Association of preoperative parameters with postoperative mortality and long-term survival after liver transplantation
======================================================================================================================

* Dionisios Vrochides
* Mazzen Hassanain
* Jeffrey Barkun
* Jean Tchervenkov
* Steven Paraskevas
* Prosanto Chaudhury
* Marcelo Cantarovich
* Marc Deschenes
* Phil Wong
* Peter Ghali
* Gabriel Chan
* Peter Metrakos

## Abstract

**Background:** The ability of Child–Turcotte–Pugh (CTP) or Model for End-Stage Liver Disease (MELD) scores to predict recipient survival after liver transplantation is controversial. This analysis aims to identify preoperative parameters that might be associated with early postoperative mortality and long-term survival after liver transplantation.

**Methods:** We studied a total of 15 parameters, using both univariate and multivariate models, among adults who underwent primary liver transplantation.

**Results:** A total of 458 primary adult liver transplants were performed. Fifty-seven (12.44%) patients died during the first 3 postoperative months and composed the early mortality group. The remaining 401 patients composed the long-term patient survival group. The parameters that were identified through univariate analysis to be associated with early postoperative mortality were CTP score, MELD score, bilirubin, creatinine, international normalized ratio and warm ischemia time (WIT). In all multivariate models, WIT retained its statistical significance. The 10-year long-term survival was 65%. The parameters that were identified to be independent predictors of long-term survival were the recipient’s sex (improved survival in women, *p* = 0.005), diagnosis of hepatocellular cancer (*p* = 0.015) and recipient’s age (*p* = 0.024).

**Conclusion:** Either CTP or MELD score, in conjunction with WIT, might have a role in predicting early postoperative mortality after liver transplantation, whereas the recipient’s sex and the absence of hepatocellular cancer are associated with improved long-term survival.

The Model for End-Stage Liver Disease (MELD) score is now used for allocation in liver transplantation waiting lists, replacing the Child–Turcotte–Pugh (CTP) score. The MELD score is primarily a “justice” system (i.e., organs from deceased donors are allocated to the sickest patients first).1 However, implementation of the MELD score has raised the issue of utility of the donated liver grafts.2 According to the “utility” concept (i.e., organs are allocated preferentially to the lowest-risk candidates), an ideal liver allocation model should not only identify the patient with the highest probability of dying on the waiting list, but also predict early postoperative mortality and long-term survival. Early postoperative mortality usually is associated with technical failures, high-risk recipients and poor-quality grafts. On the other hand, long-term survival is compromised by recurrence of primary disease, opportunistic infections and development of cancer.3 Because of this difference in causative factors, prediction of early and late mortality after liver transplantation might be associated with different preoperative parameters.

Most of the scores assessing the severity of cirrhosis, including CTP and MELD scores, have also been used to predict early mortality after liver transplantation,4 but usually without success. For example, the MELD score was unable to predict early (90-d) postoperative mortality in almost all of the relevant studies.5–7 To increase their accuracy, the most recent models for the prediction of early postoperative mortality have included the MELD score and many other variables, such as serum sodium,8 donor quality9 or serum cholinesterase.10

Prediction of long-term survival alone after liver transplantation is less well studied. Most of the relevant studies either utilize the MELD score or derive from a hepatitis C virus (HCV) patient population. Some of the former studies suggest that the MELD score can accurately predict late posttransplant mortality,11 whereas some others do not.12 On the other hand, analyses based on HCV population incorporate many other variables, such as donor’s sex and recipient’s age, in the predictive model.13 This study aims to identify preoperative parameters that might be associated with early postoperative mortality and long-term survival after liver transplantation.

## Methods

We performed a single-centre, retrospective study of prospectively collected data. We reviewed the cases of adults who underwent primary liver transplantation at the Royal Victoria Hospital in Montréal, Que., between 1990 and 2006. Those receiving retransplants and multi-organ recipients were excluded. Patients who died during the first 3 postoperative months composed the early mortality group. The remaining patients composed the long-term survival group.

