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Regional Variation in Appropriateness of Non-Hepatocellular Carcinoma Model for End-Stage Liver Disease Exception
Journal Pre-proof Regional Variation in Appropriateness of Non-Hepatocellular Carcinoma Model for End-Stage Liver Disease Exception Robert M. Cannon, MD, FACS, Eric G. Davis, MD, FACS, David S. Goldberg, MD, Raymond J. Lynch, MD, FACS, Malay B. Shah, MD, FACS, Jayme E. Locke, MD, FACS, Kelly M. McMasters, MD, FACS, Christopher M. Jones, MD, FACS PII:
S1072-7515(20)30107-1
DOI:
https://doi.org/10.1016/j.jamcollsurg.2019.12.022
Reference:
ACS 9717
To appear in:
Journal of the American College of Surgeons
Received Date: 15 December 2019 Accepted Date: 16 December 2019
Please cite this article as: Cannon RM, Davis EG, Goldberg DS, Lynch RJ, Shah MB, Locke JE, McMasters KM, Jones CM, Regional Variation in Appropriateness of Non-Hepatocellular Carcinoma Model for End-Stage Liver Disease Exception, Journal of the American College of Surgeons (2020), doi: https://doi.org/10.1016/j.jamcollsurg.2019.12.022. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc. on behalf of the American College of Surgeons.
Regional Variation in Appropriateness of Non-Hepatocellular Carcinoma Model for EndStage Liver Disease Exception Robert M Cannon, MD, FACS1, Eric G Davis, MD, FACS2, David S Goldberg, MD3, Raymond J Lynch, MD, FACS4, Malay B Shah, MD, FACS5, Jayme E Locke, MD, FACS1, Kelly M McMasters, MD, FACS2, Christopher M Jones, MD, FACS2 1: Department of Surgery, University of Alabama at Birmingham, Birmingham, AL 2: Hiram C Polk Jr MD Department of Surgery, University of Louisville, Louisville, KY 3: Department of Medicine, University of Miami, Miami, FL 4: Department of Surgery, Emory University, Atlanta, GA 5: Department of Surgery, University of Kentucky, Lexington, KY Disclosure Information: Nothing to disclose. Disclaimer: The data reported here have been supplied by the United Network for Organ Sharing as the contractor for the Organ Procurement and Transplantation Network (OPTN). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the OPTN or the US Government. Presented at the Southern Surgical Association 131st Annual Meeting, Hot Springs, VA, December 2019.
Correspondence Robert M Cannon, MD FACS 748 LHRB 701 19th Street South, Birmingham, AL 35294 Email: [email protected] Phone: 404-405-9329
Brief Title: Regional Variation in MELD Exception
Background: Patients thought to be at greater risk of liver waitlist dropout than their laboratory MELD (lMELD) score reflects are commonly given MELD exceptions, where a higher allocation MELD (aMELD) score is assigned that is thought to reflect the patient’s risk. This study was undertaken to determine whether exceptions for reasons other than hepatocellular carcinoma (HCC) are justified, and whether exception aMELD scores appropriately estimate risk. Methods: Adult primary liver transplant candidates listed in the current era of liver allocation in the UNOS database were analyzed. Patients granted non-HCC related MELD exceptions and those without MELD exceptions were compared. Rates of waitlist dropout and liver transplantation were analyzed using cause-specific hazards regression, with separate models fitted to adjust for lMELD and aMELD. Results: There were 29,243 patients, with 2,555 in the exception group. Nationally, exception patients were more likely to dropout (HR: 1.451.601.76; p<0.001) or undergo liver transplant (HR: 3.323.493.67;
p<0.001) than their lMELD adjusted counterparts. Adjusting for aMELD, exception
patients were less likely to dropout (HR: 0.700.770.85; p<0.001) and less likely to undergo liver transplant (HR: 0.720.760.80 ; p<0.001). Exception patients were not at significantly increased risk of waitlist dropout when adjusted for lMELD in 4 of 11 UNOS regions Conclusions: Despite appropriate utilization of non-HCC MELD exceptions on a national level, patients with non-HCC MELD exceptions were awarded inappropriately high priority for transplantation in many regions. This highlights the need to consider local conditions faced by transplant candidates when estimating waitlist mortality and determining priority for transplantation.
Introduction Geographic variation in access to transplantation has been the subject of ongoing and heated debate for decades1, particularly in light of the Department of Health and Human Services 1998 Final Rule stating that organ allocation “shall not be based on the candidate’s place of residence or place of listing”2. This debate in liver transplantation has focused nearly exclusively on variation in model for end-stage liver Disease (MELD) scores across donation service areas (DSAs) as the metric for geographic disparity3-7. Studies have cited a difference of over 10 points in mean MELD at transplant between different DSAs, which proponents of broadening liver distribution argue exposes patients to differing risks of waitlist mortality based on where they live3,6,7. A major flaw in this interpretation of geographic disparity in liver transplant allocation is the artificial elevation of MELD scores secondary to the addition of MELD exception points. Patients who are considered to be at higher risk of waitlist mortality than is reflected by their laboratory MELD score are eligible to have exception points added to their calculated MELD score in order to be given priority for transplant though to more accurately reflect their mortality risk. The MELD score assigned based on exception points becomes the allocation MELD, upon which priority on the liver transplant waitlist is based. Perhaps the most well-known reason for MELD exception points is hepatocellular carcinoma (HCC), as many patients with HCC have compensated cirrhosis, but face a risk of waitlist dropout and mortality from tumor progression that is not reflected in their calculated MELD score8,9. Aside from HCC, MELD exceptions may be granted for a wide variety of diagnoses including primary sclerosing cholangitis, hepatopulmonary syndrome, polycystic liver disease, and gastrointestinal bleeding10-14.
Inflation of MELD scores by application of exception points may potentially drive much of the perceived geographic disparity in regional median MELD scores at transplant15. If MELD exceptions accurately reflect the risk faced by liver transplant candidates, then allocation MELD scores are indeed a valid measure of geographic disparity in liver transplantation. On the other hand, if patients with MELD exceptions aren’t truly at higher risk for waitlist removal than is already reflected in their calculated MELD score, then regional differences in allocation MELD scores paint a false picture of disparity and exception points provide these patients with an unjustified advantage in waitlist priority. This study was thus undertaken to examine whether patients with MELD exceptions for reasons other than HCC truly face a risk of waitlist removal that is greater than already reflected in their laboratory MELD score. Methods Patient Population This study utilized the United Network for Organ Sharing (UNOS) standard transplant research and analysis (STAR) file as of March 2019 to analyze adults registered on the liver transplant waitlist between June 18, 2013 and March 1, 2018. The start date for the study was chosen to coincide with the implementation of the current liver allocation system (Share 35) while the end date was chosen to allow at least 1 year of followup for all subjects. The primary groups of interest for comparison were those who never had a MELD exception approved (nonexception group) and those who had a MELD exception approved for reasons other than HCC (referred to as the MELD exception group going forward). Patients with a diagnosis of HCC were thus excluded. In order to eliminate the effect of listing at multiple centers, patients with waitlist registration records at multiple centers, as well as waitlist removal codes indicating transplant or transfer to another center were excluded. Patients who were simultaneously listed
for non-renal organs, those initially listed as inactive, patients who were ever Status 1, and previous transplant recipients were also excluded. Outcomes The primary outcomes of interest were liver transplantation and waitlist dropout. Both of these outcomes were measured from the time of addition to the transplant waitlist until the event of interest or last followup for those remaining on the list. The following waitlist removal codes were considered waitlist dropout: death, clinical deterioration (too sick for transplant), and refused transplant. Patients remaining on the waitlist at the end of followup as well as those with the following waitlist removal codes were censored: condition improved (transplant not needed), unable to contact, other. Patients were administratively censored after 36 months of followup. Statistical Analysis Comparison of baseline characteristics between the non-exception and MELD exception groups were performed using the Wilcoxon-Mann-Whitney test for continuous variables and chisquared analysis for categorical variables. Variables for comparison were chosen based on the Scientific Registry of Transplant Recipients (SRTR) risk adjustment models for liver transplant waitlist outcomes. Univariable cause-specific hazards regression was performed to examine the hazard ratios for liver transplant and waitlist dropout associated with MELD exception status. MELD exception status was treated as a time-dependent covariate in that patients were only counted in the MELD exception group after their first MELD exception request was approved. Prior to that these patients would be counted in the non-exception group. Subsequently, multivariable cause-specific hazards regression for each outcome of interest was performed, adjusting for laboratory MELD. MELD exception status was again
treated as a time-dependent covariate. The laboratory MELD score was also treated as a timedependent covariate, allowing the model to reflect changes in patients’ natural MELD scores over the course of followup. These multivariable analyses were then repeated, substituting allocation MELD in the model for laboratory MELD. To determine whether the hazard ratios associated with non-HCC MELD exceptions varies geographically, cause-specific hazards regression for liver transplant and waitlist dropout was performed for each UNOS region, adjusting separately for laboratory and allocation MELD as above in the overall analysis. The models using laboratory MELD allowed for examination of whether exception patients were truly at a higher risk of waiting list dropout than was reflected in their laboratory MELD score, as well as to what degree MELD exception status increased their transplant rate over candidates with similar laboratory MELD scores. The model using allocation MELD, on the other hand, allowed examination of whether exception patients were advantaged or disadvantaged compared to non-exception patients with laboratory MELD scores similar to the assigned exception scores. Cause specific hazards rather than subdistribution hazards for competing risks were modeled as the hazard ratios obtained in this setting are more appropriate for etiologic (as opposed to prognostic) studies. We did not adjust for factors other than MELD in the analyses as the liver allocation policy does not utilize any other factors to estimate medical urgency; rather, all risk of waitlist mortality is assumed to be captured by the MELD score. In order to determine whether the reason for MELD exception was an important factor, the above analyses were repeated in two separate subgroups. The first subgroup analyzed were those with MELD exceptions for nonstandard diagnoses. The second subgroup analyzed were those with MELD exceptions for hepatopulmonary syndrome (HPS) meeting standard UNOS criteria. These two subgroups were chosen on the basis that they represented the two most
common non-HCC reasons for MELD exception in the study cohort. As with the regional analyses above, the models were only adjusted for laboratory and allocation MELD scores. The comparison group in both subanalyses was the same non-exception cohort utilized for the main analysis. Finally, analysis of nonstandard MELD exceptions was performed with stratification by UNOS region. There was not sufficient sample size to duplicate the regional analysis in HPS patients. The following variables had missing data: BMI at listing (n=33), working for income at time of waitlist registration (n=10). Such a low fraction of missing data is unlikely to introduce significant bias, so complete case analysis was performed. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC). P-values <0.05 were considered significant. The institutional review board of the University of Louisville determined that the study meets criteria for exemption from review. Results Analysis of All Non-HCC MELD Exceptions There were 29,423 patients included in the study cohort, including 2,555 in the exception group and 26,868 in the non-exception group. The most common reasons for MELD exceptions were non-standard exceptions (73.7% of exceptions), followed by hepatopulmonary syndrome (18.6% of exceptions) (Table 1). The median time from registration on the waitlist until initial MELD exception approval was 0.6 (0.2 - 3.2) months. The percentage of patients in the study with MELD exceptions by region ranged from 4.1% to 15.2% (Figure 1). Differences in clinical characteristics between the non- exception and MELD exception cohorts are presented in table 2, and the primary liver diagnosis for each group is listed in table
