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Keywords:

  • Hemodialysis;
  • liver transplantation;
  • MELD;
  • renal dysfunction

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Allocation of cadaveric livers for transplantation in the United States is now based on the severity of illness as determined by the model for end-stage liver disease (MELD) score, a function of bilirubin, creatinine and international normalized ratio (INR). The aim of our study was to determine the association of various pre-transplant risk factors, including the MELD score, on patient survival after orthotopic liver transplantation (OLT). The medical records of 499 consecutive patients (233 female, 266 males, mean age 50.9 ± 10.6 years) undergoing cadaveric OLT at our institution between June 1990 and February 1998 were reviewed. In the 407 patients alive at the latest contact, follow-up was 4.7 years, with a minimum of 20 months (maximum of 9.4 years). Variables considered for analysis included MELD score, age, pre-transplant renal dysfunction requiring dialysis, Child–Pugh classification, underlying liver disease, diabetes mellitus, and heart disease (ischemic/valvular/other). There were 92 deaths during follow-up. In univariate analysis, the MELD score, renal failure requiring hemodialysis pre-OLT, age > 42 years, and underlying etiology of liver disease were significantly associated with death during long-term follow-up. In multivariate models, age, underlying etiology of liver disease and renal failure requiring hemodialysis were independent predictors of death after OLT.

Presented in part at the Annual Meeting of the International Liver Transplant Society, Berlin 2001.

Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Long-term survival is a useful criterion by which success is measured after orthotopic liver transplantation (OLT). The number of patients listed for OLT has grown in recent years, and the number of cadaveric donors has remained nearly constant. This situation has led to a shortage of cadaveric organs which has become more apparent with the increasing number of people dying on the waiting list (1). Stewardship of organs is therefore necessary to maximize the utility of a scarce resource. The model for end-stage liver disease (MELD) score, calculated from the patients serum bilirubin, creatinine and international normalized ratio for prothrombin time (INR) has been recently shown to predict short- and medium-term survival of patients with cirrhosis of the liver (2,3). Since February 2002, allocation of cadaveric livers in the United States has been based on the MELD score rather than time spent on the waiting list.

Several previous studies have looked at patient survival after OLT. In these studies renal function is often recognized as a major determinant of patient survival after OLT (4). Other factors including diabetes mellitus, heart disease, underlying etiology of liver disease and gender have also been shown to influence survival after OLT (5–9). As the MELD score is heavily weighted in favor of patients with renal dysfunction, there is concern that allocation of organs based on the MELD score will adversely affect long-term outcome after liver transplantation. In addition, a recent study reported a correlation between the pre-transplant MELD score and mortality in the first 2 years after OLT (10). The aims of our study were to determine the effect of the MELD score and the role of risk factors in determining long-term outcome after OLT.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Patients

The medical records of 499 consecutive patients (233 female, 266 male, mean age 50.9 ± 10.6 years) undergoing cadaveric OLT at our institution between June 1990 and February 1998 were reviewed. Recipients of multiple liver transplants were categorized by their first procedure. Indications for liver transplantation are listed in Table 1. Risk factors considered for long-term survival after OLT included age, gender, pre-transplant renal dysfunction requiring hemodialysis, Child–Pugh classification at the time of OLT, MELD score at the time of OLT, etiology of underlying liver disease, diabetes mellitus, and heart disease (ischemic/valvular/other). All patients who had undergone at least one session of hemodialysis or had been on continuos veno-venous hemodiafiltration (CVVHD) while awaiting liver transplantation were designated as having undergone hemodialysis. The MELD score was calculated the day of OLT using the formula 10*[0.96 * log(creatinine) + 1.12 * log(INR) + 0.38 * log(total bilirubin) + 0.64] (2). Diabetes mellitus was defined as a risk factor if the serum glucose concentration was more than 140 mg/dL on two separate occasions requiring treatment with insulin, oral hypoglycemic medication or diet adjustment. Heart disease was further categorized as valvular heart disease established by echocardiography, ventricular arrhythmia without significant coronary artery disease and coronary artery disease established by stress testing or coronary angiography. Manifestations of coronary artery disease included a history of myocardial infarction or congestive heart failure.

