Quantifying Sex-Based Disparities in Liver Allocation | Gastrointestinal Surgery | JAMA Surgery | JAMA Network
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Figure 1.  Change in Excess Risk of Wait List Mortality Among Candidates for Liver Transplant Across Geographic Factors, Model for End-stage Liver Disease (MELD) Score, and Candidate Anthropometric and Liver Measurements
Change in Excess Risk of Wait List Mortality Among Candidates for Liver Transplant Across Geographic Factors, Model for End-stage Liver Disease (MELD) Score, and Candidate Anthropometric and Liver Measurements

Adjusted hazard ratio (HR) of 1.00 indicates statistically equal likelihood of wait list mortality for men and women.

Figure 2.  Change in Disparity in Likelihood of Deceased Donor Liver Transplant (DDLT) Among Liver Transplant Candidates Across Geographic Factors, Model for End-stage Liver Disease (MELD) Score, and Candidate Anthropometric and Liver Measurements
Change in Disparity in Likelihood of Deceased Donor Liver Transplant (DDLT) Among Liver Transplant Candidates Across Geographic Factors, Model for End-stage Liver Disease (MELD) Score, and Candidate Anthropometric and Liver Measurements

HR indicates hazard ratio.

Table 1.  Baseline Demographics by Sexa
Baseline Demographics by Sexa
Table 2.  Association of Waiting List Characteristics With Risk of Wait List Mortality Among Women vs Men
Association of Waiting List Characteristics With Risk of Wait List Mortality Among Women vs Men
Table 3.  Association of Waiting List Characteristics With Likelihood of DDLT Among Female Registrants
Association of Waiting List Characteristics With Likelihood of DDLT Among Female Registrants
1.
Malinchoc  M, Kamath  PS, Gordon  FD, Peine  CJ, Rank  J, ter Borg  PC.  A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts.   Hepatology. 2000;31(4):864-871. doi:10.1053/he.2000.5852 PubMedGoogle ScholarCrossref
2.
Wiesner  R, Edwards  E, Freeman  R,  et al; United Network for Organ Sharing Liver Disease Severity Score Committee.  Model for end-stage liver disease (MELD) and allocation of donor livers.   Gastroenterology. 2003;124(1):91-96. doi:10.1053/gast.2003.50016 PubMedGoogle ScholarCrossref
3.
Croome  KP, Lee  DD, Burns  JM, Keaveny  AP, Taner  CB.  Intraregional model for end-stage liver disease score variation in liver transplantation: disparity in our own backyard.   Liver Transpl. 2018;24(4):488-496. doi:10.1002/lt.25021 PubMedGoogle ScholarCrossref
4.
Dzebisashvili  N, Massie  AB, Lentine  KL,  et al.  Following the organ supply: assessing the benefit of inter-DSA travel in liver transplantation.   Transplantation. 2013;95(2):361-371. doi:10.1097/TP.0b013e3182737cfb PubMedGoogle ScholarCrossref
5.
Haugen  CE, Ishaque  T, Sapirstein  A, Cauneac  A, Segev  DL, Gentry  S.  Geographic disparities in liver supply/demand ratio within fixed-distance and fixed-population circles.   Am J Transplant. 2019;19(7):2044-2052. doi:10.1111/ajt.15297 PubMedGoogle ScholarCrossref
6.
Rana  A, Kaplan  B, Riaz  IB,  et al.  Geographic inequities in liver allograft supply and demand: does it affect patient outcomes?   Transplantation. 2015;99(3):515-520. doi:10.1097/TP.0000000000000372 PubMedGoogle ScholarCrossref
7.
Pullen  LC.  Lawsuits drive transplant community debate over liver allocation.   Am J Transplant. 2019;19(5):1251-1256. doi:10.1111/ajt.15382 PubMedGoogle ScholarCrossref
8.
Cholongitas  E, Marelli  L, Kerry  A,  et al.  Different methods of creatinine measurement significantly affect MELD scores.   Liver Transpl. 2007;13(4):523-529. doi:10.1002/lt.20994 PubMedGoogle ScholarCrossref
9.
Trotter  JF, Brimhall  B, Arjal  R, Phillips  C.  Specific laboratory methodologies achieve higher model for endstage liver disease (MELD) scores for patients listed for liver transplantation.   Liver Transpl. 2004;10(8):995-1000. doi:10.1002/lt.20195 PubMedGoogle ScholarCrossref
10.
Nephew  LD, Goldberg  DS, Lewis  JD, Abt  P, Bryan  M, Forde  KA. Exception points and body size contribute to gender disparity in liver transplantation. Clin Gastroenterol Hepatol. 2017;15(8):1286-1293 e2.
11.
Kim  WR, Biggins  SW, Kremers  WK,  et al.  Hyponatremia and mortality among patients on the liver-transplant waiting list.   N Engl J Med. 2008;359(10):1018-1026. doi:10.1056/NEJMoa0801209 PubMedGoogle ScholarCrossref