3. The median laboratory MELD score in the non-exception group at the time of waitlist registration was significantly higher (20 vs. 13; p<0.001), as were the prevalence of features of hepatic decompensation such as ascites (83.3% vs. 58.5%), history of spontaneous bacterial peritonitis (10.4% vs. 5.7%), and hepatic encephalopathy (68.1% vs. 47.9%). The median allocation MELD at the end of followup was significantly higher in the exception group than in the non-exception group (25 vs. 21; p<0.001).There were higher percentages of Caucasians (77.2% vs. 72.4%) and Asians (3.5% vs. 2.8%) in the MELD exception cohort, and higher percentages of African Americans (7.7% vs. 7.2%) and Hispanics (15.5% vs. 10.8%) in the nonexception cohort. Patients in the MELD exception cohort were more likely to have private insurance (58.1% vs. 52.0%) and be working for income at the time of waitlist registration (27.4% vs. 18.2%). On unadjusted analysis, patients in the MELD exception group were more likely to undergo liver transplant (HR 2.24, 95% CI 2.13-2.35; p<0.001) and more likely to experience waitlist dropout (HR 1.21, 95% CI 1.10-1.33; p<0.001). After adjusting for baseline differences and laboratory MELD, exception patients remained significantly more likely to undergo liver transplant and to experience waitlist dropout. When the multivariable model was repeated using allocation MELD instead of laboratory MELD, the exception patients were significantly less likely to undergo liver transplant and less likely to experience waitlist dropout (Table 4). Regional Analysis When adjusted for laboratory MELD, exception patients were at statistically similar risk of waitlist dropout compared to their non-exception counterparts in regions 1, 4, 9, and 10, while they were at a higher risk of waitlist dropout in the remaining regions (Figure 2a). Exception patients were more likely to undergo liver transplant than their non-exception counterparts when
adjusted for laboratory MELD in all regions (Figure 2b). When adjusted for allocation MELD, exception patients were less likely to drop off the waitlist in regions 1, 2, 5, 7, and 9 and more likely to drop off the waitlist in regions 8, 10, and 11. The risk of waitlist dropout was similar between exception and non-exception patients in regions 3, 4, and 6 when adjusted for allocation MELD (Figure 2c). Exception patients were more likely to undergo liver transplant when adjusted for allocation MELD in regions 6 and 11, less likely to undergo liver transplant in regions 1, 2, 4, 5, and 7, and 9, and similarly likely to undergo transplant in regions 3, 8, and 10 (Figure 2d). The cause specific hazard rations for each region and their 95% confidence intervals are provided in the eTables. Subset Analysis of Nonstandard Exceptions There were 1,884 patients with nonstandard MELD exceptions. Comparison of the nonstandard exception subgroup and the non-exception group on baseline characteristics can be found in the eTables. The median laboratory MELD at listing for the nonstandard exception group was 13 (8-18; p<0.001 vs. non-exception group), while the median laboratory MELD at the end of followup for the nonstandard exception group was 15 (9-22; p<0.001 vs. nonexception group). The median allocation MELD at the end of followup for the nonstandard MELD exception group was 25 (19-30; p<0.001 vs. non-exception group). After adjusting for laboratory MELD, patients with nonstandard exceptions were at higher risk of waitlist dropout (HR 1.52, 95% CI 1.35-1.70; p<0.001) and more likely to undergo liver transplant (HR 3.52, 95% CI 3.32-3.73; p<0.001) than their non-exception counterparts. When adjusted for allocation MELD, the nonstandard exception patients were less likely to experience waitlist dropout (HR 0.77, 95% CI 0.69-0.87; p<0.001) and less likely to undergo liver transplant (HR 0.78, 95% CI
0.74-0.83; p<0.001). Laboratory and allocation MELD adjusted hazards for waitlist dropout and liver transplantation stratified by region are displayed in Figure 3. Subset Analysis of Exceptions for Hepatopulmonary Syndrome There were 475 patients with exceptions for HPS. Comparison of the HPS exception subgroup and the non-exception group on baseline characteristics can be found in the eTables. The median laboratory MELD at listing for the HPS group was 14 (11-17; p<0.001 vs. nonexception group), while the median laboratory MELD at the end of followup was 15 (12-20; p<0.001 vs. non-exception group). The median allocation MELD at the end of followup for the HPS group was 25 (22-29; p<0.001 vs. non-exception group). After adjusting for laboratory MELD, patients with HPS exceptions were significantly more likely to experience waitlist dropout (HR 1.97, 95% CI 1.59-2.44; p<0.001) and undergo liver transplant (HR 4.08, 95% CI 3.67-4.55; p<0.001) than their non-exception counterparts. When adjusted for allocation MELD, HPS exception patients were significantly less likely to experience waitlist dropout (HR 0.73, 95% CI 0.59-0.90; p=0.004) or undergo liver transplantation (HR 0.86, 95% CI 0.77-0.96; p=0.005). Discussion When taken in aggregate on a national level, patients with non-HCC MELD exception diagnoses indeed appear to face a greater risk of waitlist dropout than was captured by their laboratory MELD score. Despite having significantly lower laboratory MELD scores and fewer features of hepatic decompensation, exception patients faced a nearly 60% greater risk of waitlist dropout when adjusted for laboratory MELD, despite undergoing liver transplant at a rate over 3 fold greater than their non-exception counterparts. When adjusting for allocation MELD, the
exception patients in the study remained less likely to drop off the waitlist and were also less likely to be transplanted. This suggests that exception patients are not as sick as is implied by their assigned allocation MELD scores. While the national level analysis does indeed confirm the validity of awarding additional allocation MELD points to non-HCC exception patients, it still does not answer the question of whether use of regional allocation MELD rather than laboratory MELD at transplant paints an exaggerated picture of geographic disparity in liver transplantation. The prevalence of non-HCC MELD exception patients on the waitlist relative to non-exception patients in this study varied over three-fold by region, from a low of 4.1% in region 1 to a high of 15.2% in region 6. This variation could conceivably result from legitimate population level differences in the prevalence of patients with exception diagnoses, but it could just as likely result from variation in center and regional level practices. For the duration of this study, MELD exception requests were adjudicated by individual review boards in each region. Boards that were either more permissive or more strict could thus introduce significant variation into the percentage of patients in each region that receive MELD exception points. The recent introduction of a national liver review board with more clear guidelines for what patients are appropriate for MELD exceptions should eliminate differences in board behavior as a source of variation in MELD exception rates, although it still will not remove individual center level practice as a factor16. Aside from differences in the prevalence of patients with non-HCC MELD exceptions, this study also reveals regional differences in the appropriateness of awarding MELD exception points for non-HCC diagnoses. If exception patients in a given region undergo liver transplant do not dropout from the waitlist at a greater rate than was predicted by their MELD score, then it would not be appropriate to award them additional allocation MELD points. In Region 1, for
example, exception patients in this study have a hazard ratio for waitlist dropout of 0.97 (p=0.97) compared to their non-exception counterparts when adjusted for laboratory MELD. This suggests that the laboratory MELD adequately reflects risk for patients with non-HCC exception diagnoses in that region. As might be expected, the exception patients in Region 1 have a significantly lower than expected risk of waitlist dropout in this study (HR 0.22; p<0.001) when accounting for their allocation MELD score. Awarding exception points to these patients thus paints an inflated picture of the risk faced by these patients if one were to only look at their allocation MELD score. This pattern of similar risk of waitlist dropout when adjusted for laboratory MELD and less than expected risk of mortality when adjusted for allocation MELD is also present in Region 9. In other regions in this study, the validity of non-HCC MELD exceptions was confirmed by an increased risk of waitlist dropout for the exception group when adjusted for laboratory MELD. There still exists the possibility that the number of exception points awarded in such regions is excessive. In regions 2, 5, and 7, patients in the exception group in this study had significantly lower than expected risk of waitlist dropout when adjusted for allocation MELD. Thus, while some additional priority appears justified in these regions based on higher than expected waitlist dropout for exception patients when accounting for laboratory MELD, the number of MELD exception points awarded appears to overestimate the risk that these patients face while awaiting liver transplantation. Ideally, patients with MELD exception diagnoses would be awarded an allocation MELD score that equalizes their risk of waitlist dropout when compared to non-exception patients. This is the case in Regions 3 and 6 in this study, where nonHCC exception patients have greater than expected waitlist dropout when adjusted for laboratory
MELD, but statistically similar waitlist dropout rates compared to non-exception patients when adjusted for allocation MELD. Having established the presence of significant regional variation in the appropriateness of MELD exception points for non-HCC diagnoses, we must now focus on potential causes for this variation. One potential explanation lies in the vastly different social conditions and healthcare environments that patients with liver disease face depending upon where they live. The World Health Organization defines “the conditions in which people are born, grow, work, live, age, and the set of forces and systems shaping the conditions of daily life” as the social determinants of health, and these are widely recognized to play a fundamental role in a variety of chronic diseases and mortality17-20. Specific to liver transplantation, transplant center density, access to specialized gastroenterology care, urbanization, education, and the availability of adequate housing have all been demonstrated to play significant roles in liver transplant waitlist outcomes21-24. Ross and colleagues demonstrated that candidates from underprivileged areas with poor access to care are at significantly greater risk of waitlist mortality than implied by their MELD scores, and that the geographic distribution of these adverse conditions is uneven25. The regions in this study in which non-HCC MELD exceptions were found to be inappropriate or where the number of points awarded was excessive contain major US population centers such as the Northeast, California, and the upper Midwest including Chicago. This suggests that variation in social determinants of health and access to care may partially explain the findings of this study, although this is only speculation at this point as our data can’t directly support such a claim. This study has a number of limitations. The first is the relatively small number of patients with non-HCC MELD exceptions leading to imprecise estimates of hazard ratios for liver
transplant and waitlist dropout, particularly in less populated regions. The small sample size also limited our ability to adjust for factors other than exception status and either laboratory or allocation MELD score. This shortcoming is mitigated by the fact that the current allocation system also does not account for these factors, but rather bases all estimation of medical urgency for transplant on the MELD score (with the exception of acute liver failure patients, who are prioritized differently). Finally, there is significant variation in sociodemographic conditions within individual UNOS regions. Further study is thus needed on a more granular geographic level. Conclusions With the above limitations in mind, we have demonstrated significant regional variation in the appropriateness of additional allocation MELD points granted to patients with non-HCC exception diagnoses. There is already a significant body of literature demonstrating the fact that HCC is over prioritized for liver transplantation8,9,26-30. Thus, our study and the numerous studies on HCC exceptions demonstrate that regional differences in allocation MELD are not an appropriate metric of geographic disparity in liver transplantation, as many patients with exception diagnoses have a significantly lower risk of waitlist dropout than is suggested by their allocation MELD score. Better metrics are thus needed that can more accurately account for the vastly different conditions faced by patients in need of liver transplantation.
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Table 1: Reasons for Model for End-Stage Liver Disease Exceptions in the Study Cohort. Exception diagnosis
Data
n % Familial amyloidosis 38 1.5 Hepatopulmonary syndrome 475 18.6 Portopulmonary hypertension 132 5.2 Primary oxaluria 26 1.0 Non-standard 1,884 73.7 Percentages are of the 2,555 patients with non-hepatocellular carcinoma Model for End-Stage Liver Disease exceptions in the study.