We studied a total of 15 parameters:

*   recipient’s sex

*   cause of cirrhosis

*   preoperative diagnosis of hepatocellular cancer (HCC)

*   recipient’s age

*   CTP score at the time of transplantation

*   MELD score at the time of transplantation

*   preoperative total bilirubin

*   preoperative creatinine

*   preoperative international normalized ration (INR)

*   preoperative albumin

*   donor’s cause of death

*   donor’s sex

*   donor’s age

*   cold ischemia time (CIT)

*   warm ischemia time (WIT)

### Statistical analysis

We evaluated all 15 parameters individually for their impact either on actual early mortality (univariate binary logistic regression analysis) or on actuarial long-term patient survival (univariate Cox regression analysis). The parameters that showed statistical significance were incorporated into models for the study of the actual early mortality (multivariate binary logistic regression analysis) and of the actuarial long-term patient survival (multivariate Cox regression analysis). To avoid collinearity, CTP and MELD scores were never used together with their components (bilirubin, creatinine, INR) in the development of multivariate models. We constructed receiver operating characteristic (ROC) curves for all variables that were shown in the multivariate models to have an impact on actual posttransplant early mortality. Statistical analysis was performed with SPSS 16.0 for Mac (SPSS Inc.).

## Results

Between 1990 and 2006, 458 primary adult liver transplants were performed. Fifty-seven (12.44%) patients died during the first 3 postoperative months and composed the early mortality group. The remaining 401 patients composed the long-term patient survival group, with a median follow-up of 4.82 years. Of these 401 recipients, 71 (17%) were followed up for more than 10 years, whereas 13 (3.2%) were lost to follow-up.

Of the 103 recipients in whom HCC was diagnosed preoperatively, 47 (45.6%) exceeded the Milan criteria. The categorical parameters that describe this cohort of patients are presented as absolute numbers and percentages (Table 1). The continuous parameters are presented as median values and ranges (Table 2).

View this table:
[Table 1](http://canjsurg.ca/content/54/2/101/T1)

Table 1 
Categorical parameters associated with early postoperative mortality (< 90 days) after liver transplantation in a cohort of 458 patients*

View this table:
[Table 2](http://canjsurg.ca/content/54/2/101/T2)

Table 2 
Continuous parameters associated with early postoperative mortality (< 90 days) after liver transplantation in a cohort of 458 patients*

Early posttransplant mortality was 12.44%. The incidence of postoperative mortality correlated positively with CTP and MELD scores at the time of transplantation. When studied individually, all components of the MELD score (recipient’s bilirubin, recipient’s creatinine and recipient’s INR) also correlated positively with early posttransplant mortality (Table 2). Of the remaining parameters (Table 1, Table 2), only WIT was identified by the univariate analysis to be associated with early postoperative mortality. Based on these observations, 2 multivariate models were built (Table 3). In the first one, both the CTP score and WIT had a statistically significant effect on post-transplant mortality. In the second model, both the MELD score and WIT retained their statistical significance. Construction of ROC curves revealed that a CTP score of 12 had a sensitivity of 36% and a specificity of 80% for predicting early mortality (c-statistic 0.633, 95% confidence interval [CI] 0.559–0.708, *p* = 0.001). In addition, a MELD score of 28 had a sensitivity of 45% and a specificity of 81% for predicting early mortality (c-statistic 0.648, 95% CI 0.565–0.730, *p* < 0.001). Finally, a WIT of 1.3 hours had a sensitivity of 20% and a specificity of 81% for predicting early mortality (c-statistic 0.614, 95% CI 0.528–0.699, *p* = 0.010). The aforementioned ROC curves are depicted in Figure 1.

![Fig. 1](http://canjsurg.ca/https://www.canjsurg.ca/content/cjs/54/2/101/F1.medium.gif)

[Fig. 1](http://canjsurg.ca/content/54/2/101/F1)

Fig. 1 
Most liver transplant recipients who died within the first 90 posttransplant days did not have a significantly longer warm ischemia time (WIT). However, the specificity of a WIT that exceeded 78 minutes (1.3 h) for predicting early postoperative mortality was 81%. MELD = Model for End-Stage Liver Disease; CTP = Child–Turcotte–Pugh.