12.
Cholongitas  E, Papatheodoridis  GV, Vangeli  M, Terreni  N, Patch  D, Burroughs  AK.  Systematic review: the model for end-stage liver disease—should it replace Child-Pugh’s classification for assessing prognosis in cirrhosis?   Aliment Pharmacol Ther. 2005;22(11-12):1079-1089. doi:10.1111/j.1365-2036.2005.02691.x PubMedGoogle ScholarCrossref
13.
Fraley  DS, Burr  R, Bernardini  J, Angus  D, Kramer  DJ, Johnson  JP.  Impact of acute renal failure on mortality in end-stage liver disease with or without transplantation.   Kidney Int. 1998;54(2):518-524. doi:10.1046/j.1523-1755.1998.00004.x PubMedGoogle ScholarCrossref
14.
Levey  AS, Perrone  RD, Madias  NE.  Serum creatinine and renal function.   Annu Rev Med. 1988;39:465-490. doi:10.1146/annurev.me.39.020188.002341 PubMedGoogle ScholarCrossref
15.
Perrone  RD, Madias  NE, Levey  AS.  Serum creatinine as an index of renal function: new insights into old concepts.   Clin Chem. 1992;38(10):1933-1953. doi:10.1093/clinchem/38.10.1933 PubMedGoogle ScholarCrossref
16.
Allen  AM, Heimbach  JK, Larson  JJ,  et al.  Reduced access to liver transplantation in women: role of height, MELD exception scores, and renal function underestimation.   Transplantation. 2018;102(10):1710-1716. doi:10.1097/TP.0000000000002196 PubMedGoogle ScholarCrossref
17.
Cholongitas  E, Marelli  L, Kerry  A,  et al.  Female liver transplant recipients with the same GFR as male recipients have lower MELD scores—a systematic bias.   Am J Transplant. 2007;7(3):685-692. doi:10.1111/j.1600-6143.2007.01666.x PubMedGoogle ScholarCrossref
18.
Goldberg  DS, French  B, Lewis  JD,  et al.  Liver transplant center variability in accepting organ offers and its impact on patient survival.   J Hepatol. 2016;64(4):843-851. doi:10.1016/j.jhep.2015.11.015 PubMedGoogle ScholarCrossref
19.
Leppke  S, Leighton  T, Zaun  D,  et al.  Scientific Registry of Transplant Recipients: collecting, analyzing, and reporting data on transplantation in the United States.   Transplant Rev (Orlando). 2013;27(2):50-56. doi:10.1016/j.trre.2013.01.002PubMedGoogle ScholarCrossref
20.
Andersen  PK, Gill  RD.  Cox’s regression model for counting processes: a large sample study.   Ann Stat. 1982;10(4):1100-1120. doi:10.1214/aos/1176345976 Google ScholarCrossref
21.
Scientific Registry of Transplant Recipients. SRTR risk adjustment model documentation: waiting list models; 2019. Accessed August 20, 2019. https://www.srtr.org/reports-tools/risk-adjustment-models-waiting-list/
22.
Snyder  JJ, Salkowski  N, Kim  SJ,  et al.  Developing statistical models to assess transplant outcomes using national registries: the process in the United States.   Transplantation. 2016;100(2):288-294. doi:10.1097/TP.0000000000000891 PubMedGoogle ScholarCrossref
23.
Therneau  TM, Grambsch  PM, Fleming  TR.  Martingale-based residuals for survival models.   Biometrika. 1990;77(1):147-160. doi:10.1093/biomet/77.1.147 Google ScholarCrossref
24.
Nguyen  QC, Osypuk  TL, Schmidt  NM, Glymour  MM, Tchetgen Tchetgen  EJ.  Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting.   Am J Epidemiol. 2015;181(5):349-356. doi:10.1093/aje/kwu278 PubMedGoogle ScholarCrossref
25.
Fukazawa  K, Yamada  Y, Nishida  S, Hibi  T, Arheart  KL, Pretto  EA  Jr.  Determination of the safe range of graft size mismatch using body surface area index in deceased liver transplantation.   Transpl Int. 2013;26(7):724-733. doi:10.1111/tri.12111 PubMedGoogle ScholarCrossref
26.
Mindikoglu  AL, Emre  SH, Magder  LS.  Impact of estimated liver volume and liver weight on gender disparity in liver transplantation.   Liver Transpl. 2013;19(1):89-95. doi:10.1002/lt.23553 PubMedGoogle ScholarCrossref
27.
Urata  K, Kawasaki  S, Matsunami  H,  et al.  Calculation of child and adult standard liver volume for liver transplantation.   Hepatology. 1995;21(5):1317-1321. doi:10.1002/hep.1840210515 PubMedGoogle ScholarCrossref
28.
Vauthey  JN, Abdalla  EK, Doherty  DA,  et al.  Body surface area and body weight predict total liver volume in Western adults.   Liver Transpl. 2002;8(3):233-240. doi:10.1053/jlts.2002.31654 PubMedGoogle ScholarCrossref
29.
DuBois  D, DuBois  EF. Clinical calorimetry (tenth paper): a formula to estimate the approximate surface area if height and weight be known. Arch Intern Med (Chic). 1916;17(6_2):863-871.
