cold ischemia time and location of the donor, as risk factors for graft failure. We hypothesized that donor hepatic steatosis is an additional independent risk factor. We analyzed the Scientific Registry of Transplant Recipients for all adult liver transplants performed from October 1, 2003, through February 6, 2008, with grafts from deceased donors to identify donor characteristics and procurement logistics parameters predictive of decreased graft survival. A proportional hazard model of donor variables, including percent steatosis from higher-risk donors, was created with graft survival as the primary outcome. Of 21,777 transplants, 5051 donors had percent macrovesicular steatosis recorded on donor liver biopsy. Compared to the 16,726 donors with no recorded liver biopsy, the donors with biopsied livers had a higher DRI, were older and more obese, and a higher percentage died from anoxia or stroke than from head trauma. The donors whose livers were biopsied became our study group. Factors most strongly associated with graft failure at 1 year after transplantation with livers from this high-risk donor group were donor age, donor liver macrovesicular steatosis, cold ischemia time, and donation after cardiac death status. In conclusion, in a high-risk donor group, macrovesicular steatosis is an independent risk factor for graft survival, along with other factors of the DRI including donor age, donor race, donation after cardiac death status, and cold ischemia time.

Survival benefit after liver transplantation: a single European center experience

BACKGROUND

The evaluation of the survival achieved with liver transplantation (LT) compared with remaining on the waiting list, the transplant benefit, should be the underlying principle of organ allocation.

METHODS

During 2004 to 2007 with an allocation system based on Model for End-Stage Liver Disease (MELD) score with exceptions, we prospectively evaluated the transplant benefit and its relation to the match between recipient and donor characteristics.

RESULTS

Among 575 patients listed for chronic liver disease, 218 (37.9%) underwent LT and 115 (20%) were removed from the list (76 deaths, 25 tumor progressions, and 14 sick conditions). The 1- and 3-year survival rates on the list were significantly related to MELD score more than or equal to 20 (57% and 33% vs. 88% and 66%, P<0.001) and to its progression during the waiting time, such as s-Na levels less than or equal to 135 mEq/L (73% and 48% vs. 86% and 69%, P<0.001). These two variables had no impact on survival after LT, except in hepatitis C virus positive recipients. The multivariate Cox model confirmed a positive transplant benefit for all cases with MELD score more than or equal to 20 and without hepatocellular carcinoma (HR 2.9; CI 1.3-6.2) independently of the type of donors. Only hepatocellular carcinoma patients with low MELD scores showed a positive transplant benefit (MELD <15; HR 2; CI 1.1-5.1).

CONCLUSIONS

LT should be reserved for cirrhotic patients with MELD score more than or equal to 20 independently of other recipient and donor matches or for cases with lower MELD score but with hepatocellular carcinoma.

Natural history of post-liver transplantation hepatitis C: A review of factors that may influence its course

Our aim was to assess long-term survival in patients transplanted for HCV-related end-stage liver disease (ESLD) and evaluate potentially modifiable predictors of survival. We performed a retrospective analysis of adult liver transplants (LT) at our institution for HCV-related ESLD since the program's inception. Pertinent demographic, clinical, and biochemical information was retrieved from electronic medical records and histological data from 990 per-protocol liver biopsies were collected. Three hundred eighty LT were performed at our institution during the study period, 206 patients were transplanted for HCV-related ESLD; 6 died within 30 days of transplantation and were not included. The remaining 200 recipients (DDLT 168 LDLT 32) constituted the evaluable population. The demographics were as follows: 150 males, median age 53 years; median donor age 39 years; hepatocellular carcinoma (HCC) in 26%. Overall 1-, 5-, and 7-year survival: 95%, 81%, and 79%; median survival 43 months, mortality 15%. Significant HCV recurrence (HAI >or=6 and/or fibrosis >or=2) was present in 49%, "early recurrence" (within 1 year of LT) in 30.5% and biopsy-proven acute rejection was present in 27%. Factors with a significant negative impact on patient survival included: fibrosis stage >or=2 at 12-month biopsy, advanced donor age, history of HCC and early acute rejection. Survival was similar regardless of the donor type (DDLT vs. LDLT). Early and aggressive HCV recurrence has a very heavy toll on patient survival. Prompt recognition and treatment of "rapid fibrosers" may impart benefit. As has been described before, avoidance of rejection and selection of young donors for HCV-positive recipients will also improve survival in this population. On the basis of our findings, LDLT is a good option for HCV-positive recipients.

