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Abstract

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Older recipient age is associated with worse posttransplant survival. Although the median age of liver disease patients undergoing orthotopic liver transplantation (OLT) continues to rise, prognostic factors for posttransplant survival specific to older patients have not been defined. To address this issue, the United Network for Organ Sharing/Organ Procurement and Transplantation Network outcome database was searched to identify prognostic factors for the 8070 liver recipients 60 years old or older who underwent transplantation from 1994 to 2005. Prognostic factors were assessed with univariate analysis and multivariate modeling. The 5 strongest prognostic variables (ventilator status, diabetes mellitus, hepatitis C virus, creatinine levels ≥1.6 mg/dL, and recipient and donor age ≥120 years) were aggregated to define a novel older recipient prognostic score (ORPS). The overall 1- and 5-year posttransplant survival rates were 83% and 67%, respectively. The risk model, created by the assignment of 1 point to each ORPS factor, stratified patient outcomes into distinct prognostic groups at the 1-, 3-, and 5-year posttransplant time points (P < 0.001). The 5-year survival rates for patients with ORPS values of 0, 1, and 2 points were 75%, 69%, and 58%, respectively. Patients who underwent transplantation with an ORPS > 2 points consistently experienced 5-year survival rates of less than 50%. In conclusion, in liver transplant recipients 60 years old or older, the ORPS was able to predict significant and clinically relevant differences in posttransplant survival. By optimization of donor selection for recipients over the age of 60 years, clinical utilization of the ORPS model may enhance organ utilization for all patients awaiting OLT. Liver Transpl, 2010. © 2010 AASLD.

The prevalence of end-stage liver disease in patients 60 years old or older has significantly increased over the last 20 years.1-3 The reasons for this demographic shift in the disease spectrum are multiple. First, the incidence of chronic hepatitis C virus (HCV)–related cirrhosis and associated hepatocellular cancers is sharply rising.4 Because of an increase in HCV infections in young adults during the 1960s and 1970s and the long latency period from infection to the development of complications, patients are now presenting with manifestations of cirrhosis and indications for liver transplant in their sixth and seventh decades of life. Currently, the most common indication for liver transplantation in the United States is cirrhosis caused by viral hepatitis.5

The second reason that the liver transplant list is aging is that overall life expectancies are increasing. The most recent vital statistics report for the United States determined that a 65-year-old today can expect to live an average of 17 additional years, whereas a 75-year-old can expect an average of 11 more years of life.6 Because of these data, older patients are more frequently being considered candidates for liver transplantation. These data have gained relevance because recent studies have noted that recipient age over 60 years is associated with poorer survival after liver transplantation, particularly when age is combined with more severe liver disease or other risk factors for poor outcome.7-10 In light of the preliminary data suggesting poorer outcomes in the growing group of older liver transplant recipients,11 it is imperative that liver transplant outcomes in recipients 60 years old or older be examined to determine prognostic factors for survival after transplantation.

Simultaneous to the aging of the recipient population, there has also been an increase in the use of liver allografts from donors over the age of 60 years.12 This trend can be attributed to the shortage of donor organs, which has led to the use of extended criteria donor allografts. The definition of extended criteria donor varies between institutions but typically includes donors of increased age. Previous studies have associated increased donor age with posttransplant survival, particularly for recipients with hepatitis C,5, 10, 13-17 but a large-scale analysis of the prognostic impact of donor age in older recipients is lacking.

The aging of both recipients and donors presents new challenges to the liver transplant community. To maximize global posttransplant outcomes, there is increased interest in analyzing the survival impact of various combinations of donor and recipient characteristics. Hypothetically, the results of these analyses will enhance our ability to match donors and recipients, limit futile transplants, and maximize long-term outcomes.

The purpose of our study was to identify variables in the United Network for Organ Sharing (UNOS)/Organ Procurement and Transplantation Network (OPTN) database, available at the time of organ offer, that predict survival after transplantation in recipients 60 years old or older. From these variables, a novel older recipient prognostic score (ORPS) was constructed to prospectively predict survival in this specific patient group.