30.
Mosteller  RD.  Simplified calculation of body-surface area.   N Engl J Med. 1987;317(17):1098. doi:10.1056/NEJM198710223171717PubMedGoogle Scholar
31.
Heinemann  A, Wischhusen  F, Püschel  K, Rogiers  X.  Standard liver volume in the Caucasian population.   Liver Transpl Surg. 1999;5(5):366-368. doi:10.1002/lt.500050516PubMedGoogle ScholarCrossref
32.
Yoshizumi  T, Taketomi  A, Kayashima  H,  et al.  Estimation of standard liver volume for Japanese adults.   Transplant Proc. 2008;40(5):1456-1460. doi:10.1016/j.transproceed.2008.02.082PubMedGoogle ScholarCrossref
33.
Choukèr  A, Martignoni  A, Dugas  M,  et al.  Estimation of liver size for liver transplantation: the impact of age and gender.   Liver Transpl. 2004;10(5):678-685. doi:10.1002/lt.20113PubMedGoogle ScholarCrossref
34.
DeLand  FH, North  WA.  Relationship between liver size and body size.   Radiology. 1968;91(6):1195-1198. doi:10.1148/91.6.1195PubMedGoogle ScholarCrossref
35.
Freeman  RB, Wiesner  RH, Edwards  E, Harper  A, Merion  R, Wolfe  R; United Network for Organ Sharing/Organ Procurement and Intestine Transplantation Network Liver and Transplantation Committee.  Results of the first year of the new liver allocation plan.   Liver Transpl. 2004;10(1):7-15. doi:10.1002/lt.20024 PubMedGoogle ScholarCrossref
36.
Kanwal  F, Dulai  GS, Spiegel  BMR, Yee  HF, Gralnek  IM.  A comparison of liver transplantation outcomes in the pre- vs. post-MELD eras.   Aliment Pharmacol Ther. 2005;21(2):169-177. doi:10.1111/j.1365-2036.2005.02321.x PubMedGoogle ScholarCrossref
37.
Cullaro  G, Sarkar  M, Lai  JC.  Sex-based disparities in delisting for being “too sick” for liver transplantation.   Am J Transplant. 2018;18(5):1214-1219. doi:10.1111/ajt.14608 PubMedGoogle ScholarCrossref
38.
Ge  J, Gilroy  R, Lai  JC.  Receipt of a pediatric liver offer as the first offer reduces waitlist mortality for adult women.   Hepatology. 2018;68(3):1101-1110. doi:10.1002/hep.29906 PubMedGoogle ScholarCrossref
39.
Wan  P, Li  Q, Zhang  J, Xia  Q.  Right lobe split liver transplantation versus whole liver transplantation in adult recipients: a systematic review and meta-analysis.   Liver Transpl. 2015;21(7):928-943. doi:10.1002/lt.24135 PubMedGoogle ScholarCrossref
40.
Deshpande  R, Hirose  R, Mulligan  D.  Liver allocation and distribution: time for a change.   Curr Opin Organ Transplant. 2017;22(2):162-168. doi:10.1097/MOT.0000000000000397 PubMedGoogle ScholarCrossref
41.
Bowring  MG, Zhou  S, Chow  EKH, Massie  AB, Segev  DL, Gentry  SE.  Geographic disparity in deceased donor liver transplant rates following Share 35.   Transplantation. 2019;103(10):2113-2120. doi:10.1097/TP.0000000000002643 PubMedGoogle ScholarCrossref
42.
Goldberg  DS, French  B, Sahota  G, Wallace  AE, Lewis  JD, Halpern  SD.  Use of population-based data to demonstrate how waitlist-based metrics overestimate geographic disparities in access to liver transplant care.   Am J Transplant. 2016;16(10):2903-2911. doi:10.1111/ajt.13820 PubMedGoogle ScholarCrossref
43.
Mehrotra  S, Kilambi  V, Bui  K,  et al.  A concentric neighborhood solution to disparity in liver access that contains current UNOS districts.   Transplantation. 2018;102(2):255-278. doi:10.1097/TP.0000000000001934 PubMedGoogle ScholarCrossref
44.
Parikh  ND, Marrero  WJ, Sonnenday  CJ, Lok  AS, Hutton  DW, Lavieri  MS.  Population-based analysis and projections of liver supply under redistricting.   Transplantation. 2017;101(9):2048-2055. doi:10.1097/TP.0000000000001785 PubMedGoogle ScholarCrossref
45.
Reed  A, Chapman  WC, Knechtle  S, Chavin  K, Gilroy  R, Klintmalm  GBG.  Equalizing MELD scores over broad geographies is not the most efficacious way to allocate a scarce resource in a value-based environment.   Ann Surg. 2015;262(2):220-223. doi:10.1097/SLA.0000000000001331 PubMedGoogle ScholarCrossref
46.
Snyder  JJ, Salkowski  N, Wey  A, Pyke  J, Israni  AK, Kasiske  BL.  Organ distribution without geographic boundaries: a possible framework for organ allocation.   Am J Transplant. 2018;18(11):2635-2640. doi:10.1111/ajt.15115 PubMedGoogle ScholarCrossref
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    Original Investigation
    May 20, 2020