Liver transplantation: from Child to MELD

The widespread availability of transplantation in most major medical centers in the United States, together with a growing number of transplant candidates, has made it necessary for primary care providers, especially internal medicine and family practice physicians to be active in the clinical care of these patients before and after transplantation. This review provides an overview of the liver transplantation process, including indications, contraindications, time of referral to a transplant center, the current organ allocation system, and briefly touches on the expanding field of living donor liver transplantation.

Cost prediction in liver transplantation using pretransplant donor and recipient characteristics

BACKGROUND

Liver transplantation is a costly procedure and its cost is likely driven by both donor and recipient factors. Recently, the recipient model for end-stage liver disease (MELD) score has been correlated with increased posttransplant cost; however, other factors have not been identified. We sought to identify if other donor and recipient factors are associated with increased cost.

METHODS

One hundred sixty-six liver transplants performed at our center from January 2004 through February 2006 were included in the estimation sample, and the subsequent 75 transplants were used as a validation cohort. To determine whether donor factors influenced cost, two latent class linear regression models were created from the estimation sample: one considering only recipient variables (model A) and a second incorporating both donor and recipient factors (model B). The resultant models were then validated in the second group of patients and compared with the best single-segment linear regression models.

RESULTS

Model A predictors include pretransplant intensive care unit (ICU) stay, age x body mass index, and calculated MELD. In model B, significant predictors are calculated MELD, age, age x pretransplant ICU stay, and donor age more than 40 as significant variables. In validation, only model A remained predictive of cost.

CONCLUSIONS

Although marginal donor factors are recognized to influence clinical outcome, they did not factor significantly in cost modeling. In addition to MELD, the recipient factors of pretransplant ICU stay, age, and body mass index are pretransplant variables correlated mostly with posttransplant cost across broad populations.

1H NMR metabonomics of plasma lipoprotein subclasses: elucidation of metabolic clustering by self-organising maps

(1)H NMR spectra of plasma are known to provide specific information on lipoprotein subclasses in the form of complex overlapping resonances. A combination of (1)H NMR and self-organising map (SOM) analysis was applied to investigate if automated characterisation of subclass-related metabolic interactions can be achieved. To reliably assess the intrinsic capability of (1)H NMR for resolving lipoprotein subclass profiles, sum spectra representing the pure lipoprotein subclass part of actual plasma were simulated with the aid of experimentally derived model signals for 11 distinct lipoprotein subclasses. Two biochemically characteristic categories of spectra, representing normolipidaemic and metabolic syndrome status, were generated with corresponding lipoprotein subclass profiles. A set of spectra representing a metabolic pathway between the two categories was also generated. The SOM analysis, based solely on the aliphatic resonances of these simulated spectra, clearly revealed the lipoprotein subclass profiles and their changes. Comparable SOM analysis in a group of 69 experimental (1)H NMR spectra of serum samples, which according to biochemical analyses represented a wide range of lipoprotein lipid concentrations, corroborated the findings based on the simulated data. Interestingly, the choline-N(CH(3))(3) region seems to provide more resolved clustering of lipoprotein subclasses in the SOM analyses than the methyl-CH(3) region commonly used for subclass quantification. The results illustrate the inherent suitability of (1)H NMR metabonomics for automated studies of lipoprotein subclass-related metabolism and demonstrate the power of SOM analysis in an extensive and representative case of (1)H NMR metabonomics.