Table 1.  Indications for liver transplantation
Etiology of liver diseaseNumber (%)
Alcohol57 (12)
Autoimmune26 (6)
Viral118 (24)
Primary biliary cirrhosis79 (16)
Primary sclerosing cholangitis124 (25)
α-1-anti-trypsin deficiency25 (5)
Malignancy13 (3)
Other44 (9)
Total499

Statistical analysis

Univariate logistic regression models were used to assess the association of 30-day death with renal failure requiring hemodialysis, MELD score at the time of the OLT [both the actual score as well as dichotomizing the score at the 75th percentile (<21 points vs. ≥ 21 points)], age, gender, diabetes mellitus, heart disease, and Child–Pugh classification at the time of OLT. Age was dichotomized at 42 years (≤42 vs. > 42 years), the youngest 20% of patients, after investigation, showed that the age effect for the youngest patients was having a reduced risk of death post-transplantation. Because there was a total of 15 deaths during this period no multivariate models were attempted. However in an attempt to determine whether the MELD score or pre-transplant hemodialysis was more strongly associated with 30-day survival, a model was run including both factors as predictors. Overall patient survival post-transplantation was estimated using the Kaplan–Meier method. Univariate determination of those risk factors associated with overall patient survival after OLT, including the 15 deaths in the early time period, was done using Cox proportional hazards regression. Those risk factors univariately significant, p ≤ 0.05, were considered as candidates for a multiple variable model, also performed using Cox proportional hazards regression. The alpha-level was set at 0.05 for statistical significance. The study was approved by the Institutional Review Board.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Patient demographics are shown in Table 2. The most common indication for OLT was cholestatic liver disease (primary biliary cirrhosis and primary sclerosing cholangitis) in 203 patients (41%). Among patients with cardiac disease, 11 patients had coronary artery disease including a history of myocardial infarction in 6 patients and congestive cardiac failure in 2 patients. Of the 13 patients with malignancies, 6 patients had hepatocellular carcinomas, 5 patients had cholangiocarcinoma and 2 patients had hemangioendothelioma. One hundred patients (20%) had a serum creatinine ≥1.5 pre-transplant. Twenty-two patients of these patients were on hemodialysis. No patient received peritoneal dialysis. Nine patients received a combined kidney and liver transplant. Among the group of patients requiring dialysis prior to OLT, 14 of the 22 patients (64%) required dialysis in the post-transplant period. The minimum period of follow-up was 20 months in those patients alive at the last contact. There were 15 deaths in the first 30 days after transplantation, another 25 during the first year, and a total of 92 deaths during follow-up (Table 3). The Kaplan–Meier estimate of survival probabilities at 1 and 5 years after transplantation were 92%[95% confidence interval (CI), 84–94] and 82% (95% CI, 79–86), respectively (Figure 1). The major cause of death was infection in 24% of patients (22 out of 92 deaths). The median duration of follow-up in the remainder of the 407 patients was 4.7 years (range 1.7–9.4).

Table 2.  Demographic data
Demographic (n = 499)Mean ± SD (range)/number (%)
Age50.9 ± 10.6 (18.8–71.7)
Gender (female)233 (46.7%)
Diabetes – Total77 (15.4%)
 Insulin dependent45 (9%)
 Non-insulin dependent32 (6.4%)
Heart Disease – Total105 (21%)
 Coronary artery disease11 (2.2%)
 Amyloid3 (0.6%)
Pre-transplant dialysis22 (4.4%)
Child–Pugh Score8.7 ± 2.1 (5–15)
 Child's A (5–7)143 (28.7%)
 Child's B (8–10)246 (49.3%)
 Child's C (11–15)110 (22%)
MELD score16.9 ± 9.9 (–2–65)
Table 3.  Causes of death in patients undergoing orthotopic liver transplantation
Cause of deathNumber (%)
Infection22 (24)
Cardiovascular 8 (9)
Cancer 8 (9)
Intra-operative death 7 (8)
Primary nonfunction 6 (7)
Recurrent cancer 6 (7)
(hepatocellular/cholangiocarcinoma)
Recurrent viral hepatitis 5 (5)
Cerebrovascular accident 5 (5)
ARDS 3 (3)
Miscellaneous16 (17)
Unknown 6 (7)
Total92 (100)
image

Figure 1. Kaplan–Meier estimation of overall patient survival.