    Quantifying Sex-Based Disparities in Liver Allocation

    Author Affiliations
    • 1Division of Transplantation, Department of Surgery, University of Alabama at Birmingham School of Medicine, Birmingham
    • 2Division of Transplant Surgery, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 3Division of Transplantation, Department of Surgery, University of Colorado School of Medicine, Aurora
    • 4Division of Gastroenterology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 5Division of Renal and Electrolytes, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia
    • 6Division of Transplantation, Department of Surgery, University of California School of Medicine, San Francisco, San Francisco
    JAMA Surg. 2020;155(7):e201129. doi:10.1001/jamasurg.2020.1129
    Key Points

    Question  What proportion of sex-based disparities in liver allocation is associated with geographic location, candidate anthropometric and liver measurements, or Model for End-stage Liver Disease score?

    Findings  In this cohort study of 81 357 participants, women were 8.6% more likely to die while waiting for a liver transplant and were 14.4% less likely to receive a deceased donor liver transplant compared with men. Candidate anthropometric and liver measurements and creatinine level had stronger associations than geographic location with sex disparities in wait list mortality and the likelihood of deceased donor liver transplant.

    Meaning  The findings suggest that mitigating sex-based disparities in liver allocation may require a comprehensive approach that extends beyond geographic factors currently being considered in the transplant community.

    Abstract

    Importance  Differences in local organ supply and demand have introduced geographic inequities in the Model for End-stage Liver Disease (MELD) score–based liver allocation system, prompting national debate and patient-initiated lawsuits. No study to our knowledge has quantified the sex disparities in allocation associated with clinical vs geographic characteristics.