PATIENTS AND METHODS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Exploration of the UNOS/OPTN database identified 46,772 liver transplants performed from 1994 to 2005. After the exclusion of repeat transplants, multiorgan transplants, and status 1–listed recipients, the data set was limited to the 8070 recipients (17%) 60 years old or older who underwent transplantation during this time. Relevant recipient and donor variables listed in the UNOS/OPTN database were simultaneously recorded.

The Model for End-Stage Liver Disease (MELD) score was available for the subset of 3278 study patients (40%) who underwent transplantation from 2002 to 2005. Survival rates stratified by the laboratory MELD score at transplant (<16, 16-23, and >23) were graphed with Kaplan-Meier analysis and compared with log-rank tests. The c-index statistic (an estimation of the area under the receiver operating characteristic curve), which quantified the proportion of all patient pairs for whom the predicted and observed survival rates were concordant,18 was used to rate the ability of MELD to stratify outcomes between groups.

Twenty-six potentially prognostic recipient and donor variables that were listed in the UNOS/OPTN database were identified. To make the analysis clinically relevant, statistical models mainly focused on variables available at the time of organ offer. These variables included recipient demographic variables (age, sex, race, and UNOS region), recipient clinical variables [body mass index (BMI), hospitalization status, hepatocellular carcinoma (HCC) status, HCV status, hepatitis B virus (HBV) status, serum creatinine, and ventilator status], recipient disease severity variables (time waiting, encephalopathy, ascites, diabetes mellitus, and serum albumin), donor variables (age, sex, BMI, race, diabetes mellitus, hypertension, cigarette use, HBV status, and donor proximity), and other variables (combined donor and recipient age). Univariate survival analysis with log-rank tests was used to determine the prognostic significance of these variables (SPSS version 14, SPSS, Inc., Chicago, IL), with a P value less than 0.05 indicating statistical significance.

For multivariate analysis, all statistically and clinically significant variables in univariate analysis were entered into a Cox proportional hazards model. Forward logistic regression was used to identify variables with independent statistical significance. From the 8 study factors with independent statistical significance in multivariate analysis, the 5 study variables with the strongest independent associations with posttransplant survival were used as the basis for the ORPS system. Due to their similar odds ratios, each of these 5 variables was assigned 1 point, and patients with data for all 5 variables were stratified on the basis of the total points. Survival rates for each patient group were graphed with Kaplan-Meier survival curves and compared with log-rank tests (SPSS version 14) Again, the c-index statistic was used to assess the ability of the ORPS to stratify survival between score groups.

RESULTS

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Study Variables and Overall Survival

The distribution of study factors within the study population of liver recipients 60 years or older from 1994 to 2005 available in the OPTN data set is detailed in Table 1. These include recipient demographic factors (age at transplant, sex, race, and UNOS region), recipient clinical factors (BMI, inpatient status, HCC status, HCV status, HBV status, serum creatinine, and mechanical ventilation), recipient disease severity factors (encephalopathy, wait time, ascites, diabetes mellitus, and serum albumin), donor variables (age, sex, BMI, race, diabetes mellitus, hypertension, cigarette use, hepatitis B core antibody status, and locoregional-national proximity to the transplant center), and other variables (combined recipient and donor age, cold ischemic time, and warm ischemic time).