View this table:
[Table 3](http://canjsurg.ca/content/54/2/101/T3)

Table 3 
Multivariate analysis models for the prediction of actual early mortality (2 models) and actuarial long-term survival (1 model) after liver transplantation

The 10-year long-term actuarial survival was 65%. The parameters that were identified by the univariate analysis to be associated with improved long-term survival were recipient’s sex (female), diagnosis of either primary biliary cirrhosis or primary sclerosing cholangitis and recipient’s age. On the other hand, diagnosis of HCV cirrhosis and preoperative diagnosis of HCC correlated inversely with long-term survival (Table 4, Table 5). The multivariate model that was built based on these observations (Table 3) revealed that recipient’s sex (female) and recipient’s age correlated positively with long-term survival, whereas presence of HCC had a negative impact. Interestingly, in the multivariate model, primary diagnosis lost its statistical significance. The impact of recipient’s sex on long-term survival is depicted in Figure 2.

![Fig. 2](http://canjsurg.ca/https://www.canjsurg.ca/content/cjs/54/2/101/F2.medium.gif)

[Fig. 2](http://canjsurg.ca/content/54/2/101/F2)

Fig. 2 
Patient long-term survival after liver transplantation, stratified by recipient’s sex, adjusted for primary diagnosis, presence of hepatocellular cancer and recipient’s age. Early posttransplant deaths (*n* = 57), mainly due to technical complications, were excluded.

View this table:
[Table 4](http://canjsurg.ca/content/54/2/101/T4)

Table 4 
Categorical parameters associated with long-term survival after liver transplantation in a cohort of 401 patients, after exclusion of patients with early postoperative mortality (< 90 days)

View this table:
[Table 5](http://canjsurg.ca/content/54/2/101/T5)

Table 5 
Continuous parameters associated with long-term survival after liver transplantation in a cohort of 401 patients, after exclusion of patients with early postoperative mortality (< 90 days)

## Discussion

The issue of allocation of deceased donor organs is not new, and it spans through all types of grafts (e.g., liver, kidney). Two different philosophical approaches have been developed.14 The first approach, the justice or equity view, maximizes access for all potential candidates and adopts a “sickest first” policy. The second approach, the utility view, strives to maximize graft survival and therefore allocates organs preferentially to the lowest-risk candidates. Ideally, with a limitless supply of donated organs, a justice allocation system should be implemented. However, since donated grafts are scarce resources, especially now that the increase in demand has been on the part of patients who were previously considered unsuitable for transplantation, the issue of organ utility is gradually more discussed within the transplant community.15 This debate is furthered fueled by a stipulation in US federal regulation that organ allocation should strive to maximize lifetime benefit.16 To develop a liver graft allocation system that incorporates the principle of utility, the development of a model that predicts posttransplant outcomes is necessary. Since MELD or CTP scores are unable to accurately predict postoperative patient survival,5–7,12 identification of preoperative parameters that correlate with outcomes is mandatory. Indeed, it seems implausible that any system, which needs to combine justice and utility in liver graft allocation, would not include donor or other variables that have been shown to be important.8,10,17

In the present study, while the incidence of early postoperative mortality correlates positively with either CTP or MELD scores, the c-statistic of the ROC curves was well below 0.7 for both scores. This finding is in agreement with previous literature.5–7 Warm ischemia time was an independent predictor of early postoperative mortality, but the c-statistic of the relevant ROC curve was again below 0.7. However, WIT might have a place in a model predicting early postoperative mortality after liver transplantation. Actually, WIT is an intraoperative rather than a preoperative parameter. In addition, transplant surgeons always try to achieve the smallest WIT possible and, whenever a long WIT is recorded, intraoperative technical difficulties have been encountered (e.g., poor vascular anastomosis, inadequacy of inflow vessels, ongoing hemorrhage). Moreover, these technical difficulties explain the broad range of WIT and the subsequent wide CI in parametric analysis. However, despite the wide CI in both multivariate analyses, because of the high odds ratios (2.944 and 2.823, respectively) WIT-based nomograms for predicting post-transplant mortality should be developed and validated in large (ideally multicentre) patient cohorts. In this way, calculation of the maximum allowable WIT for a specific recipient, graft combination will become feasible, with all its implications for the training of transplant fellows and for the intraoperative surgical performance.