Table 2: Differences between the no exception and non-HCC MELD exception cohorts in the study. Continuous variables are summarized as median (interquartile range) and categorical variables as count (percentage). Variable Age at listing, y, median (IQR) Sex, n (%) Female Male Ethnicity, n (%) Caucasian African American Hispanic Asian Native American/other ABO type, n (%) A AB B O BMI at listing, kg/m2, median (IQR) Diabetes at listing, n (%) Yes No Unknown Albumin at listing, mg/dL, median (IQR) Bilirubin at listing, mg/dL, median (IQR) Creatinine at listing, mg/dL, median (IQR) INR at listing, median (IQR) Serum sodium at listing, mEq/L, median (IQR) Laboratory MELD at listing, median (IQR) Laboratory MELD at end of follow up, median (IQR) Allocation MELD at the end of follow up, median (IQR) On dialysis at listing, n (%) Listed for simultaneous kidney transplant, n (%) On life support at listing, n (%) On ventilator at listing, n (%) History of portal vein thrombus at listing, n (%) Yes No
No exception (n=26,868) 56 (49-62)
Exception (n=2,555) 55 (46-62)
10730 (39.9) 16138 (60.1)
1228 (48.1) 1327 (51.9)
p Value <0.001* <0.001* <0.001*
19446 (72.4) 2067 (7.7) 4164 (15.5) 741 (2.8) 450 (1.7)
1972 (77.2) 185 (7.2) 275 (10.8) 89 (3.5) 34 (1.3)
10229 (38.1) 1050 (3.9) 3286 (12.2) 12303 (45.8) 28.5 (24.8-33.1)
967 (37.9) 99 (3.9) 326 (12.8) 1163 (45.5) 27.1 (23.7-31.6)
7641 (28.4) 19150 (71.3) 77 (0.3) 3.0 (2.6-6.5) 3.4 (1.8-8.4) 1.1 (0.8-1.7) 1.5 (1.3-2.0) 136 (133-139) 20 (15-28)
616 (24.1) 1934 (75.7) 5 (0.2) 3.3 (2.8-3.8) 1.6 (0.8-3.0) 0.9 (0.7-1.2) 1.2 (1.1-1.5) 137 (134-140) 13 (9-18)
<0.001* <0.001* <0.001* <0.001* <0.001* <0.001*
24 (16-34)
15 (10-22)
<0.001*
21 (8-33) 3067 (11.4) 3464 (12.9) 1049 (3.9) 673 (2.5)
25 (20-29) 119 (4.7) 298 (11.7) 20 (0.8) 8 (0.3)
<0.001* <0.001* 0.075 <0.001* <0.001* 0.468
1855 (6.9) 24833 (92.4)
183 (7.2) 2350 (92.0)
0.025*
<0.001* <0.001*
Unknown 180 (0.7) 22 (0.9) Ascites at listing, n (%) None 4503 (16.8) 1060 (41.5) Slight 13534 (50.4) 926 (36.2) Moderate 8831 (32.9) 569 (22.3) History of upper abdominal surgery, n (%) Yes 11086 (41.3) 1332 (52.1) No 15446 (57.5) 1196 (46.8) Unknown 336 (1.3) 27 (1.1) History of spontaneous bacterial peritonitis, n (%) Yes 2781 (10.4) 145 (5.7) No 23982 (88.9) 2397 (93.8) Unknown 195 (0.7) 13 (0.5) Encephalopathy at listing, n (%) None 8586 (32.0) 1330 (52.1) Grade 1-2 15878 (59.1) 1120 (43.8) Grade 3-4 2404 (9.0) 104 (4.1) History of TIPS shunt, n (%) Yes 2547 (9.5) 200 (7.8) No 23910 (89.0) 2319 (90.8) Unknown 411 (1.5) 36 (1.4) Insurance at listing, n (%) Private 13983 (52.0) 1484 (58.1) Public 12576 (46.8) 1027 (40.2) Other (such as self, charity, donation) 309 (1.2) 44 (1.7) Education at listing, n (%) Less than high school 1537 (5.7) 104 (4.1) Completed high school 17444 (64.9) 1512 (59.2) College degree 4827 (18.0) 588 (23.0) Postgraduate degree 1771 (6.6) 247 (9.7) Unknown 1289 (4.8) 104 (4.1) Working for income at listing, n (%) Yes 4896 (18.2) 698 (27.4) No 21561 (80.3) 1819 (71.3) Unknown 404 (1.5) 35 (1.4) IQR, interquartile range; MELD, Model for End-Stage Renal Disease; TIPS, transjugular intrahepatic portosystemic shunt
<0.001*
<0.001*
<0.001*
<0.001*
0.020*
<0.001*
<0.001*
<0.001*
Table 3: Liver diagnoses in the no exception and non-hepatocellular carcinoma Model for EndStage Liver Disease Exception Cohorts Variable Chronic viral hepatitis Cholestatic liver disease Alcoholic liver disease Nonalcoholic steatohepatitis Acute hepatic necrosis Autoimmune hepatitis Cryptogenic cirrhosis Metabolic disease Non-hepatocellular carcinoma tumor Polycystic liver disease Other
No exception (n=26,868) n % 4751 17.7 2237 8.3 9860 36.7
Exception (n=2,555) n % 308 12.1 410 16.1 408 16.0
5542
20.6
427
16.7
589 1152 1584 567
2.2 4.3 5.9 2.1
24 68 129 156
0.9 2.7 5.1 6.1
75
0.3
311
12.2
94 417
0.4 1.6
186 128
7.3 5.0
Table 4: Waitlist Outcomes Associated with Non-Hepatocellular Carcinoma Model for EndStage Liver Disease Exceptions Hazard ratio
Lower 95% CI
Upper 95% CI
Variable p Value Liver transplant Lab MELD model 3.489 3.316 3.671 <0.001* Allocation MELD 0.721 0.801 <0.001* model 0.760 Waitlist dropout Lab MELD model 1.599 1.449 1.764 <0.001* Allocation MELD model 0.770 0.696 0.852 <0.001* Cause specific hazard ratios are for the Model for End-Stage Liver Disease (MELD) exception group compared to the non-exception group, adjusted for either laboratory or allocation MELD, as indicated. MELD, Model for End-Stage Liver Disease
Figure legend Figure 1: Proportion of patients in the cohort with non-hepatocellular carcinoma exceptions relative to patients without a Model for End-Stage Liver Disease exception, by United Network for Organ Sharing region. Figure 2: Adjusted cause specific hazard ratios with 95% CI for liver transplant and waitlist dropout associated with non-hepatocellular carcinoma Model for End-Stage Liver Disease (MELD) exception status, stratified by region. (A) Hazard ratios for waitlist dropout, adjusted for laboratory MELD. (B) Hazard ratios for liver transplant, adjusted for laboratory MELD. (C) Hazard ratios for waitlist dropout, adjusted for allocation MELD. (D) Hazard ratios for liver transplant, adjusted for allocation MELD. Figure 3: Adjusted cause specific hazard ratios with 95% confidence intervals for liver transplant and waitlist dropout associated with non-standard Model for End-Stage Liver Disease (MELD) exceptions, stratified by region. (A) Hazard ratios for waitlist dropout, adjusted for laboratory MELD. (B) Hazard ratios for liver transplant, adjusted for laboratory MELD. (C) Hazard ratios for waitlist dropout, adjusted for allocation MELD. (D) Hazard ratios for liver transplant, adjusted for allocation MELD.
Precis Model End Stage Liver Disease exception for non-hepatocellular carcinoma diagnoses overprioritize patients for liver transplant in several United Network for Organ Sharing regions, painting a false picture of geographic disparity.
eTable 1: Adjusted Hazard Ratios for Liver Transplant Associated with Model for End-Stage Liver Disease Exception By Region Variable Region 1 Lab MELD model Allocation MELD model Region 2 Lab MELD model Allocation MELD model Region 3 Lab MELD model Allocation MELD model Region 4 Lab MELD model Allocation MELD model Region 5 Lab MELD model Allocation MELD model Region 6 Lab MELD model Allocation MELD model Region 7 Lab MELD model Allocation MELD model Region 8 Lab MELD model Allocation MELD model Region 9 Lab MELD model Allocation MELD model Region 10 Lab MELD model Allocation MELD model Region 11 Lab MELD model Allocation MELD model
Hazard ratio
Lower 95% CI
Upper 95% CI
p Value
2.611 0.4
1.888 0.287
3.611 0.558
<0.001* <0.001*
4.242 0.449
3.61 0.378
4.985 0.533
<0.001* <0.001*
3.779 1.102
3.36 0.981
4.25 1.238
<0.001* 0.103
2.877 0.682
2.369 0.559
3.495 0.831
<0.001* <0.001*
2.641 0.438
2.181 0.361
3.198 0.531
<0.001* <0.001*
5.179 1.594
4.081 1.246
6.573 2.037
<0.001* <0.001*
2.594 0.452
2.169 0.376
3.101 0.544
<0.001* <0.001*
4.416 0.962
3.663 0.794
5.325 1.165
<0.001* 0.69
3.627 0.701
2.921 0.56
4.503 0.877
<0.001* 0.002*
3.571 0.98
3.101 0.849
4.113 1.131
<0.001* 0.779
3.126 1.198
2.719 1.042
3.594 1.378
<0.001* <0.001*
*Statistically significant MELD, Model for End-Stage Liver Disease
eTable 2: Adjusted Hazard Ratios for Waitlist Dropout Associated with Model for End-Stage Liver Disease Exception by Region Hazard ratio Lower 95% CI
Variable Region 1 Lab MELD model 0.97 Allocation MELD model 0.222 Region 2 Lab MELD model 1.726 Allocation MELD model 0.645 Region 3 Lab MELD model 2.339 Allocation MELD model 1.078 Region 4 Lab MELD model 1.209 Allocation MELD model 0.789 Region 5 Lab MELD model 1.395 Allocation MELD model 0.496 Region 6 Lab MELD model 1.851 Allocation MELD model 1.575 Region 7 Lab MELD model 1.843 Allocation MELD model 0.477 Region 8 Lab MELD model 1.776 Allocation MELD model 1.718 Region 9 Lab MELD model 1.109 Allocation MELD model 0.316 Region 10 Lab MELD model 1.39 Allocation MELD model 1.705 Region 11 Lab MELD model 2.174 Allocation MELD model 1.922 *Statistically significant MELD, Model for End-Stage Liver Disease
Upper 95% CI
p Value
0.479 0.109
1.965 0.453
0.97 <0.001*
1.288 0.475
2.313 0.876
<0.001* 0.005*
1.761 0.812
3.108 1.43
<0.001* 0.604
0.908 0.59
1.608 1.054
0.194 0.109
1.037 0.367
1.875 0.669
0.028* <0.001*
1.177 0.993
2.911 2.498
0.008* 0.054
1.4 0.36
2.427 0.63
<0.001* <0.001*
1.222 1.178
2.583 2.504
0.003* 0.005*
0.752 0.213
1.636 0.47
0.602 <0.001*
0.947 1.153
2.038 2.523
0.092 0.008*
1.638 1.449
2.886 2.548
<0.001* <0.001*