Table 1. Descriptive Statistics for Selected Recipient and Donor Factors for Patients 60 Years Old or Older Who Underwent Liver Transplantation (n = 8070, 1994-2005)
VariableStratan%
Recipient demographics   
 Age60-69 years732592
70-79 years6278
≥80 years4<1
 SexMale450657
Female344643
 RaceCaucasian498379
Black2444
Hispanic70911
Asian4116
 UNOS region1. Upper Northeast2233
2. Pennsylvania, New Jersey, Delaware, Maryland, District of Columbia, and West Virginia5639
3. Southeast116118
4. Texas and Oklahoma4858
5. Southwest105117
6. Pacific Northwest1542
7. Midwest71111
8. Great Plains4257
9. New York5549
10. Indiana, Ohio, and Michigan4808
11. Mid-Atlantic4818
Recipient clinical variables   
 BMI<35 kg/m2629390
≥35 kg/m267610
 Inpatient statusHospitalized228829
Home565171
 HCC statusPositive87211
Negative705389
 HCV statusPositive220133
Negative445867
 HBV statusPositive125218
Negative557382
 Creatinine≥1.6 mg/dL161821
<1.6 mg/dL622579
 Mechanical ventilationYes2253
No772797
Recipient disease severity   
 EncephalopathyYes272756
No346044
 Wait time<7 days5447
≥7 days737993
 AscitesYes634482
No137218
 Diabetes mellitusYes195326
No553374
 Albumin<2.5 mg/dL184724
≥2.5 mg/dL578276
Donor variables   
 Donor age<40 years380847
40-49 years147918
50-59 years129616
60-69 years94512
≥70 years5317
 Donor sexMale445856
Female349344
 Donor BMI≥35 kg/m24205
<35 kg/m2725095
 Donor raceBlack87711
Other711089
 Donor diabetes mellitusYes4917
No701893
 Donor hypertensionYes204427
No541173
 Donor cigarette useYes282738
No460262
 Donor HBV core antibodyPositive3855
Negative746595
 Donor proximityLocal585473
Regional170321
National5096
Other variables   
 Donor and recipient age<100 years328541
100-119 years276234
120-139 years173022
140-159 years2813
≥160 years1<1
 Cold ischemic time0-5.9 hours146626
6-8.9 hours275350
9-11.9 hours70513
≥12 hours63311
 Warm ischemic time0-29 minutes79315
30-60 minutes340967
≥60 minutes91118

For the entire cohort (n = 8070), the 1-, 3-, and 5-year allograft survival rates were 79%, 70%, and 63%, respectively. The corresponding 1-, 3-, and 5-year recipient survival rates were 83%, 74%, and 67%, respectively.

Univariate Analysis of Recipient Survival

From the total list of study variables, univariate analysis of posttransplant patient survival yielded 16 study variables associated with worse patient outcomes (Table 2). The recipient variables that predicted poor survival included age of 70 years or greater, hospitalization at the time of transplant, positive hepatitis C serology, serum creatinine levels of 1.6 mg/dL or higher, ventilator requirement, presence of encephalopathy, wait time less than 7 days, diagnosis of diabetes mellitus, and serum albumin levels less than 2.5 mg/dL. The donor variables that were associated with worse posttransplant survival were donor age of 50 years or greater, diabetes mellitus, hypertension, positive hepatitis B core antibody serology, and national donor share. In addition, a combined donor and recipient age of 120 years or more and a cold ischemic time longer than 12 hours predicted poor survival (all univariate P values <0.05).

Table 2. Recipient and Donor Factors Significantly Associated with Posttransplant Survival in Univariate Analysis for Patients 60 Years Old or Older Who Underwent Liver Transplantation (n = 8070, 1994-2005)
VariableStratan5-Year Survival (%)Univariate P Value
Recipient demographics    
 Age60-69 years732568 
70-79 years62756<0.001
≥80 years4 
Recipient clinical variables    
 Inpatient statusHospitalized228848<0.001
Home565156 
 HCV statusPositive220148<0.001
Negative445855 
 Creatinine≥1.6 mg/dL161844<0.001
<1.6 mg/dL622555 
 Mechanical ventilationYes22538<0.001
No772753 
Recipient disease severity    
 EncephalopathyYes3347530.002
No490254 
 Wait time<7 days544460.011
≥7 days737953 
 Diabetes mellitusYes1953480.001
No553356 
 Albumin<2.5 mg/dL184746<0.001
≥2.5 mg/dL578255 
Donor variables    
 Donor age<40 years380870 
40-49 years147971 
50-59 years129663<0.001
60-69 years94560 
≥70 years53160 
 Donor diabetes mellitusYes491490.007
No701853 
 Donor hypertensionYes204451<0.001
No541154 
 Donor HBV core antibodyPositive385440.002
Negative746553 
 Donor proximityLocal585468 
Regional170366 
National509600.047
Other variables    
 Donor and recipient age<100 years328570 
100-119 years276269 
120-139 years173061<0.001
140-159 years28155 
≥160 years1 
 Cold ischemic time0-5.9 hours146669 
6-8.9 hours275369 
9-11.9 hours70568 
≥12 hours633610.001