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Univariate logistic regression analysis was used to assess risk factors for association with patient death during the first 30 days after transplantation. This analysis showed that renal failure requiring hemodialysis (p < 0.001), odds ratio (OR) (95% CI) = 18.9 (6.0–59.6), and MELD score (p < 0.001), OR (95% CI) = 2.0 (1.4–2.8) per 10 points in MELD, were significantly associated with early death (Table 4). In multivariate analysis, the MELD score was no longer significant.

Table 4.  Univariate analysis of risk factors for death within 30 days of OLT
DemographicAll 499 patients
p-valueOdds ratio (95% CI)
  1. 1Evaluated as a continuous measure, the risk ratio cited reflects the increased odds of death within 30 days of transplant for an increase of 10 points in the MELD score.

  2. 2A MELD score of 21 corresponds to the 75th percentile in the study patients.

  3. 3Relative to a diagnosis of PBC.

  4. PBC, primary biliary cirrhosis; PSC, primary sclerosing cholangitis.

Pre-transplant hemodialysis<0.00118.9 (6.0–59.6)
Age > 42 years 0.210 3.7 (0.5–28.4)
MELD score (/10 points)1<0.001 2.0 (1.4–2.8)
MELD score ≥ 21 points2 0.012 3.8 (1.3–10.7)
Diabetes mellitus 0.358 0.4 (0.1–3.0)
Heart disease 0.243 1.9 (0.6–5.7)
Child–Pugh classification 0.070 2.0 (0.9–4.3)
Male gender 0.602 0.8 (0.3–2.1)
Etiology of liver disease3
 PBC  1.0
 Alcohol 0.400 2.8 (0.3–32.1)
 Alpha-1-anti-trypsin def. 0.04510.6 (1.1–107.4)
 Autoimmune hepatitis 0.449 0.0
 Malignancies 0.607 0.0
 PSC 0.842 1.3 (0.1–14.3)
 Viral Hepatitis 0.372 2.7 (0.3–25.0)
 Other 0.209 4.3 (0.4–42.8)

Univariate proportional hazards regression was used to assess risk factors for association with patient death any time during the post-transplantation period, including the first 30 days (Table 5). In these analyses pre-transplant hemodialysis [p < 0.001, relative risk (RR) = 4.0], the MELD score (p = 0.01, RR = 1.25 per 10 points), age > 42 years (p = 0.01, RR = 2.3), and etiology of underlying liver disease relative to primary biliary cirrhosis (p < 0.001) were each significant risk factors for death after OLT. The final multiple variable model included age >42 years (p < 0.001, RR = 3.4), etiology of underlying liver disease (p < 0.001), and renal failure requiring hemodialysis pre-OLT (p < 0.001, RR = 5.1) as independent risk factors for death after OLT (Table 6). After including renal failure requiring hemodialysis in this model, the association of patient death and the MELD score was no longer significant, p = 0.2 RR (95% CI) = 1.1 (0.9–1.4) per 10 points in MELD. The median (25th percentile–75th percentile) MELD score in patients dying during follow-up was 16.9 (10.4–24.9) and in patients alive at last follow-up was 14.6 (10.3–19.8). However, as there were only a small number of patients on hemodialysis, the model was also run after excluding these patients. Again, the MELD score was not found to be an independent predictor of death after OLT even after patients on hemodialysis were excluded.

Table 5.  Univariate analysis of risk factors for death subsequent to OLT
Demographicp-valueRisk ratio (95% CI)
  1. 1Evaluated as a continuous measure, the risk ratio cited reflects the increased odds of death within 30 days of transplant for an increase of 10 points in the MELD score.

  2. 2Relative to a diagnosis of PBC.