eTable 3: Adjusted Hazard Ratios for Liver Transplant Associated with Non-Standard Model for End-Stage Liver Disease Exception by Region Hazard Variable ratio Lower 95% CI Upper 95% CI p Value Region 1 Lab MELD model 5.272 2.865 9.701 <0.001* Allocation MELD model 0.388 0.211 0.714 0.002* Region 2 Lab MELD model 4.631 3.866 5.547 <0.001* Allocation MELD model 0.414 0.342 0.501 <0.001* Region 3 Lab MELD model 3.97 3.471 4.541 <0.001* Allocation MELD model 1.184 1.037 1.353 0.013* Region 4 Lab MELD model 2.303 1.825 2.906 <0.001* Allocation MELD model 0.626 0.495 0.792 <0.001* Region 5 Lab MELD model 2.105 1.653 2.68 <0.001* Allocation MELD model 0.421 0.332 0.535 <0.001* Region 6 Lab MELD model 4.784 3.714 6.162 <0.001* Allocation MELD model 1.648 1.273 2.134 <0.001* Region 7 Lab MELD model 2.861 2.319 3.531 <0.001* Allocation MELD model 0.457 0.369 0.566 <0.001* Region 8 Lab MELD model 4.136 3.364 5.087 <0.001* Allocation MELD model 0.968 0.785 1.194 0.762 Region 9 Lab MELD model 3.787 3.004 4.775 <0.001* Allocation MELD model 0.738 0.581 0.937 0.013* Region 10 Lab MELD model 3.822 3.258 4.484 <0.001* Allocation MELD model 0.987 0.841 1.16 0.878 Region 11 Lab MELD model 2.852 2.439 3.335 <0.001* Allocation MELD model 1.258 1.075 1.471 0.004* *Statistically significant MELD, Model for End-Stage Liver Disease
eTable 4: Adjusted Hazard Ratios For Waitlist Dropout Associated with Non-Standard Model for End-Stage Liver Disease Exception By Region Hazard ratio Lower 95% CI
Variable Region 1 Lab MELD model 0.837 Allocation MELD model 0.087 Region 2 Lab MELD model 1.697 Allocation MELD model 0.604 Region 3 Lab MELD model 2.289 Allocation MELD model 1.052 Region 4 Lab MELD model 0.976 Allocation MELD model 0.743 Region 5 Lab MELD model 1.078 Allocation MELD model 0.465 Region 6 Lab MELD model 1.707 Allocation MELD model 1.643 Region 7 Lab MELD model 2.107 Allocation MELD model 0.473 Region 8 Lab MELD model 1.816 Allocation MELD model 1.983 Region 9 Lab MELD model 0.886 Allocation MELD model 0.264 Region 10 Lab MELD model 1.43 Allocation MELD model 1.804 Region 11 Lab MELD model 1.955 Allocation MELD model 1.976 *Statistically significant MELD, Model for End-Stage Liver Disease
Upper 95% CI
p Value
1.117 0.012
6.009 0.623
0.86 0.015*
1.215 0.427
2.371 0.856
0.002* 0.005*
1.664 0.768
3.15 1.442
<0.001* 0.753
0.692 0.525
1.377 1.05
0.889 0.092
0.725 0.312
1.603 0.692
0.711 <0.001*
1.052 1.008
2.768 2.678
0.03* 0.046*
1.526 0.342
2.91 0.655
<0.001* <0.001*
1.216 1.327
2.711 2.964
0.004* 0.001*
0.553 1.164
1.417 0.425
0.612 <0.001*
0.931 1.166
2.195 2.791
0.102 0.008*
1.42 1.438
2.692 2.716
<0.001* <0.001*
eTable 5: Differences in the No Exception and Non-Standard Model for End-Stage Liver Disease Exception Cohorts
56 (49-62)
Non-standard exception (n=1,884) 55 (45-62)
10730 (39.9) 16138 (60.1)
886 (47.0) 998 (53.0)
No exception (n=26,868) Variable Age at listing, y, median (IQR) Sex, n (%) Female Male Ethnicity, n (%) Caucasian African American Hispanic Asian Native American/other ABO Type, n (%) A AB B O BMI at listing, kg/m2, median (IQR) Diabetes at listing, n (%) Yes No Unknown Albumin at listing, mg/dL, median (IQR) Bilirubin at listing, mg/dL, median (IQR) Creatinine at listing, mg/dL, median (IQR) INR at listing, median (IQR) Serum sodium at listing, mEq/L, median (IQR) Laboratory MELD at listing, median (IQR) Laboratory MELD at end of follow up, median (IQR) Allocation MELD at the end of follow up, median (IQR) On dialysis at listing, n (%) Listed for simultaneous kidney transplant, n (%) On life support at listing, n (%) On ventilator at listing, n (%)
p Value <0.001* <0.001*
<0.001* 19446 (72.4) 2067 (7.7) 4164 (15.5) 741 (2.8) 450 (1.7)
1453 (77.1) 163 (8.7) 171 (9.1) 78 (4.1) 19 (1.0) 0.002*
10229 (38.1) 1050 (3.9) 3286 (12.2) 12303 (45.8) 28.5 (24.8-33.1)
693 (36.8) 74 (3.9) 252 (13.4) 865 (45.9) 26.9 (23.6-31.3)
7641 (28.4) 19150 (71.3) 77 (0.3) 3.0 (2.6-6.5) 3.4 (1.8-8.4) 1.1 (0.8-1.7) 1.5 (1.3-2.0)
422 (22.4) 1459 (77.4) 3 (0.2) 3.3 (2.8-3.8) 1.4 (0.7-2.9) 0.9 (0.7-1.3) 1.2 (1.1-1.4)
<0.001* <0.001* <0.001* <0.001*
136 (133-139) 20 (15-28)
137 (134-140) 13 (8-18)
<0.001* <0.001*
24 (16-34)
15 (9-22)
<0.001*
21 (8-33) 3067 (11.4)
25 (19-30) 83 (4.4)
<0.001* <0.001*
3464 (12.9) 1049 (3.9) 673 (2.5)
236 (12.5) 10 (0.5) 7 (0.4)
0.646 <0.001* <0.001*
<0.001* <0.001*
History of portal vein thrombus at listing, n (%) Yes No Unknown Ascites at listing, n (%) None Slight Moderate History of upper abdominal surgery, n (%) Yes No Unknown History of spontaneous bacterial peritonitis, n (%) Yes No Unknown Encephalopathy at listing, n (%) None Grade 1-2 Grade 3-4 History of TIPS, n (%) Yes No Unknown Insurance at listing, n (%) Private Public Other (such as self, charity, donation) Education at listing, n (%) Less than high school Completed high school College degree Postgraduate degree Unknown Working for income at listing, n (%) Yes No Unknown *Statistically significant
0.343 1855 (6.9) 24833 (92.4) 180 (0.7)
132 (7.0) 1734 (92.0) 18 (1.0) <0.001*
4503 (16.8) 13534 (50.4) 8831 (32.9)
793 (42.1) 613 (32.5) 478 (25.4)
11086 (41.3) 15446 (57.5) 336 (1.3)
1022 (54.3) 838 (44.5) 24 (1.3)
2781 (10.4) 23982 (88.9) 195 (0.7)
112 (5.9) 1763 (93.6) 9 (0.5)
8586 (32.0) 15878 (59.1) 2404 (9.0)
1017 (54.0) 783 (41.6) 84 (4.5)
<0.001*
<0.001*
<0.001*
0.008* 2547 (9.5) 23910 (89.0) 411 (1.5)
138 (7.3) 1715 (91.0) 31 (1.7) <0.001*
13983 (52.0) 12576 (46.8) 309 (1.2)
1156 (61.4) 689 (36.6) 39 (2.1) <0.001*
1537 (5.7) 17444 (64.9) 4827 (18.0) 1771 (6.6) 1289 (4.8)
68 (3.6) 1076 (57.1) 461 (24.5) 198 (10.5) 81 (4.3)
4896 (18.2) 21561 (80.3) 404 (1.5)
579 (30.8) 1276 (67.8) 26 (1.4)
<0.001*
TIPS, transjugular intrahepatic portosystemic shunt
eTable 6: Differences in the No Exception and Hepatopulmonary Syndrome Model for EndStage Liver Disease Exception Cohorts
Variable Age at listing, y, median (IQR) Sex, n (%) Female Male Ethnicity, n (%) Caucasian African American Hispanic Asian Native American/other ABO Type, n (%) A AB B O BMI at listing, kg/m2, median (IQR) Diabetes at listing, n (%) Yes No Unknown Albumin at listing, mg/dL, median (IQR) Bilirubin at listing, mg/dL, median (IQR) Creatinine at listing, mg/dL, median (IQR) INR at listing, median (IQR) Serum sodium at listing, mEq/L, median (IQR) Laboratory MELD at listing, median (IQR) Laboratory MELD at end of follow up, median (IQR) Allocation MELD at the end of follow up, median (IQR) On dialysis at listing, n (%) Listed for simultaneous kidney transplant, n (%) On life support at listing, n (%)
No exception (n=26,868) 56 (49-62)
Hepatopulmonary syndrome exception (n=475) 57 (51-62)
10730 (39.9) 16138 (60.1)
248 (52.2) 227 (47.8)
p Value 0.085 <0.001*
<0.001* 19446 (72.4) 2067 (7.7) 4164 (15.5) 741 (2.8) 450 (1.7)
380 (80.0) 8 (1.7) 72 (15.2) 6 (1.3) 9 (1.9)
10229 (38.1) 1050 (3.9) 3286 (12.2) 12303 (45.8) 28.5 (24.8-33.1)
194 (40.8) 20 (4.2) 49 (10.3) 212 (44.6) 28.2 (24.3-32.7)
7641 (28.4) 19150 (71.3) 77 (0.3) 3.0 (2.6-6.5) 3.4 (1.8-8.4)
159 (33.5) 316 (66.5) 0 (0) 3.1 (2.8-3.5) 2.4 (1.6-3.4)
0.007* <0.001*
1.1 (0.8-1.7) 1.5 (1.3-2.0)
0.8 (0.7-1.0) 1.4 (1.2-1.5)
<0.001* <0.001*
136 (133-139)
138 (135-140)
<0.001*
20 (15-28)
14 (11-17)
<0.001*
24 (16-34)
15 (12-20)
<0.001*
21 (8-33) 3067 (11.4)
25 (22-29) 4 (0.8)
<0.001* <0.001*
3464 (12.9) 1049 (3.9)
10 (2.1) 9 (1.9)
<0.001* 0.024*
0.460
0.174 0.030*
On ventilator at listing, n (%) History of portal vein thrombus at listing, n (%) Yes No Unknown Ascites at listing, n (%) None Slight Moderate History of upper abdominal surgery, n (%) Yes No Unknown History of spontaneous bacterial peritonitis, n (%) Yes No Unknown Encephalopathy at listing, n (%) None Grade 1-2 Grade 3-4 History of TIPS, n (%) Yes No Unknown Insurance at listing, n (%) Private Public Other (such as self, charity, donation) Education at listing, n (%) Less than high school Completed high school College degree Postgraduate degree Unknown Working for income at listing, n (%) Yes No
673 (2.5)
1 (0.2)
1855 (6.9) 24833 (92.4) 180 (0.7)
41 (8.6) 433 (91.2) 1 (0.2)
0.001* 0.167
<0.001* 4503 (16.8) 13534 (50.4) 8831 (32.9)
170 (35.8) 237 (49.9) 68 (14.3) 0.005*
11086 (41.3) 15446 (57.5) 336 (1.3)
228 (48.0) 245 (51.6) 2 (0.4) 0.001*
2781 (10.4) 23982 (88.9) 195 (0.7)
24 (5.1) 448 (94.3) 3 (0.6) <0.001*
8586 (32.0) 15878 (59.1) 2404 (9.0)
191 (40.2) 267 (56.2) 17 (3.6) 0.468
2547 (9.5) 23910 (89.0) 411 (1.5)
44 (9.3) 427 (89.9) 4 (0.8) 0.237
13983 (52.0) 12576 (46.8)
234 (49.3) 238 (50.1)
309 (1.2)
3 (0.6)
1537 (5.7) 17444 (64.9) 4827 (18.0) 1771 (6.6) 1289 (4.8)
25 (5.3) 315 (66.3) 84 (17.7) 33 (7.0) 18 (3.8)
0.842
0.512 4896 (18.2) 21561 (80.3)
77 (16.2) 390 (82.1)
Unknown *Statistically significant
404 (1.5)
TIPS, transjugular intrahepatic portosystemic shunt
8 (1.7)
S1072-7515(20)30107-1
DOI:
https://doi.org/10.1016/j.jamcollsurg.2019.12.022
Reference:
ACS 9717
To appear in:
Journal of the American College of Surgeons
Received Date: 15 December 2019 Accepted Date: 16 December 2019
Please cite this article as: Cannon RM, Davis EG, Goldberg DS, Lynch RJ, Shah MB, Locke JE, McMasters KM, Jones CM, Regional Variation in Appropriateness of Non-Hepatocellular Carcinoma Model for End-Stage Liver Disease Exception, Journal of the American College of Surgeons (2020), doi: https://doi.org/10.1016/j.jamcollsurg.2019.12.022. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Inc. on behalf of the American College of Surgeons.