Determination of Recipient, Donor, and Combined Recipient and Donor Age Prognostic Breakpoints

To identify clinically relevant breakpoints, recipient age, donor age, and combined recipient and donor age risk factors were individually analyzed as continuous variables. With respect to recipient age, the study population was defined as patients 60 years old or older. An individual analysis of posttransplant patient survival stratified by recipient age (range = 60-87 years) indicated a survival advantage for recipients 60 to 69 years old (n = 7325, 5-year survival = 68%) versus recipients 70 years old or older (n = 627, 5-year survival = 56%, univariate P < 0.001; Table 2). An analysis of donor age as a continuous variable identified an increased risk of posttransplant death when donors 50 years old or older were involved. The use of donors younger than 40 years and the use of donors between 40 and 49 years of age led to similar 5-year survival rates (70% versus 71%, respectively, P = 0.84). In contrast, the use of donors 50 to 59 years old, 60 to 69 years old, and 70 years or older resulted in significantly worse recipient 5-year survival rates of 63%, 60%, and 60%, respectively (univariate P < 0.001; Table 2).

A detailed analysis of combined recipient and donor age as a continuous variable yielded a clear recipient outcome breakpoint at a value of 120 years. A comparison of 2 recipient and donor age combinations (<100 years and 100-119 years) showed no significant difference in recipient 5-year survival rates (70% versus 69%, P = 0.46). However, recipient and donor age combinations of 120 to 139 years and 140 years or more were associated with significantly poorer outcomes (5-year survival rates of 61% and 55%, respectively, P < 0.001; Table 2 and Fig. 1).

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Figure 1. Kaplan-Meier analysis of patient survival stratified by recipient and donor age. An outcome breakpoint at 120 years is indicated (P < 0.001).

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Multivariate Analysis

All variables significantly associated with poor survival in univariate analysis were entered into a Cox proportional hazards model. In multivariate analysis, the variables that were independent predictors of poor patient survival were recipient albumin levels less than 2.5 mg/dL [hazard ratio (HR) = 1.18, confidence interval (CI) = 1.05-1.32, P = 0.005], recipient hospitalization at the time of organ offer (HR = 1.20, CI = 1.07-1.34, P = 0.002), ventilator dependence at the time of transplant (HR = 1.26, CI = 1.08-1.71, P = 0.009), recipient diabetes mellitus (HR = 1.27, CI = 1.14-1.42, P < 0.001), recipient positive hepatitis C serology (HR = 1.34, CI = 1.21-1.49, P < 0.001), recipient serum creatinine levels of 1.6 mg/dL or higher (HR = 1.40, CI = 1.25-1.58, P < 0.001), and combined recipient and donor age of 120 years or more (HR = 1.49, CI = 1.33-1.66, P < 0.001; Table 3).

Table 3. Multivariate Analysis of Risk Factors Independently Associated with Death After Liver Transplantation in Patients 60 Years Old or Older
VariableStratanOdds Ratio95% CIMultivariate P Value
  1. NOTE: The risk factors are presented in the ascending order of odds ratios. The 5 risk factors with multivariate statistical significance and odds ratios greater than 1.2 (bold) were entered into the ORPS model.

Recipient albumin<2.5 mg/dL18471.181.05-1.320.005
≥2.5 mg/dL5782   
Recipient inpatient statusHospitalized22881.201.07-1.340.002
Home5651   
Recipient mechanical ventilationYes2251.261.08-1.710.009
No7727   
Recipient diabetes mellitusYes19531.271.14-1.42<0.001
No5533   
Recipient HCV statusPositive22011.341.21-1.49<0.001
Negative4458   
Recipient creatinine≥1.6 mg/dL16181.401.25-1.58<0.001
<1.6 mg/dL6225   
Donor and recipient age<100 years32851.00
100-119 years27621.080.98-1.20NS
120-139 years17301.471.30-1.59<0.001
140-159 years2811.641.31-2.06<0.001
≥1601NS

Novel ORPS System

According to the rank order of the multivariate analysis odds ratios, the 5 strongest predictors of poor patient survival (mechanical ventilation, recipient diabetes mellitus, recipient positive HCV serology, recipient serum creatinine levels of 1.6 mg/dL or higher, and combined recipient and donor age of 120 years or more) were used as the basis for the novel ORPS system. For 6325 study patients, data were available for all 5 variables. With each variable assigned 1 point, 1825 recipients (29%) had a score of 0, 2710 recipients (43%) had a score of 1, 1416 recipients (22%) had a score of 2, 334 recipients (5%) had a score of 3, 39 recipients (1%) had a score of 4, and 1 recipient underwent transplantation with all 5 ORPS risk factors present.