Pre-transplant hemodialysis<0.0014.0 (2.1, 7.5)
Age > 42 years<0.0012.3 (1.2, 4.5)
MELD score (/10 points)1 0.0121.3 (1.1, 1.5)
Diabetes mellitus 0.3461.3 (0.8, 2.2)
Heart disease 0.2791.3 (0.8, 2.1)
Child–Pugh classification 0.3221.2 (0.9, 1.6)
Male gender 0.6031.1 (0.7, 1.7)
Etiology of liver disease2
 PBC 1.0
 Alcohol 0.0053.6 (1.5, 8.8)
 Alpha-1-anti-trypsin def. 0.0094.1 (1.4, 11.7)
 Autoimmune hepatitis 0.0652.8 (0.9, 8.3)
 Malignancies 0.0953.2 (0.8, 12.3)
 PSC 0.7751.1 (0.5, 2.9)
 Viral hepatitis 0.0242.6 (1.1, 1.6)
 Other 0.0033.9 (1.6, 9.4)

The presence of heart disease or diabetes mellitus pre-transplant was not significantly associated with long-term survival after OLT in this group of patients.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References

Our study found that hemodialysis pre-transplantation, age at the time of transplantation and the etiology of underlying liver disease were independent predictors of long-term survival after OLT. The MELD score, Child–Pugh class, the presence of diabetes mellitus or heart disease did not predict survival after OLT.

In February 2002, the MELD score was implemented in an effort to reduce deaths occurring on the waiting list for cadaveric liver transplantation. However, as the score is heavily weighted in favor of patients with renal dysfunction, concerns have been raised whether this organ allocation system would result in decreased post-transplant survival rates (4). While the MELD score was univariately significant in our study, once hemodialysis pre-transplantation was included, the MELD score was no longer an independent predictor of patient death post-transplantation. This is probably because the MELD score assesses some risks for death that are no longer pertinent issues for the patient after transplantation. Therefore while the MELD score has some association with death after transplantation, the impact of hemodialysis on survival appears to be stronger signifying that hemodialysis is a better predictor of a patient's post-transplant mortality risk than the MELD score. Additionally, the MELD score had no association with death even after patients on hemodialysis were excluded from the analysis. There have been very few reports examining the predictive ability of the MELD score to post-transplant survival. In a cohort of 42 status 2A patients the MELD score was not found to be predictive of short-term survival after OLT (11). Other reports have shown a correlation between the pre-transplantation MELD scores and survival after OLT (10,12). Our study indicates that the MELD score is not an independent predictor of death after OLT when pre-transplant hemodialysis, age and etiology of liver disease are taken into account and may provide support for the current allocation system. However, our study was a retrospective one and it would be difficult to speculate if our results could be reproduced under the current organ allocation system. Therefore, further prospective studies should be undertaken to determine if the MELD score is also an independent predictor of mortality after liver transplantation and thus further evaluate its applicability to the organ allocation system.

Renal dysfunction requiring hemodialysis was an independent predictor of death during long-term follow-up after OLT in the entire group of patients and appeared to be a stronger predictor of death compared with the MELD score. However in patients surviving the first year after OLT, neither pre-transplant hemodialysis nor the MELD score were independent predictors of death in our study. Fifty percent of patients requiring dialysis in the pre-transplant period (11 of 22) died during follow-up. The major cause of death in this group was infection in 45% (5 of 11) of patients (Table 3). Of the patients who received combined liver and kidney transplants (n = 9), two (22%) died during follow-up. Pre-operative renal dysfunction has been shown to be associated with an increased hospital mortality rate and predicts short-term survival after OLT (13,14). Renal dysfunction has also been described as an independent predictor of both short-term and long-term survival in a small number of patients with nonbiliary cirrhosis after liver transplantation, and in patients with fulminant hepatic failure and cirrhosis (15–18). Nair et al. in their analysis of the UNOS database have shown that the presence of moderate or severe renal failure was associated with a lower long-term survival after OLT, although the incidence of hemodialysis is unknown in their study (4). Increased mortality in patients with renal dysfunction and those on hemodialysis is thought to be related to an increased incidence of bacterial and fungal infections (11,14,19). Infections have also been reported to be the major cause of death in the first 5 years after OLT, similar to that seen in our cohort of patients (20). Our study clearly demonstrates the increased risk of death in patients with dialysis-dependent renal failure undergoing OLT secondary to infections and addresses the need for greater vigilance against infection in this subgroup of patients (Figure 2). The UNOS modification of MELD score currently in place for organ allocation caps the serum creatinine at 4 mg/dL and adds no extra points for patients on hemodialysis. Based on the findings of our study, the impact of hemodialysis on long-term outcome after OLT needs to be studied further.

image

Figure 2. Kaplan–Meier estimate of the difference in survival between patients with a pre-transplant history of hemodialysis and those without. The error bars indicate 95% confidence limits.