Regional Variation in Appropriateness of Non-Hepatocellular Carcinoma Model for EndStage Liver Disease Exception Robert M Cannon, MD, FACS1, Eric G Davis, MD, FACS2, David S Goldberg, MD3, Raymond J Lynch, MD, FACS4, Malay B Shah, MD, FACS5, Jayme E Locke, MD, FACS1, Kelly M McMasters, MD, FACS2, Christopher M Jones, MD, FACS2 1: Department of Surgery, University of Alabama at Birmingham, Birmingham, AL 2: Hiram C Polk Jr MD Department of Surgery, University of Louisville, Louisville, KY 3: Department of Medicine, University of Miami, Miami, FL 4: Department of Surgery, Emory University, Atlanta, GA 5: Department of Surgery, University of Kentucky, Lexington, KY Disclosure Information: Nothing to disclose. Disclaimer: The data reported here have been supplied by the United Network for Organ Sharing as the contractor for the Organ Procurement and Transplantation Network (OPTN). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy of or interpretation by the OPTN or the US Government. Presented at the Southern Surgical Association 131st Annual Meeting, Hot Springs, VA, December 2019.
Correspondence Robert M Cannon, MD FACS 748 LHRB 701 19th Street South, Birmingham, AL 35294 Email: [email protected] Phone: 404-405-9329
Brief Title: Regional Variation in MELD Exception
Background: Patients thought to be at greater risk of liver waitlist dropout than their laboratory MELD (lMELD) score reflects are commonly given MELD exceptions, where a higher allocation MELD (aMELD) score is assigned that is thought to reflect the patient’s risk. This study was undertaken to determine whether exceptions for reasons other than hepatocellular carcinoma (HCC) are justified, and whether exception aMELD scores appropriately estimate risk. Methods: Adult primary liver transplant candidates listed in the current era of liver allocation in the UNOS database were analyzed. Patients granted non-HCC related MELD exceptions and those without MELD exceptions were compared. Rates of waitlist dropout and liver transplantation were analyzed using cause-specific hazards regression, with separate models fitted to adjust for lMELD and aMELD. Results: There were 29,243 patients, with 2,555 in the exception group. Nationally, exception patients were more likely to dropout (HR: 1.451.601.76; p<0.001) or undergo liver transplant (HR: 3.323.493.67;
p<0.001) than their lMELD adjusted counterparts. Adjusting for aMELD, exception
patients were less likely to dropout (HR: 0.700.770.85; p<0.001) and less likely to undergo liver transplant (HR: 0.720.760.80 ; p<0.001). Exception patients were not at significantly increased risk of waitlist dropout when adjusted for lMELD in 4 of 11 UNOS regions Conclusions: Despite appropriate utilization of non-HCC MELD exceptions on a national level, patients with non-HCC MELD exceptions were awarded inappropriately high priority for transplantation in many regions. This highlights the need to consider local conditions faced by transplant candidates when estimating waitlist mortality and determining priority for transplantation.
Introduction Geographic variation in access to transplantation has been the subject of ongoing and heated debate for decades1, particularly in light of the Department of Health and Human Services 1998 Final Rule stating that organ allocation “shall not be based on the candidate’s place of residence or place of listing”2. This debate in liver transplantation has focused nearly exclusively on variation in model for end-stage liver Disease (MELD) scores across donation service areas (DSAs) as the metric for geographic disparity3-7. Studies have cited a difference of over 10 points in mean MELD at transplant between different DSAs, which proponents of broadening liver distribution argue exposes patients to differing risks of waitlist mortality based on where they live3,6,7. A major flaw in this interpretation of geographic disparity in liver transplant allocation is the artificial elevation of MELD scores secondary to the addition of MELD exception points. Patients who are considered to be at higher risk of waitlist mortality than is reflected by their laboratory MELD score are eligible to have exception points added to their calculated MELD score in order to be given priority for transplant though to more accurately reflect their mortality risk. The MELD score assigned based on exception points becomes the allocation MELD, upon which priority on the liver transplant waitlist is based. Perhaps the most well-known reason for MELD exception points is hepatocellular carcinoma (HCC), as many patients with HCC have compensated cirrhosis, but face a risk of waitlist dropout and mortality from tumor progression that is not reflected in their calculated MELD score8,9. Aside from HCC, MELD exceptions may be granted for a wide variety of diagnoses including primary sclerosing cholangitis, hepatopulmonary syndrome, polycystic liver disease, and gastrointestinal bleeding10-14.
Inflation of MELD scores by application of exception points may potentially drive much of the perceived geographic disparity in regional median MELD scores at transplant15. If MELD exceptions accurately reflect the risk faced by liver transplant candidates, then allocation MELD scores are indeed a valid measure of geographic disparity in liver transplantation. On the other hand, if patients with MELD exceptions aren’t truly at higher risk for waitlist removal than is already reflected in their calculated MELD score, then regional differences in allocation MELD scores paint a false picture of disparity and exception points provide these patients with an unjustified advantage in waitlist priority. This study was thus undertaken to examine whether patients with MELD exceptions for reasons other than HCC truly face a risk of waitlist removal that is greater than already reflected in their laboratory MELD score. Methods Patient Population This study utilized the United Network for Organ Sharing (UNOS) standard transplant research and analysis (STAR) file as of March 2019 to analyze adults registered on the liver transplant waitlist between June 18, 2013 and March 1, 2018. The start date for the study was chosen to coincide with the implementation of the current liver allocation system (Share 35) while the end date was chosen to allow at least 1 year of followup for all subjects. The primary groups of interest for comparison were those who never had a MELD exception approved (nonexception group) and those who had a MELD exception approved for reasons other than HCC (referred to as the MELD exception group going forward). Patients with a diagnosis of HCC were thus excluded. In order to eliminate the effect of listing at multiple centers, patients with waitlist registration records at multiple centers, as well as waitlist removal codes indicating transplant or transfer to another center were excluded. Patients who were simultaneously listed
for non-renal organs, those initially listed as inactive, patients who were ever Status 1, and previous transplant recipients were also excluded. Outcomes The primary outcomes of interest were liver transplantation and waitlist dropout. Both of these outcomes were measured from the time of addition to the transplant waitlist until the event of interest or last followup for those remaining on the list. The following waitlist removal codes were considered waitlist dropout: death, clinical deterioration (too sick for transplant), and refused transplant. Patients remaining on the waitlist at the end of followup as well as those with the following waitlist removal codes were censored: condition improved (transplant not needed), unable to contact, other. Patients were administratively censored after 36 months of followup. Statistical Analysis Comparison of baseline characteristics between the non-exception and MELD exception groups were performed using the Wilcoxon-Mann-Whitney test for continuous variables and chisquared analysis for categorical variables. Variables for comparison were chosen based on the Scientific Registry of Transplant Recipients (SRTR) risk adjustment models for liver transplant waitlist outcomes. Univariable cause-specific hazards regression was performed to examine the hazard ratios for liver transplant and waitlist dropout associated with MELD exception status. MELD exception status was treated as a time-dependent covariate in that patients were only counted in the MELD exception group after their first MELD exception request was approved. Prior to that these patients would be counted in the non-exception group. Subsequently, multivariable cause-specific hazards regression for each outcome of interest was performed, adjusting for laboratory MELD. MELD exception status was again
treated as a time-dependent covariate. The laboratory MELD score was also treated as a timedependent covariate, allowing the model to reflect changes in patients’ natural MELD scores over the course of followup. These multivariable analyses were then repeated, substituting allocation MELD in the model for laboratory MELD. To determine whether the hazard ratios associated with non-HCC MELD exceptions varies geographically, cause-specific hazards regression for liver transplant and waitlist dropout was performed for each UNOS region, adjusting separately for laboratory and allocation MELD as above in the overall analysis. The models using laboratory MELD allowed for examination of whether exception patients were truly at a higher risk of waiting list dropout than was reflected in their laboratory MELD score, as well as to what degree MELD exception status increased their transplant rate over candidates with similar laboratory MELD scores. The model using allocation MELD, on the other hand, allowed examination of whether exception patients were advantaged or disadvantaged compared to non-exception patients with laboratory MELD scores similar to the assigned exception scores. Cause specific hazards rather than subdistribution hazards for competing risks were modeled as the hazard ratios obtained in this setting are more appropriate for etiologic (as opposed to prognostic) studies. We did not adjust for factors other than MELD in the analyses as the liver allocation policy does not utilize any other factors to estimate medical urgency; rather, all risk of waitlist mortality is assumed to be captured by the MELD score. In order to determine whether the reason for MELD exception was an important factor, the above analyses were repeated in two separate subgroups. The first subgroup analyzed were those with MELD exceptions for nonstandard diagnoses. The second subgroup analyzed were those with MELD exceptions for hepatopulmonary syndrome (HPS) meeting standard UNOS criteria. These two subgroups were chosen on the basis that they represented the two most
common non-HCC reasons for MELD exception in the study cohort. As with the regional analyses above, the models were only adjusted for laboratory and allocation MELD scores. The comparison group in both subanalyses was the same non-exception cohort utilized for the main analysis. Finally, analysis of nonstandard MELD exceptions was performed with stratification by UNOS region. There was not sufficient sample size to duplicate the regional analysis in HPS patients. The following variables had missing data: BMI at listing (n=33), working for income at time of waitlist registration (n=10). Such a low fraction of missing data is unlikely to introduce significant bias, so complete case analysis was performed. All statistical analyses were performed using SAS version 9.4 (SAS Institute, Cary, NC). P-values <0.05 were considered significant. The institutional review board of the University of Louisville determined that the study meets criteria for exemption from review. Results Analysis of All Non-HCC MELD Exceptions There were 29,423 patients included in the study cohort, including 2,555 in the exception group and 26,868 in the non-exception group. The most common reasons for MELD exceptions were non-standard exceptions (73.7% of exceptions), followed by hepatopulmonary syndrome (18.6% of exceptions) (Table 1). The median time from registration on the waitlist until initial MELD exception approval was 0.6 (0.2 - 3.2) months. The percentage of patients in the study with MELD exceptions by region ranged from 4.1% to 15.2% (Figure 1). Differences in clinical characteristics between the non- exception and MELD exception cohorts are presented in table 2, and the primary liver diagnosis for each group is listed in table