The Cox proportional hazards analysis of patient survival stratified by ORPS demonstrated survival differences between each ORPS group (all pairwise comparisons, P < 0.001; Table 4 and Fig. 2). An ORPS of 0 was associated with 1-, 3-, and 5-year survival rates of 88%, 82%, and 75%, respectively. Patients with an ORPS of 1 experienced 1-, 3-, and 5-year survival rates of 85%, 77%, and 69%, respectively. Patients with an ORPS of 2 experienced 1-, 3-, and 5-year survival rates of 80%, 67%, and 58%, respectively. Patients with an ORPS of 3 experienced 1-, 3-, and 5-year survival rates of 69%, 57%, and 49%, respectively, and were 2.69 times more likely to die than patients with an ORPS of 0. Patients with an ORPS of 4 experienced 1-, 3-, and 5-year survival rates of 73%, 48%, and 41%, respectively, and were 3.16 times more likely to die than patients with an ORPS of 0. The single patient who underwent transplantation with an ORPS of 5 died 103 days after transplantation, and this yielded an odds ratio of death of 13.51 versus recipients with an ORPS of 0. Each of these survival differences was statistically significant at the 1-, 3-, and 5-year survival time points (P < 0.001). Figure 3 further describes the hazard statistic (the risk of death over time) for patients in each of the ORPS groups. The c-index value for the novel survival risk score was 0.74, indicating a significant correlation between the survival predicted by each ORPS value and the actual outcome.

thumbnail image

Figure 2. Kaplan-Meier curves for post–liver transplant survival stratified by points assigned in the ORPS system. One point was assigned to each of the following 5 major prognostic variables: mechanical ventilation, diabetes mellitus, HCV infection, serum creatinine levels of 1.6 mg/dL or higher, and combined recipient and donor age of at least 120 years (P < 0.001 for all pairwise log-rank comparisons at the 1-, 3-, and 5-year follow-up time points).

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thumbnail image

Figure 3. Hazard function graph indicating post-liver transplantation the risk of death stratified by points assigned in the ORPS system. One point was assigned to each of the following 5 major prognostic variables: mechanical ventilation, diabetes mellitus, HCV infection, serum creatinine levels of 1.6 mg/dL or higher, and combined recipient and donor age of at least 120 years.

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Table 4. Cox Proportional Hazards Modeling of Recipient Survival Rates Stratified by ORPS
ORPS Valuen%1-Year Survival (%)5-Year Survival (%)Relative Risk95% CIP Value
  1. NOTE: Significant survival differences were demonstrated between each ORPS value at 1- and 5-year time points. ORPS values greater than 2 were associated with 5-year survival rates lower than 50% (bold).

018252988751.00
127104385691.331.18-1.49<0.001
214162280581.901.68-2.16<0.001
3334569492.692.22-3.27<0.001
439173413.161.98-5.08<0.001
5100013.511.90-100.00<0.001

In general, the ability of the ORPS to stratify outcomes was independent of the UNOS region or donor proximity. In 9 of the 11 UNOS regions examined, the ORPS P value was statistically significant. Likewise, the ability of the ORPS to stratify outcomes was maintained with the subset of liver recipients who underwent transplantation with local or regional donors (P < 0.001 for both). Only the small cohort of national share donor transplants (n = 306) did not conform to the ORPS model (P = 0.26).