Liver transplantation: an update 2007

PURPOSE OF REVIEW

Recent attention in liver transplantation has focused on equity in organ allocation and management of posttransplant complications.

RECENT FINDINGS

Adoption of the model for end-stage liver disease for liver allocation has been successful in implementing a system based on medical urgency rather than waiting time. Refinements are being studied in improving the prediction of mortality and improving transplant benefit by balancing pretransplant mortality and posttransplant survival. Emerging literature is examining expansion of the current criteria for transplantation of hepatocellular carcinoma and the role of neoadjuvant therapy. Chronic renal dysfunction after liver transplantation is a source of considerable morbidity. Nephron-sparing immunosuppression regimens are emerging with encouraging results. Hepatitis C virus infection is difficult to differentiate histologically from rejection, although newer markers are being developed. Antiviral and immunosuppressive strategies for reducing the severity of hepatitis C virus recurrence are discussed. Alcohol relapse is common after liver transplant in alcoholic liver disease patients and can lead to worse outcomes.

SUMMARY

Organ allocation tends to evolve under the model for end-stage liver disease with a focus on maximizing transplant benefit. Hepatitis C virus, hepatocellular carcinoma, chronic renal dysfunction and alcohol relapse are major challenges, and continued research in these areas will undoubtedly lead to better outcomes for transplant recipients.

Allocation of nonstandard livers to transplant candidates with high MELD scores: Should this practice be continued?

MELD and PELD scores of 255 consecutive grafts were calculated (236 adult cases and 19 pediatric cases). No correction for the etiology of liver disease was performed. Retransplants were excluded. Three categories of patients were identified: low MELD (scores <12, n = 61); intermediate MELD (scores between 12-24, n = 159); high MELD (scores > or =25, n = 35). Grafts were categorized according to donor quality: standard livers (n = 199), vs nonstandard livers (n = 56). Nonstandard livers were identified by age > or =60, or at least by two of the following conditions: severe hemodynamic instability, ultrasound evidence of steatosis, natriemia > or =155 mEq/L, ICU stay >7 days, liver trauma, protracted anoxia as cause of brain death, transaminases levels x 4. In standard livers, the 12-month graft survival (GS) for low, intermediate, and high MELD classes were 88%, 74%, and 77%, respectively. In nonstandard livers, the 12-month GS for the low, intermediate, and high MELD classes were 84%, 55%, and 44%, respectively; differences between low MELD class and both intermediate and high MELD classes were significant (P < .05). Cox regression analysis of all cases identified the following parameters as independent predictors of GS: donor status; donor age; and recipient creatinine. The highest correlation with GS was found using donor age and recipient creatinine as covariates. In standard livers no variable was able to predict GS. In nonstandard livers the MELD-PELD score was the unique variable able to predict GS. We suggest avoiding the use of nonstandard livers for patients with high MELD scores.

Developing a prognostic model for 90-day mortality after liver transplantation based on pretransplant recipient factors

BACKGROUND

Current statistical prognostic models for mortality after liver transplantation do not have good discriminatory ability. Furthermore, the methodology used to develop these models is often flawed. The objective of this paper is to develop a prognostic model for 90-day mortality after liver transplantation based on pretransplant recipient factors, employing a rigorous model development method.

METHODS

We used data on 4,829 patient that were prospectively collected for the UK & Ireland Liver Transplant Audit. Switching regression was employed to impute missing values combined with a bootstrapping approach for variable selection.

RESULTS

In all, 452 patients (9.4%) died within 90 days of their transplantation. The final prognostic model was well calibrated and discriminated moderately well between patients who did and who did not die (c-statistic 0.65, 95% CI [0.63, 0.68]). Although discrimination was not excellent overall, the results showed that those patients with a "low" chance of dying within 90 days of their transplant and those with a "high" chance of dying could be differentiated from patients with a "intermediate" chance.