3. The median laboratory MELD score in the non-exception group at the time of waitlist registration was significantly higher (20 vs. 13; p<0.001), as were the prevalence of features of hepatic decompensation such as ascites (83.3% vs. 58.5%), history of spontaneous bacterial peritonitis (10.4% vs. 5.7%), and hepatic encephalopathy (68.1% vs. 47.9%). The median allocation MELD at the end of followup was significantly higher in the exception group than in the non-exception group (25 vs. 21; p<0.001).There were higher percentages of Caucasians (77.2% vs. 72.4%) and Asians (3.5% vs. 2.8%) in the MELD exception cohort, and higher percentages of African Americans (7.7% vs. 7.2%) and Hispanics (15.5% vs. 10.8%) in the nonexception cohort. Patients in the MELD exception cohort were more likely to have private insurance (58.1% vs. 52.0%) and be working for income at the time of waitlist registration (27.4% vs. 18.2%). On unadjusted analysis, patients in the MELD exception group were more likely to undergo liver transplant (HR 2.24, 95% CI 2.13-2.35; p<0.001) and more likely to experience waitlist dropout (HR 1.21, 95% CI 1.10-1.33; p<0.001). After adjusting for baseline differences and laboratory MELD, exception patients remained significantly more likely to undergo liver transplant and to experience waitlist dropout. When the multivariable model was repeated using allocation MELD instead of laboratory MELD, the exception patients were significantly less likely to undergo liver transplant and less likely to experience waitlist dropout (Table 4). Regional Analysis When adjusted for laboratory MELD, exception patients were at statistically similar risk of waitlist dropout compared to their non-exception counterparts in regions 1, 4, 9, and 10, while they were at a higher risk of waitlist dropout in the remaining regions (Figure 2a). Exception patients were more likely to undergo liver transplant than their non-exception counterparts when
adjusted for laboratory MELD in all regions (Figure 2b). When adjusted for allocation MELD, exception patients were less likely to drop off the waitlist in regions 1, 2, 5, 7, and 9 and more likely to drop off the waitlist in regions 8, 10, and 11. The risk of waitlist dropout was similar between exception and non-exception patients in regions 3, 4, and 6 when adjusted for allocation MELD (Figure 2c). Exception patients were more likely to undergo liver transplant when adjusted for allocation MELD in regions 6 and 11, less likely to undergo liver transplant in regions 1, 2, 4, 5, and 7, and 9, and similarly likely to undergo transplant in regions 3, 8, and 10 (Figure 2d). The cause specific hazard rations for each region and their 95% confidence intervals are provided in the eTables. Subset Analysis of Nonstandard Exceptions There were 1,884 patients with nonstandard MELD exceptions. Comparison of the nonstandard exception subgroup and the non-exception group on baseline characteristics can be found in the eTables. The median laboratory MELD at listing for the nonstandard exception group was 13 (8-18; p<0.001 vs. non-exception group), while the median laboratory MELD at the end of followup for the nonstandard exception group was 15 (9-22; p<0.001 vs. nonexception group). The median allocation MELD at the end of followup for the nonstandard MELD exception group was 25 (19-30; p<0.001 vs. non-exception group). After adjusting for laboratory MELD, patients with nonstandard exceptions were at higher risk of waitlist dropout (HR 1.52, 95% CI 1.35-1.70; p<0.001) and more likely to undergo liver transplant (HR 3.52, 95% CI 3.32-3.73; p<0.001) than their non-exception counterparts. When adjusted for allocation MELD, the nonstandard exception patients were less likely to experience waitlist dropout (HR 0.77, 95% CI 0.69-0.87; p<0.001) and less likely to undergo liver transplant (HR 0.78, 95% CI
0.74-0.83; p<0.001). Laboratory and allocation MELD adjusted hazards for waitlist dropout and liver transplantation stratified by region are displayed in Figure 3. Subset Analysis of Exceptions for Hepatopulmonary Syndrome There were 475 patients with exceptions for HPS. Comparison of the HPS exception subgroup and the non-exception group on baseline characteristics can be found in the eTables. The median laboratory MELD at listing for the HPS group was 14 (11-17; p<0.001 vs. nonexception group), while the median laboratory MELD at the end of followup was 15 (12-20; p<0.001 vs. non-exception group). The median allocation MELD at the end of followup for the HPS group was 25 (22-29; p<0.001 vs. non-exception group). After adjusting for laboratory MELD, patients with HPS exceptions were significantly more likely to experience waitlist dropout (HR 1.97, 95% CI 1.59-2.44; p<0.001) and undergo liver transplant (HR 4.08, 95% CI 3.67-4.55; p<0.001) than their non-exception counterparts. When adjusted for allocation MELD, HPS exception patients were significantly less likely to experience waitlist dropout (HR 0.73, 95% CI 0.59-0.90; p=0.004) or undergo liver transplantation (HR 0.86, 95% CI 0.77-0.96; p=0.005). Discussion When taken in aggregate on a national level, patients with non-HCC MELD exception diagnoses indeed appear to face a greater risk of waitlist dropout than was captured by their laboratory MELD score. Despite having significantly lower laboratory MELD scores and fewer features of hepatic decompensation, exception patients faced a nearly 60% greater risk of waitlist dropout when adjusted for laboratory MELD, despite undergoing liver transplant at a rate over 3 fold greater than their non-exception counterparts. When adjusting for allocation MELD, the
exception patients in the study remained less likely to drop off the waitlist and were also less likely to be transplanted. This suggests that exception patients are not as sick as is implied by their assigned allocation MELD scores. While the national level analysis does indeed confirm the validity of awarding additional allocation MELD points to non-HCC exception patients, it still does not answer the question of whether use of regional allocation MELD rather than laboratory MELD at transplant paints an exaggerated picture of geographic disparity in liver transplantation. The prevalence of non-HCC MELD exception patients on the waitlist relative to non-exception patients in this study varied over three-fold by region, from a low of 4.1% in region 1 to a high of 15.2% in region 6. This variation could conceivably result from legitimate population level differences in the prevalence of patients with exception diagnoses, but it could just as likely result from variation in center and regional level practices. For the duration of this study, MELD exception requests were adjudicated by individual review boards in each region. Boards that were either more permissive or more strict could thus introduce significant variation into the percentage of patients in each region that receive MELD exception points. The recent introduction of a national liver review board with more clear guidelines for what patients are appropriate for MELD exceptions should eliminate differences in board behavior as a source of variation in MELD exception rates, although it still will not remove individual center level practice as a factor16. Aside from differences in the prevalence of patients with non-HCC MELD exceptions, this study also reveals regional differences in the appropriateness of awarding MELD exception points for non-HCC diagnoses. If exception patients in a given region undergo liver transplant do not dropout from the waitlist at a greater rate than was predicted by their MELD score, then it would not be appropriate to award them additional allocation MELD points. In Region 1, for
example, exception patients in this study have a hazard ratio for waitlist dropout of 0.97 (p=0.97) compared to their non-exception counterparts when adjusted for laboratory MELD. This suggests that the laboratory MELD adequately reflects risk for patients with non-HCC exception diagnoses in that region. As might be expected, the exception patients in Region 1 have a significantly lower than expected risk of waitlist dropout in this study (HR 0.22; p<0.001) when accounting for their allocation MELD score. Awarding exception points to these patients thus paints an inflated picture of the risk faced by these patients if one were to only look at their allocation MELD score. This pattern of similar risk of waitlist dropout when adjusted for laboratory MELD and less than expected risk of mortality when adjusted for allocation MELD is also present in Region 9. In other regions in this study, the validity of non-HCC MELD exceptions was confirmed by an increased risk of waitlist dropout for the exception group when adjusted for laboratory MELD. There still exists the possibility that the number of exception points awarded in such regions is excessive. In regions 2, 5, and 7, patients in the exception group in this study had significantly lower than expected risk of waitlist dropout when adjusted for allocation MELD. Thus, while some additional priority appears justified in these regions based on higher than expected waitlist dropout for exception patients when accounting for laboratory MELD, the number of MELD exception points awarded appears to overestimate the risk that these patients face while awaiting liver transplantation. Ideally, patients with MELD exception diagnoses would be awarded an allocation MELD score that equalizes their risk of waitlist dropout when compared to non-exception patients. This is the case in Regions 3 and 6 in this study, where nonHCC exception patients have greater than expected waitlist dropout when adjusted for laboratory
MELD, but statistically similar waitlist dropout rates compared to non-exception patients when adjusted for allocation MELD. Having established the presence of significant regional variation in the appropriateness of MELD exception points for non-HCC diagnoses, we must now focus on potential causes for this variation. One potential explanation lies in the vastly different social conditions and healthcare environments that patients with liver disease face depending upon where they live. The World Health Organization defines “the conditions in which people are born, grow, work, live, age, and the set of forces and systems shaping the conditions of daily life” as the social determinants of health, and these are widely recognized to play a fundamental role in a variety of chronic diseases and mortality17-20. Specific to liver transplantation, transplant center density, access to specialized gastroenterology care, urbanization, education, and the availability of adequate housing have all been demonstrated to play significant roles in liver transplant waitlist outcomes21-24. Ross and colleagues demonstrated that candidates from underprivileged areas with poor access to care are at significantly greater risk of waitlist mortality than implied by their MELD scores, and that the geographic distribution of these adverse conditions is uneven25. The regions in this study in which non-HCC MELD exceptions were found to be inappropriate or where the number of points awarded was excessive contain major US population centers such as the Northeast, California, and the upper Midwest including Chicago. This suggests that variation in social determinants of health and access to care may partially explain the findings of this study, although this is only speculation at this point as our data can’t directly support such a claim. This study has a number of limitations. The first is the relatively small number of patients with non-HCC MELD exceptions leading to imprecise estimates of hazard ratios for liver
transplant and waitlist dropout, particularly in less populated regions. The small sample size also limited our ability to adjust for factors other than exception status and either laboratory or allocation MELD score. This shortcoming is mitigated by the fact that the current allocation system also does not account for these factors, but rather bases all estimation of medical urgency for transplant on the MELD score (with the exception of acute liver failure patients, who are prioritized differently). Finally, there is significant variation in sociodemographic conditions within individual UNOS regions. Further study is thus needed on a more granular geographic level. Conclusions With the above limitations in mind, we have demonstrated significant regional variation in the appropriateness of additional allocation MELD points granted to patients with non-HCC exception diagnoses. There is already a significant body of literature demonstrating the fact that HCC is over prioritized for liver transplantation8,9,26-30. Thus, our study and the numerous studies on HCC exceptions demonstrate that regional differences in allocation MELD are not an appropriate metric of geographic disparity in liver transplantation, as many patients with exception diagnoses have a significantly lower risk of waitlist dropout than is suggested by their allocation MELD score. Better metrics are thus needed that can more accurately account for the vastly different conditions faced by patients in need of liver transplantation.
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Table 1: Reasons for Model for End-Stage Liver Disease Exceptions in the Study Cohort. Exception diagnosis
Data
n % Familial amyloidosis 38 1.5 Hepatopulmonary syndrome 475 18.6 Portopulmonary hypertension 132 5.2 Primary oxaluria 26 1.0 Non-standard 1,884 73.7 Percentages are of the 2,555 patients with non-hepatocellular carcinoma Model for End-Stage Liver Disease exceptions in the study.