Analysis of the MELD Prognostic Value

A MELD score was available for 3278 study patients (40%). Survival rates for these patients were plotted with 3 pretransplant laboratory MELD score groups (<16, 16-23, and >23). For the group with MELD scores less than 16 (n = 1510), 1- and 3-year survival rates were 87% and 77%, respectively; for the group with MELD scores of 16 to 23 (n = 1184), 1- and 3-year survival rates were 83% and 77%, respectively; and for the group with MELD scores greater than 23 (n = 584), 1- and 3-year survival rates were 75% and 72%, respectively. Statistically, a MELD score greater than 23 predicted poor outcome, but lower preoperative MELD scores were unable to differentiate posttransplant survival rates. The c-index statistic for the prognostic value of patient grouping by MELD score was only 0.54, indicating a relative inability of the MELD score to accurately stratify observed patient outcomes.

DISCUSSION

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

Many studies have attempted to identify predictive variables that would allow for better utilization of the scarce resource of liver allografts. Most of these studies have included variables such as the cold ischemic time and operative complications that, although important, are not clinically useful in pretransplant donor-recipient matching. Others have found significant associations between certain variables and outcomes but have been limited in the ability to determine independent significance by small sample sizes.17, 19-21

Few of these analyses have focused on the growing group of recipients that are 60 years old or older.2, 17 During the time period of interest, recipients who were 60 years old or older represented 17% of all liver transplants performed, and they were the fastest growing age group in liver transplantation; this makes them a relevant clinical cohort for examination. Although recent analyses have demonstrated a reduction in survival in older recipients, particularly those over 65 years of age,11 risk factors specific to this high-risk population have not been well defined. In addition, there is a general acceptance that age, in and of itself, becomes a relevant subjective factor in donor-recipient matching as patients enter the seventh decade of life. The purpose of this study was to determine the objective relevance of this age milestone.

Because the age of recipients and donors was a major focus of this study, we assessed outcomes by subdividing the older recipient cohort and comparing the survival rates of recipients in their sixties with those of recipients in their seventies and eighties. The multivariate analysis determined that, among recipients 60 years old or older, age over 70 years was not an independent prognostic factor. This finding is likely due to heavy selection bias for octogenarians with fewer risk factors in comparison with their younger counterparts. The detailed analysis of donor age determined that risk first increased at the age of 50 years and progressed with increasing donor age. These data differ only slightly from other analyses that did not focus on older recipients but did identify donor age over 40 years as a significant risk factor.22

Currently, the most commonly used prognostic measure in liver transplantation is the MELD score. This score, derived from measures of serum creatinine, bilirubin, and the international normalized ratio, has been used by UNOS since 2002 to prioritize listed patients. The MELD score was originally validated as a prognostic index for mortality in patients with cirrhosis undergoing transjugular intrahepatic portosystemic shunting procedures.23 Subsequently, it has been shown to have prognostic value in a variety of medical and surgical treatments of liver disease.24-26 Although the MELD score has a strong association with liver transplant waitlist mortality, its value as a prognostic indicator for posttransplant patient survival is controversial.27-29 Furthermore, the ability of the MELD score to predict posttransplant survival in patients over the age of 60 years has not been examined. Not surprisingly, our analysis of the prognostic value of the pretransplant laboratory MELD score determined that the MELD score was a poor posttransplant prognostic indicator in this patient population.

The current study calculated the prognostic value of multiple recipient, donor, and combined variables for identifying a set of MELD-independent prognostic factors for posttransplant patient survival. To make the results of this study readily applicable to clinical practice, the analyzed variables were generally limited to those that would be available in the pretransplant setting. This analysis determined that the core set of variables most strongly associated with posttransplant survival in patients 60 years old or older were recipient mechanical ventilation, recipient diabetes mellitus, recipient positive HCV serology, recipient serum creatinine levels of 1.6 mg/dL or higher, and combined recipient and donor age of 120 years or more.

When the final set of clinically significant variables was used as the basis for the novel ORPS system, patient survival could be stratified over a wide range of patient presentations independent of the UNOS region or donor proximity. For example, patients with none of these major factors for poor prognosis experienced a 5-year survival rate of 75%, whereas patients with more than 2 of these factors experienced a 5-year survival rate of only 46%. Furthermore, the survival differences between ORPS system–aggregated groups were statistically significant at the 1-, 3-, and 5-year time points (all P < 0.001).