Female sex was among the parameters that were identified to be independent predictors of improved long-term survival. Previously, female sex has been associated with increased recipient survival.18,19 Absence of HCC was associated, as anticipated, with improved long-term survival. This is particularly true for this specific patient cohort, since almost half of the recipients with diagnoses of HCC exceeded Milan criteria.20 An interesting finding was the observation that older recipients tended to have a slightly better long-term survival (odds ratio 1.022). Most likely this is a curiosity of statistics, since it is hard to explain the finding pathophysiologically and since many authors have reported the opposite result.21 On the other hand, it was surprising to find that HCV cirrhosis was not associated with statistically inferior long-term survival. The satisfactory survival curves in patients with HCV might be explained by the antithymocyte globulin induction/low maintenance immunosupression policy followed in our centre, which is suggested to be associated with good outcomes in this particular patient population.22

## Conclusion

The CTP or MELD scores, in conjunction with WIT, might have a role in predicting early postoperative mortality after liver transplantation. On the other hand, recipient sex and absence of HCC are associated with improved long-term survival.

## Footnotes

*   **Competing interests:** None declared.

*   **Contributors:** Drs. Vrochides, Tchervenkov and Wong designed the study. Drs. Vrochides, Hassanain, Chaudhury, Ghali and Chan acquired the data, which Drs. Vrochides, Hassanain, Barkun, Paraskevar, Chantarovich and Deschenes analyzed. Drs. Vrochides and Hassanain wrote the article, which all other authors reviewed. All authors approved the final version for publication.

*   Accepted January 14, 2010.

## References

1.  Freeman RB, Wiesner R, Edwards E.Results of the first year of the new liver allocation plan.Liver Transpl 2004;10:7–15.
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1002/lt.20024&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=14755772&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 
    
    [Web of Science](http://canjsurg.ca/lookup/external-ref?access_num=000220373600002&link_type=ISI) 

2.  Schaubel DE, Guidinger MK, Biggins SW, et al.Survival benefit-based deceased-donor liver allocation.Am J Transplant 2009;9:970
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1111/j.1600-6143.2009.02571.x&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=19341419&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 

3.  Asfar S, Metrakos P, Fryer J, et al.An analysis of late deaths after liver transplantation.Transplantation 1996;61:1377
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1097/00007890-199605150-00016&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=8629300&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 
    
    [Web of Science](http://canjsurg.ca/lookup/external-ref?access_num=A1996UL46800016&link_type=ISI) 

4.  Christensen E.Prognostic models including the Child-Pugh, MELD and Mayo risk scores: Where are we and where should we go.J Hepatol 2004;41:344
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1016/j.jhep.2004.06.005&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=15288486&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 
    
    [Web of Science](http://canjsurg.ca/lookup/external-ref?access_num=000223455500024&link_type=ISI) 

5.  Desai NM, Mange KC, Crawford MD, et al.Predicting outcome after liver transplantation: utility of the model for end-stage liver disease and a newly derived discrimination function.Transplantation 2004;77:99
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1097/01.TP.0000101009.91516.FC&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=14724442&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 

6.  Jacob M, Copley LP, Lewsey JD, et al.Pretransplant MELD score and post liver transplantation survival in the UK and Ireland.Liver Transpl 2004;10:903
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1002/lt.20169&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=15237375&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 

7.  Wiesner RH, McDiarmid SV, Kamath PS, et al.MELD and PELD: application of survival models to liver allocation.Liver Transpl 2001;7:567
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1053/jlts.2001.25879&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=11460223&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 
    
    [Web of Science](http://canjsurg.ca/lookup/external-ref?access_num=000169879600002&link_type=ISI) 