Table 2: Differences between the no exception and non-HCC MELD exception cohorts in the study. Continuous variables are summarized as median (interquartile range) and categorical variables as count (percentage). Variable Age at listing, y, median (IQR) Sex, n (%) Female Male Ethnicity, n (%) Caucasian African American Hispanic Asian Native American/other ABO type, n (%) A AB B O BMI at listing, kg/m2, median (IQR) Diabetes at listing, n (%) Yes No Unknown Albumin at listing, mg/dL, median (IQR) Bilirubin at listing, mg/dL, median (IQR) Creatinine at listing, mg/dL, median (IQR) INR at listing, median (IQR) Serum sodium at listing, mEq/L, median (IQR) Laboratory MELD at listing, median (IQR) Laboratory MELD at end of follow up, median (IQR) Allocation MELD at the end of follow up, median (IQR) On dialysis at listing, n (%) Listed for simultaneous kidney transplant, n (%) On life support at listing, n (%) On ventilator at listing, n (%) History of portal vein thrombus at listing, n (%) Yes No
No exception (n=26,868) 56 (49-62)
Exception (n=2,555) 55 (46-62)
10730 (39.9) 16138 (60.1)
1228 (48.1) 1327 (51.9)
p Value <0.001* <0.001* <0.001*
19446 (72.4) 2067 (7.7) 4164 (15.5) 741 (2.8) 450 (1.7)
1972 (77.2) 185 (7.2) 275 (10.8) 89 (3.5) 34 (1.3)
10229 (38.1) 1050 (3.9) 3286 (12.2) 12303 (45.8) 28.5 (24.8-33.1)
967 (37.9) 99 (3.9) 326 (12.8) 1163 (45.5) 27.1 (23.7-31.6)
7641 (28.4) 19150 (71.3) 77 (0.3) 3.0 (2.6-6.5) 3.4 (1.8-8.4) 1.1 (0.8-1.7) 1.5 (1.3-2.0) 136 (133-139) 20 (15-28)
616 (24.1) 1934 (75.7) 5 (0.2) 3.3 (2.8-3.8) 1.6 (0.8-3.0) 0.9 (0.7-1.2) 1.2 (1.1-1.5) 137 (134-140) 13 (9-18)
<0.001* <0.001* <0.001* <0.001* <0.001* <0.001*
24 (16-34)
15 (10-22)
<0.001*
21 (8-33) 3067 (11.4) 3464 (12.9) 1049 (3.9) 673 (2.5)
25 (20-29) 119 (4.7) 298 (11.7) 20 (0.8) 8 (0.3)
<0.001* <0.001* 0.075 <0.001* <0.001* 0.468
1855 (6.9) 24833 (92.4)
183 (7.2) 2350 (92.0)
0.025*
<0.001* <0.001*
Unknown 180 (0.7) 22 (0.9) Ascites at listing, n (%) None 4503 (16.8) 1060 (41.5) Slight 13534 (50.4) 926 (36.2) Moderate 8831 (32.9) 569 (22.3) History of upper abdominal surgery, n (%) Yes 11086 (41.3) 1332 (52.1) No 15446 (57.5) 1196 (46.8) Unknown 336 (1.3) 27 (1.1) History of spontaneous bacterial peritonitis, n (%) Yes 2781 (10.4) 145 (5.7) No 23982 (88.9) 2397 (93.8) Unknown 195 (0.7) 13 (0.5) Encephalopathy at listing, n (%) None 8586 (32.0) 1330 (52.1) Grade 1-2 15878 (59.1) 1120 (43.8) Grade 3-4 2404 (9.0) 104 (4.1) History of TIPS shunt, n (%) Yes 2547 (9.5) 200 (7.8) No 23910 (89.0) 2319 (90.8) Unknown 411 (1.5) 36 (1.4) Insurance at listing, n (%) Private 13983 (52.0) 1484 (58.1) Public 12576 (46.8) 1027 (40.2) Other (such as self, charity, donation) 309 (1.2) 44 (1.7) Education at listing, n (%) Less than high school 1537 (5.7) 104 (4.1) Completed high school 17444 (64.9) 1512 (59.2) College degree 4827 (18.0) 588 (23.0) Postgraduate degree 1771 (6.6) 247 (9.7) Unknown 1289 (4.8) 104 (4.1) Working for income at listing, n (%) Yes 4896 (18.2) 698 (27.4) No 21561 (80.3) 1819 (71.3) Unknown 404 (1.5) 35 (1.4) IQR, interquartile range; MELD, Model for End-Stage Renal Disease; TIPS, transjugular intrahepatic portosystemic shunt
<0.001*
<0.001*
<0.001*
<0.001*
0.020*
<0.001*
<0.001*
<0.001*
Table 3: Liver diagnoses in the no exception and non-hepatocellular carcinoma Model for EndStage Liver Disease Exception Cohorts Variable Chronic viral hepatitis Cholestatic liver disease Alcoholic liver disease Nonalcoholic steatohepatitis Acute hepatic necrosis Autoimmune hepatitis Cryptogenic cirrhosis Metabolic disease Non-hepatocellular carcinoma tumor Polycystic liver disease Other
No exception (n=26,868) n % 4751 17.7 2237 8.3 9860 36.7
Exception (n=2,555) n % 308 12.1 410 16.1 408 16.0
5542
20.6
427
16.7
589 1152 1584 567
2.2 4.3 5.9 2.1
24 68 129 156
0.9 2.7 5.1 6.1
75
0.3
311
12.2
94 417
0.4 1.6
186 128
7.3 5.0
Table 4: Waitlist Outcomes Associated with Non-Hepatocellular Carcinoma Model for EndStage Liver Disease Exceptions Hazard ratio
Lower 95% CI
Upper 95% CI
Variable p Value Liver transplant Lab MELD model 3.489 3.316 3.671 <0.001* Allocation MELD 0.721 0.801 <0.001* model 0.760 Waitlist dropout Lab MELD model 1.599 1.449 1.764 <0.001* Allocation MELD model 0.770 0.696 0.852 <0.001* Cause specific hazard ratios are for the Model for End-Stage Liver Disease (MELD) exception group compared to the non-exception group, adjusted for either laboratory or allocation MELD, as indicated. MELD, Model for End-Stage Liver Disease
Figure legend Figure 1: Proportion of patients in the cohort with non-hepatocellular carcinoma exceptions relative to patients without a Model for End-Stage Liver Disease exception, by United Network for Organ Sharing region. Figure 2: Adjusted cause specific hazard ratios with 95% CI for liver transplant and waitlist dropout associated with non-hepatocellular carcinoma Model for End-Stage Liver Disease (MELD) exception status, stratified by region. (A) Hazard ratios for waitlist dropout, adjusted for laboratory MELD. (B) Hazard ratios for liver transplant, adjusted for laboratory MELD. (C) Hazard ratios for waitlist dropout, adjusted for allocation MELD. (D) Hazard ratios for liver transplant, adjusted for allocation MELD. Figure 3: Adjusted cause specific hazard ratios with 95% confidence intervals for liver transplant and waitlist dropout associated with non-standard Model for End-Stage Liver Disease (MELD) exceptions, stratified by region. (A) Hazard ratios for waitlist dropout, adjusted for laboratory MELD. (B) Hazard ratios for liver transplant, adjusted for laboratory MELD. (C) Hazard ratios for waitlist dropout, adjusted for allocation MELD. (D) Hazard ratios for liver transplant, adjusted for allocation MELD.
Precis Model End Stage Liver Disease exception for non-hepatocellular carcinoma diagnoses overprioritize patients for liver transplant in several United Network for Organ Sharing regions, painting a false picture of geographic disparity.
eTable 1: Adjusted Hazard Ratios for Liver Transplant Associated with Model for End-Stage Liver Disease Exception By Region Variable Region 1 Lab MELD model Allocation MELD model Region 2 Lab MELD model Allocation MELD model Region 3 Lab MELD model Allocation MELD model Region 4 Lab MELD model Allocation MELD model Region 5 Lab MELD model Allocation MELD model Region 6 Lab MELD model Allocation MELD model Region 7 Lab MELD model Allocation MELD model Region 8 Lab MELD model Allocation MELD model Region 9 Lab MELD model Allocation MELD model Region 10 Lab MELD model Allocation MELD model Region 11 Lab MELD model Allocation MELD model
Hazard ratio
Lower 95% CI
Upper 95% CI
p Value
2.611 0.4
1.888 0.287
3.611 0.558
<0.001* <0.001*
4.242 0.449
3.61 0.378
4.985 0.533
<0.001* <0.001*
3.779 1.102
3.36 0.981
4.25 1.238
<0.001* 0.103
2.877 0.682
2.369 0.559
3.495 0.831
<0.001* <0.001*
2.641 0.438
2.181 0.361
3.198 0.531
<0.001* <0.001*
5.179 1.594
4.081 1.246
6.573 2.037
<0.001* <0.001*
2.594 0.452
2.169 0.376
3.101 0.544
<0.001* <0.001*
4.416 0.962
3.663 0.794
5.325 1.165
<0.001* 0.69
3.627 0.701
2.921 0.56
4.503 0.877
<0.001* 0.002*
3.571 0.98
3.101 0.849
4.113 1.131
<0.001* 0.779
3.126 1.198
2.719 1.042
3.594 1.378
<0.001* <0.001*
*Statistically significant MELD, Model for End-Stage Liver Disease
eTable 2: Adjusted Hazard Ratios for Waitlist Dropout Associated with Model for End-Stage Liver Disease Exception by Region Hazard ratio Lower 95% CI
Variable Region 1 Lab MELD model 0.97 Allocation MELD model 0.222 Region 2 Lab MELD model 1.726 Allocation MELD model 0.645 Region 3 Lab MELD model 2.339 Allocation MELD model 1.078 Region 4 Lab MELD model 1.209 Allocation MELD model 0.789 Region 5 Lab MELD model 1.395 Allocation MELD model 0.496 Region 6 Lab MELD model 1.851 Allocation MELD model 1.575 Region 7 Lab MELD model 1.843 Allocation MELD model 0.477 Region 8 Lab MELD model 1.776 Allocation MELD model 1.718 Region 9 Lab MELD model 1.109 Allocation MELD model 0.316 Region 10 Lab MELD model 1.39 Allocation MELD model 1.705 Region 11 Lab MELD model 2.174 Allocation MELD model 1.922 *Statistically significant MELD, Model for End-Stage Liver Disease
Upper 95% CI
p Value
0.479 0.109
1.965 0.453
0.97 <0.001*
1.288 0.475
2.313 0.876
<0.001* 0.005*
1.761 0.812
3.108 1.43
<0.001* 0.604
0.908 0.59
1.608 1.054
0.194 0.109
1.037 0.367
1.875 0.669
0.028* <0.001*
1.177 0.993
2.911 2.498
0.008* 0.054
1.4 0.36
2.427 0.63
<0.001* <0.001*
1.222 1.178
2.583 2.504
0.003* 0.005*
0.752 0.213
1.636 0.47
0.602 <0.001*
0.947 1.153
2.038 2.523
0.092 0.008*
1.638 1.449
2.886 2.548
<0.001* <0.001*