The results of our OPTN data set analysis parallel those of the analysis that generated the recently popularized donor risk index (DRI).22 Although the data analysis that generated the DRI did not focus on recipients over the age of 60 years, the findings in these 2 studies are similar. One of the 7 independent prognostic factors in the DRI is donor age, with an escalating risk ratio in each decade of donor life over the age of 40 years. In the analysis used to develop the DRI, the variable with the highest risk ratio in the DRI was donor age over 70 years (relative risk = 1.65). In our analysis, donor age became an independent prognostic factor in each decade of donor life over 50 years, with a clear breakpoint in posttransplant survival for recipient and donor age combinations greater than 120 years.

With 2 of the 5 ORPS components fixed (HCV and diabetes), the ORPS can be used by liver transplant selection committees to assess transplant candidacy as early as the time of recipient listing. For listed patients, the 3 dynamic components of the ORPS (mechanical ventilation, serum creatinine, and combined recipient and donor age of 120 years or more) gain value and assist the transplant team in assessing the risk of posttransplant mortality at the time of organ offer. The application of the ORPS during the continuum from candidate listing to organ offer, therefore, could facilitate allocation of the valuable commodity of liver allografts to candidates with a higher likelihood of long-term survival. After appropriate validation, the ORPS system could be used by liver transplant teams, in concert with other risk scores such as the DRI, to prospectively identify futile donor-recipient combinations within this high-risk population.

Despite its inherent limitations, the OPTN database remains the most complete data set for the analysis of outcomes for liver transplant patients. The database yielded over 8000 patient records with extensive information regarding possible risk factors for poor outcomes. Previous analyses of outcomes from this database have accurately guided the development of treatment strategies for patients with end-stage liver disease who are candidates for liver transplantation. This database, therefore, is the best tool available to the transplant community for assessing outcome data and facilitates a determination of suitability for potential donor-recipient combinations.

In calculating the impact of these prognostic factors on outcomes, we used a model for posttransplant benefit/deficit that compared posttransplant outcomes for patients with or without various risk factors. The risk of death associated with remaining on the waitlist without transplant was not factored into the model. We recognize that patients with multiple risk factors for poor posttransplant outcome also have a poorer waitlist prognosis. When the scarce resource of donor allografts is being allocated, however, the decision to allocate an organ to a high-risk patient also increases the waitlist mortality of other candidates with fewer risk factors.27 In this setting, it is important to recognize the constellation of factors associated with the worst posttransplant outcomes, particularly when expected 5-year survival rates fall below the 50% level of futility.30, 31

In summary, this analysis determined that the strongest prognostic factor for liver transplant recipients 60 years old or older was the allocation of a liver allograft from an older donor that created a recipient and donor age combination equal to or greater than 120 years. Liver transplants with a combined recipient and donor age of at least 120 years were associated with a 20% reduction in postoperative survival. Furthermore, the influence of transplantation with a recipient and donor age combination of at least 120 years was found across all patient groups (postoperative survival deficit range = 12.7%-20.9%), with the most dramatic impact occurring in patients with 2 or more other major factors for poor prognosis. In light of these data, caution should be used in accepting allografts for recipients 60 years old or older from donors that qualify as extended criteria donors on the basis of older age, particularly when the older recipient has a mechanical ventilation requirement, HCV infection, diabetes mellitus, and/or renal insufficiency.

By accurately predicting waitlist mortality, the MELD system has led to significant improvements in the impartiality of the liver allograft allocation process. However, accumulating data show that the MELD system has poor prognostic value for posttransplant survival, particularly in certain patient groups. For example, our study has determined that there are multiple MELD-independent clinical variables that have important prognostic significance in recipients over 60 years old. The strongest of these factors is a combined recipient and donor age of at least 120 years. Further assessment of postoperative benefit/detriment calculations for similarly constructed prognostic systems may advance the field toward specific recipient-donor matching that maximizes positive outcomes and most fairly allocates resources.

Acknowledgements

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES

The authors recognize Dr. Natasha Becker for her contribution to project database management and statistical support and Ms. Brigitte Taylor for her administrative support in the preparation of this article.

REFERENCES

  1. Top of page
  2. Abstract
  3. PATIENTS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. REFERENCES