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Age at the time of transplantation and the etiology of underlying liver disease were independent predictors of long-term survival after OLT. With increasing age at the time of OLT, an increase in death of 26% per 10 years of age was seen. Although age ≤ 42 years was seen as an independent predictor of survival, we also found that OLT could be performed safely in patients over 65 years of age. The 5-year survival in our cohort of highly selected patients who were 65 years of age and older (n = 42) was >60%. Four of the patients in our study were at least 70 years old at the time of OLT. Patients undergoing OLT for primary biliary cirrhosis had the best outcomes compared with patients with other causes of end-stage liver disease while the poorest outcome was seen in patients with chronic autoimmune liver disease. It has been well documented in the literature that patients with cholestatic liver disease have improved survival compared with patients with other etiologies of chronic liver disease (9). The survival in our cohort of patients may have thus been influenced by the fact that there was an increased number of patients with cholestatic liver disease (41%) compared with other populations.

The presence of diabetes mellitus, including the use of insulin, was not a risk factor for long-term survival after OLT. There have only been a few studies looking at the impact of diabetes mellitus on liver transplantation and the results have been contradictory. Short-term studies have found no difference in graft and patient survival after OLT in diabetics while other longer term studies have found significantly lower survival in diabetics (5,7,21–23). An excess of cardiovascular, eye, neurologic, infectious and renal complications have been seen in diabetic patients undergoing OLT and are thought to contribute to the decreased survival in some of the reported studies (5,7). Diabetes mellitus was not an independent predictor of survival in our cohort of patients. Stringent patient selection criteria may have been an important reason for the lack of excess mortality in diabetics in our study as all patients were pre-screened for complications related to diabetes mellitus. Although the 5-year survival of diabetic patients over 65 years of age was lower when compared with all patients over the age of 65 years (61% vs. 74%), this difference was not statistically significant.

The presence of heart disease was not an independent predictor of survival after OLT in our select group of patients. While the incidence of coronary artery disease has been reported to be between 2.5% and 27% in OLT candidates depending on the criteria used, a small number of studies have addressed the outcome of patients with coronary artery disease undergoing OLT (24–27). Plotkin et al. reported on 32 patients with coronary artery disease treated with medication (n = 9), angioplasty (n = 1) or coronary artery bypass grafting (n = 22) who underwent OLT (28). The 32-month mortality was 50% with 50% of these patients dying peri-operatively. Of the 11 patients with coronary artery disease in our study only 1 patient (9%) died during follow-up. While our results are at variance with the experience of Plotkin et al. the number of patients in our study may be too small for comparison with the larger study. Additionally, we had stringent criteria for the selection of patients with coronary artery disease for OLT and this may have resulted in the difference in survival. All our patients with coronary artery disease were asymptomatic at the time of OLT and underwent extensive testing for inducible myocardial ischemia before proceeding to OLT. It is unclear from our study if the presence of both coronary artery disease and diabetes mellitus increase mortality after OLT. Five out of 11 patients (45%) with coronary artery disease had diabetes mellitus in our study. Only one patient died in this cohort, and this patient was not diabetic.

Our study has some drawbacks. In our paper no special preference was given to patients on hemodialysis. The MELD score may therefore have been artificially lowered in patients on hemodialysis. However, 13 out of 22 (7 alive and 6 dead) patients on hemodialysis continued to have serum creatinine levels > 4 mg/dL and this discrepancy was therefore present only in a small number of patients. The serum creatinine values are also capped at 4 mg/dL in the UNOS modification of the MELD score which we did not apply to the MELD calculation in our paper. The majority of our patients (41%) also had cholestatic liver disease which may not be truly representative of the current cohort of patients who undergo OLT. Patients undergoing OLT during the time period of the study also had a lower MELD score compared with patients who currently undergo OLT.

In summary, renal dysfunction requiring dialysis, age and the etiology of underlying liver disease were independent predictors of death after OLT, while MELD score was not an independent predictor of death after OLT. Heart disease and diabetes mellitus were not predictors of death after OLT in our analysis; however, stringent selection criteria were at place at our institution which likely contributed to improved survival in these two risk groups. In order to ensure optimal utilization of donor organs in the era of MELD, further prospective studies are needed to determine factors that predict long-term survival after OLT.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. References
  • 1
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