CONCLUSIONS

Our model can provide transplant candidates with predictions of their early posttransplantation prospects before any donor information is known, which is essential information for patients with end-stage liver disease for whom liver transplantation is a treatment option.

A systematic review of the performance of the model for end-stage liver disease (MELD) in the setting of liver transplantation

The Model for End-Stage Liver Disease (MELD) score is now used for allocation in liver transplantation (LT) waiting lists, replacing the Child-Turcotte-Pugh (CTP) score. However, there is debate as whether it is superior to CTP score to predict mortality in patients with cirrhosis on the LT waiting list and after LT. We reviewed studies comparing the accuracy of MELD vs. CTP score in transplantation settings. We found that in studies of the LT waiting list (12,532 patients with cirrhosis), only 4 of 11 showed MELD to be superior to CTP in predicting short-term (3-month) mortality. In addition, 2 of 3 studies (n = 1,679) evaluating the changes in MELD score (DeltaMELD) showed that DeltaMELD had better prediction for mortality than the baseline MELD score. The impact of MELD on post-LT mortality was assessed in 15 studies (20,456 patients); only 6 (9,522 patients) evaluated the discriminative ability of MELD score using the concordance (c) statistic (the MELD score had always a c-statistic < 0.70). In 11 studies (19,311 patients), high MELD score indicated poor post-LT mortality for cutoff values of 24-40 points. In re-LT patients, 2 of 4 studies evaluated the discriminative ability of MELD score on post-LT mortality. Finally, several studies have shown that the predictive ability of MELD score increases by adding clinical variables (hepatic encephalopathy, ascites) or laboratory (sodium) parameters. On the basis of the current literature, MELD score does not perform better than the CTP score for patients with cirrhosis on the waiting list and cannot predict post-LT mortality.

The Nonstandard Liver, a Hidden Resource That Cannot Be Overlooked: Implications for the Identification of the Best Recipient

We described the characteristics of livers already labeled as marginal, nonstandard, or selected with extended criteria: donors of elderly age, steatosis, hemodynamic instability, long cold ischemia time, high serum Na, HbcAb-positive status, HCVAb-positive status. Recipients characteristics (gender, UNOS status, MELD score, indication for transplantation) and their best possible match to nonstandard donors were evaluated with a report of the recent guidelines and the specific algorithms to optimize recipient identification.

Liver allocation for hepatocellular carcinoma: a European Center policy in the pre-MELD era

BACKGROUND

Policies to decrease dropout during waiting time for liver transplantation (LT) are under debate.

METHODS

We evaluated the allocation system from 1996 to 2003, when recipients had priority related to Child-Pugh score and donors >60 years were mainly offered to recipients with hepatocellular carcinoma (HCC). The outcomes of 656 patients with chronic liver disease (142 HCC and 514 non-HCC) listed for LT were prospectively evaluated, considering recipient and donor features.

RESULTS

Transplantation and dropout rates were similar between HCC and non-HCC patients: 64.1% vs. 70.6% and 26% vs. 22.6%. Multivariate analysis showed the probability of being transplanted within 3 months was related to Child-Pugh score >10 and to HCC, whereas the probability of being removed from the list within 3 months was only related to Child-Pugh score >10. HCC patients had a lower median waiting time (97 vs. 197 days, P<0.001), a higher rate of donors > 60 years (50.5% vs. 33.5%, P<0.005) and with steatosis (31.6% vs. 14.3%, P<0.01), but a lower Child-Pugh score (9.1+/-2.1 vs. 9.6+/-1.7, P<0.05) than non-HCC patients. The 5-year patient survival was comparable since registration on the list and since LT: 56.9% and 77% in the HCC group vs. 61.4% and 79% in the non-HCC patients. Donors > 60 years affected outcome after LT in the non-HCC group, but not in the HCC patients.

CONCLUSION

By allocating donors >60 years mainly to HCC patients, we controlled dropout without affecting their survival and the outcome of non-HCC patients.