8.  Biggins SW, Kim WR, Terrault NA, et al.Evidence-based incorporation of serum sodium concentration into MELD.Gastroenterology 2006;130:1652
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1053/j.gastro.2006.02.010&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=16697729&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 
    
    [Web of Science](http://canjsurg.ca/lookup/external-ref?access_num=000237686700016&link_type=ISI) 

9.  Cholongitas E, Marelli L, Shusang V, et al.A systematic review of the performance of the model for end-stage liver disease (MELD) in the setting of liver transplantation.Liver Transpl 2006;12:1049
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1002/lt.20824&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=16799946&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 
    
    [Web of Science](http://canjsurg.ca/lookup/external-ref?access_num=000238711700006&link_type=ISI) 

10. Weismüller TJ, Prokein J, Becker T, et al.Prediction of survival after liver transplantation by pre-transplant parameters.Scand J Gastroenterol 2008;43:736
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=18569992&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 

11. Habib A, Duorchik I, Ahmad J, et al.MELD as predictor of post-transplantation.Hepatology 2004;40:261A
    
    

12. Narayanan Menon KV, Nyberg SL, Harmsen WS, et al.MELD and other factors associated with survival after liver transplantation.Am J Transplant 2004;4:819
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1111/j.1600-6143.2004.00433.x&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=15084180&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 

13. Ghobrial RM, Steadman R, Gornbein J, et al.A 10-year experience of liver transplantation for hepatitis C: analysis of factors determining outcome in over 500 patients.Ann Surg 2001;234:384–93discussion 393–4
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1097/00000658-200109000-00012&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=11524591&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 
    
    [Web of Science](http://canjsurg.ca/lookup/external-ref?access_num=000170873100022&link_type=ISI) 

14. Shapiro R.Kidney allocation and the perception of fairness.Am J Transplant 2007;7:1041
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=17456196&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 

15. Freeman RB.Survival benefit: quality versus quantity and trade-offs in developing new renal allocation systems.Am J Transplant 2007;7:1043
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=17359499&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 

16. (1998) Federal Register Doc. 98-8191, Organ procurement and transplantation network §121.8. Available: [http://federalregister.gov/a/98-8191](http://federalregister.gov/a/98-8191). accessed 2011 Feb. 22.
    
    

17. Burroughs AK, Sabin C, Rolles K, et al.Three- and twelve-month mortality following first liver transplant among adults in Europe: predictive models for outcome.Lancet 2006;367:225
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1016/S0140-6736(06)68033-1&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=16427491&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 
    
    [Web of Science](http://canjsurg.ca/lookup/external-ref?access_num=000234810600027&link_type=ISI) 

18. Waki K.UNOS Liver Registry: ten year survivals.Clin Transpl 200629–39.
    
    

19. Yao FY, Saab S, Bass NM, et al.Prediction of survival after liver retransplantation for late graft failure based on preoperative prognostic scores.Hepatology 2004;39:230
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1002/hep.20005&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=14752842&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 

20. Mazzaferro V, Regalia E, Doci R, et al.Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis.N Engl J Med 1996;334:693
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1056/NEJM199603143341104&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=8594428&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom) 
    
    [Web of Science](http://canjsurg.ca/lookup/external-ref?access_num=A1996TZ52800004&link_type=ISI) 

21. Beckebaum S, Sotiropoulos GC, Klein CG, et al.Predictive factors of outcome in patients transplanted for hepatits B.Transplantation 2009;87:872
    
    

22. Horton PJ, Tchervenkov J, Barkun JS, et al.Antithymocyte globulin induction therapy in hepatitis C positive liver transplant recipients.J Gastrointest Surg 2005;9:896
    
    [CrossRef](http://canjsurg.ca/lookup/external-ref?access_num=10.1016/j.gassur.2005.06.020&link_type=DOI) 
    
    [PubMed](http://canjsurg.ca/lookup/external-ref?access_num=16137581&link_type=MED&atom=%2Fcjs%2F54%2F2%2F101.atom)