eTable 3: Adjusted Hazard Ratios for Liver Transplant Associated with Non-Standard Model for End-Stage Liver Disease Exception by Region Hazard Variable ratio Lower 95% CI Upper 95% CI p Value Region 1 Lab MELD model 5.272 2.865 9.701 <0.001* Allocation MELD model 0.388 0.211 0.714 0.002* Region 2 Lab MELD model 4.631 3.866 5.547 <0.001* Allocation MELD model 0.414 0.342 0.501 <0.001* Region 3 Lab MELD model 3.97 3.471 4.541 <0.001* Allocation MELD model 1.184 1.037 1.353 0.013* Region 4 Lab MELD model 2.303 1.825 2.906 <0.001* Allocation MELD model 0.626 0.495 0.792 <0.001* Region 5 Lab MELD model 2.105 1.653 2.68 <0.001* Allocation MELD model 0.421 0.332 0.535 <0.001* Region 6 Lab MELD model 4.784 3.714 6.162 <0.001* Allocation MELD model 1.648 1.273 2.134 <0.001* Region 7 Lab MELD model 2.861 2.319 3.531 <0.001* Allocation MELD model 0.457 0.369 0.566 <0.001* Region 8 Lab MELD model 4.136 3.364 5.087 <0.001* Allocation MELD model 0.968 0.785 1.194 0.762 Region 9 Lab MELD model 3.787 3.004 4.775 <0.001* Allocation MELD model 0.738 0.581 0.937 0.013* Region 10 Lab MELD model 3.822 3.258 4.484 <0.001* Allocation MELD model 0.987 0.841 1.16 0.878 Region 11 Lab MELD model 2.852 2.439 3.335 <0.001* Allocation MELD model 1.258 1.075 1.471 0.004* *Statistically significant MELD, Model for End-Stage Liver Disease
eTable 4: Adjusted Hazard Ratios For Waitlist Dropout Associated with Non-Standard Model for End-Stage Liver Disease Exception By Region Hazard ratio Lower 95% CI
Variable Region 1 Lab MELD model 0.837 Allocation MELD model 0.087 Region 2 Lab MELD model 1.697 Allocation MELD model 0.604 Region 3 Lab MELD model 2.289 Allocation MELD model 1.052 Region 4 Lab MELD model 0.976 Allocation MELD model 0.743 Region 5 Lab MELD model 1.078 Allocation MELD model 0.465 Region 6 Lab MELD model 1.707 Allocation MELD model 1.643 Region 7 Lab MELD model 2.107 Allocation MELD model 0.473 Region 8 Lab MELD model 1.816 Allocation MELD model 1.983 Region 9 Lab MELD model 0.886 Allocation MELD model 0.264 Region 10 Lab MELD model 1.43 Allocation MELD model 1.804 Region 11 Lab MELD model 1.955 Allocation MELD model 1.976 *Statistically significant MELD, Model for End-Stage Liver Disease
Upper 95% CI
p Value
1.117 0.012
6.009 0.623
0.86 0.015*
1.215 0.427
2.371 0.856
0.002* 0.005*
1.664 0.768
3.15 1.442
<0.001* 0.753
0.692 0.525
1.377 1.05
0.889 0.092
0.725 0.312
1.603 0.692
0.711 <0.001*
1.052 1.008
2.768 2.678
0.03* 0.046*
1.526 0.342
2.91 0.655
<0.001* <0.001*
1.216 1.327
2.711 2.964
0.004* 0.001*
0.553 1.164
1.417 0.425
0.612 <0.001*
0.931 1.166
2.195 2.791
0.102 0.008*
1.42 1.438
2.692 2.716
<0.001* <0.001*
eTable 5: Differences in the No Exception and Non-Standard Model for End-Stage Liver Disease Exception Cohorts
56 (49-62)
Non-standard exception (n=1,884) 55 (45-62)
10730 (39.9) 16138 (60.1)
886 (47.0) 998 (53.0)
No exception (n=26,868) Variable Age at listing, y, median (IQR) Sex, n (%) Female Male Ethnicity, n (%) Caucasian African American Hispanic Asian Native American/other ABO Type, n (%) A AB B O BMI at listing, kg/m2, median (IQR) Diabetes at listing, n (%) Yes No Unknown Albumin at listing, mg/dL, median (IQR) Bilirubin at listing, mg/dL, median (IQR) Creatinine at listing, mg/dL, median (IQR) INR at listing, median (IQR) Serum sodium at listing, mEq/L, median (IQR) Laboratory MELD at listing, median (IQR) Laboratory MELD at end of follow up, median (IQR) Allocation MELD at the end of follow up, median (IQR) On dialysis at listing, n (%) Listed for simultaneous kidney transplant, n (%) On life support at listing, n (%) On ventilator at listing, n (%)
p Value <0.001* <0.001*
<0.001* 19446 (72.4) 2067 (7.7) 4164 (15.5) 741 (2.8) 450 (1.7)
1453 (77.1) 163 (8.7) 171 (9.1) 78 (4.1) 19 (1.0) 0.002*
10229 (38.1) 1050 (3.9) 3286 (12.2) 12303 (45.8) 28.5 (24.8-33.1)
693 (36.8) 74 (3.9) 252 (13.4) 865 (45.9) 26.9 (23.6-31.3)
7641 (28.4) 19150 (71.3) 77 (0.3) 3.0 (2.6-6.5) 3.4 (1.8-8.4) 1.1 (0.8-1.7) 1.5 (1.3-2.0)
422 (22.4) 1459 (77.4) 3 (0.2) 3.3 (2.8-3.8) 1.4 (0.7-2.9) 0.9 (0.7-1.3) 1.2 (1.1-1.4)
<0.001* <0.001* <0.001* <0.001*
136 (133-139) 20 (15-28)
137 (134-140) 13 (8-18)
<0.001* <0.001*
24 (16-34)
15 (9-22)
<0.001*
21 (8-33) 3067 (11.4)
25 (19-30) 83 (4.4)
<0.001* <0.001*
3464 (12.9) 1049 (3.9) 673 (2.5)
236 (12.5) 10 (0.5) 7 (0.4)
0.646 <0.001* <0.001*
<0.001* <0.001*
History of portal vein thrombus at listing, n (%) Yes No Unknown Ascites at listing, n (%) None Slight Moderate History of upper abdominal surgery, n (%) Yes No Unknown History of spontaneous bacterial peritonitis, n (%) Yes No Unknown Encephalopathy at listing, n (%) None Grade 1-2 Grade 3-4 History of TIPS, n (%) Yes No Unknown Insurance at listing, n (%) Private Public Other (such as self, charity, donation) Education at listing, n (%) Less than high school Completed high school College degree Postgraduate degree Unknown Working for income at listing, n (%) Yes No Unknown *Statistically significant
0.343 1855 (6.9) 24833 (92.4) 180 (0.7)
132 (7.0) 1734 (92.0) 18 (1.0) <0.001*
4503 (16.8) 13534 (50.4) 8831 (32.9)
793 (42.1) 613 (32.5) 478 (25.4)
11086 (41.3) 15446 (57.5) 336 (1.3)
1022 (54.3) 838 (44.5) 24 (1.3)
2781 (10.4) 23982 (88.9) 195 (0.7)
112 (5.9) 1763 (93.6) 9 (0.5)
8586 (32.0) 15878 (59.1) 2404 (9.0)
1017 (54.0) 783 (41.6) 84 (4.5)
<0.001*
<0.001*
<0.001*
0.008* 2547 (9.5) 23910 (89.0) 411 (1.5)
138 (7.3) 1715 (91.0) 31 (1.7) <0.001*
13983 (52.0) 12576 (46.8) 309 (1.2)
1156 (61.4) 689 (36.6) 39 (2.1) <0.001*
1537 (5.7) 17444 (64.9) 4827 (18.0) 1771 (6.6) 1289 (4.8)
68 (3.6) 1076 (57.1) 461 (24.5) 198 (10.5) 81 (4.3)
4896 (18.2) 21561 (80.3) 404 (1.5)
579 (30.8) 1276 (67.8) 26 (1.4)
<0.001*
TIPS, transjugular intrahepatic portosystemic shunt
eTable 6: Differences in the No Exception and Hepatopulmonary Syndrome Model for EndStage Liver Disease Exception Cohorts
Variable Age at listing, y, median (IQR) Sex, n (%) Female Male Ethnicity, n (%) Caucasian African American Hispanic Asian Native American/other ABO Type, n (%) A AB B O BMI at listing, kg/m2, median (IQR) Diabetes at listing, n (%) Yes No Unknown Albumin at listing, mg/dL, median (IQR) Bilirubin at listing, mg/dL, median (IQR) Creatinine at listing, mg/dL, median (IQR) INR at listing, median (IQR) Serum sodium at listing, mEq/L, median (IQR) Laboratory MELD at listing, median (IQR) Laboratory MELD at end of follow up, median (IQR) Allocation MELD at the end of follow up, median (IQR) On dialysis at listing, n (%) Listed for simultaneous kidney transplant, n (%) On life support at listing, n (%)
No exception (n=26,868) 56 (49-62)
Hepatopulmonary syndrome exception (n=475) 57 (51-62)
10730 (39.9) 16138 (60.1)
248 (52.2) 227 (47.8)
p Value 0.085 <0.001*
<0.001* 19446 (72.4) 2067 (7.7) 4164 (15.5) 741 (2.8) 450 (1.7)
380 (80.0) 8 (1.7) 72 (15.2) 6 (1.3) 9 (1.9)
10229 (38.1) 1050 (3.9) 3286 (12.2) 12303 (45.8) 28.5 (24.8-33.1)
194 (40.8) 20 (4.2) 49 (10.3) 212 (44.6) 28.2 (24.3-32.7)
7641 (28.4) 19150 (71.3) 77 (0.3) 3.0 (2.6-6.5) 3.4 (1.8-8.4)
159 (33.5) 316 (66.5) 0 (0) 3.1 (2.8-3.5) 2.4 (1.6-3.4)
0.007* <0.001*
1.1 (0.8-1.7) 1.5 (1.3-2.0)
0.8 (0.7-1.0) 1.4 (1.2-1.5)
<0.001* <0.001*
136 (133-139)
138 (135-140)
<0.001*
20 (15-28)
14 (11-17)
<0.001*
24 (16-34)
15 (12-20)
<0.001*
21 (8-33) 3067 (11.4)
25 (22-29) 4 (0.8)
<0.001* <0.001*
3464 (12.9) 1049 (3.9)
10 (2.1) 9 (1.9)
<0.001* 0.024*
0.460
0.174 0.030*
On ventilator at listing, n (%) History of portal vein thrombus at listing, n (%) Yes No Unknown Ascites at listing, n (%) None Slight Moderate History of upper abdominal surgery, n (%) Yes No Unknown History of spontaneous bacterial peritonitis, n (%) Yes No Unknown Encephalopathy at listing, n (%) None Grade 1-2 Grade 3-4 History of TIPS, n (%) Yes No Unknown Insurance at listing, n (%) Private Public Other (such as self, charity, donation) Education at listing, n (%) Less than high school Completed high school College degree Postgraduate degree Unknown Working for income at listing, n (%) Yes No
673 (2.5)
1 (0.2)
1855 (6.9) 24833 (92.4) 180 (0.7)
41 (8.6) 433 (91.2) 1 (0.2)
0.001* 0.167
<0.001* 4503 (16.8) 13534 (50.4) 8831 (32.9)
170 (35.8) 237 (49.9) 68 (14.3) 0.005*
11086 (41.3) 15446 (57.5) 336 (1.3)
228 (48.0) 245 (51.6) 2 (0.4) 0.001*
2781 (10.4) 23982 (88.9) 195 (0.7)
24 (5.1) 448 (94.3) 3 (0.6) <0.001*
8586 (32.0) 15878 (59.1) 2404 (9.0)
191 (40.2) 267 (56.2) 17 (3.6) 0.468
2547 (9.5) 23910 (89.0) 411 (1.5)
44 (9.3) 427 (89.9) 4 (0.8) 0.237
13983 (52.0) 12576 (46.8)
234 (49.3) 238 (50.1)
309 (1.2)
3 (0.6)
1537 (5.7) 17444 (64.9) 4827 (18.0) 1771 (6.6) 1289 (4.8)
25 (5.3) 315 (66.3) 84 (17.7) 33 (7.0) 18 (3.8)
0.842
0.512 4896 (18.2) 21561 (80.3)
77 (16.2) 390 (82.1)
Unknown *Statistically significant
404 (1.5)
TIPS, transjugular intrahepatic portosystemic shunt
8 (1.7)