    Objective  To estimate the proportion of sex disparity in wait list mortality and deceased donor liver transplant (DDLT) associated with clinical and geographic characteristics.

    Design, Setting, and Participants  This retrospective cohort study used adult (age ≥18 years) liver-only transplant listings reported to the Organ Procurement and Transplantation Network from June 18, 2013, through March 1, 2018.

    Exposure  Liver transplant waiting list.

    Main Outcomes and Measures  Primary outcomes included wait list mortality and DDLT. Multivariate Cox proportional hazards regression models were constructed, and inverse odds ratio weighting was used to estimate the proportion of disparity across geographic location, MELD score, and candidate anthropometric and liver measurements.

    Results  Among 81 357 adults wait-listed for liver transplant only, 36.1% were women (mean [SD] age, 54.7 [11.3] years; interquartile range, 49.0-63.0 years) and 63.9% were men (mean [SD] age, 55.7 [10.1] years; interquartile range, 51.0-63.0 years). Compared with men, women were 8.6% more likely to die while on the waiting list (adjusted hazard ratio [aHR], 1.11; 95% CI, 1.04-1.18) and were 14.4% less likely to receive a DDLT (aHR, 0.86; 95% CI, 0.84-0.88). In the geographic domain, organ procurement organization was the only variable that was significantly associated with increased disparity between female sex and wait list mortality (22.1% increase; aHR, 1.22; 95% CI, 1.09-1.30); no measure of the geographic domain was associated with DDLT. Laboratory and allocation MELD scores were associated with increases in disparities in wait list mortality: 1.14 (95% CI, 1.09-1.19; 50.1% increase among women) and DDLT: 0.87 (95% CI, 0.86-0.88; 10.3% increase among women). Candidate anthropometric and liver measurements had the strongest association with disparities between men and women in wait list mortality (125.8% increase among women) and DDLT (49.0% increase among women).

    Conclusions and Relevance  Our findings suggest that addressing geographic disparities alone may not mitigate sex-based disparities, which were associated with the inability of the MELD score to accurately estimate disease severity in women and to account for candidate anthropometric and liver measurements in this study.

    Introduction

    In 2002, allocation in liver transplantation evolved from a model based on waiting time to a model defined by objective medical urgency. A metric for identifying patients at highest risk of death within 90 days of being placed on a wait list, known as the Model for End-stage Liver Disease (MELD) score, was introduced.1,2 MELD scores rank order candidates such that the sickest patients receive highest priority on the match run (priority list for receiving a transplant). Concerns for organ viability further dictated that livers be allocated locally first, then by region, and finally nationally, with the only mandatory regional share of livers required for wait-listed candidates with MELD scores of at least 35.3,4 Observed disparate median allocation MELD scores and wait list mortality across US transplant centers have reflected differences in local organ supply and demand.5,6 These geographic disparities mean that at any given moment, the sickest patient in the US as defined by MELD score may not actually be at the top of the local match run when a liver becomes available, prompting ongoing national debate and patient-initiated federal lawsuits regarding the need for geographic equity in liver allocation.7 Debate surrounding geographic disparities in liver allocation is rooted in 2 key assumptions: (1) that the MELD score accurately identifies the sickest patients8,9 and (2) that patients at the top of the match run actually receive the liver transplant.10

    The MELD score is a mathematical algorithm that uses objective laboratory data, including sodium level, serum total bilirubin level, serum creatinine (sCr) level, and the international normalized ratio (INR) for prothrombin time, to estimate wait list mortality within 90 days.11 Inclusion of sCr level as a measure of kidney function has been considered a major advantage of the MELD score because impaired kidney function is an established negative prognostic marker for cirrhosis.12,13 However, sCr level is merely a surrogate for kidney function, and sCr concentrations have been shown to vary widely based on differences in muscle mass; therefore, reported sCr values can have wide variation across individuals with the same kidney function (eg, glomerular filtration rate) but different baseline characteristics, such as age, sex, race, and ethnicity.14,15 Multiple studies16,17 have shown that sCr level overestimates kidney function in women compared with men such that women with the same glomerular filtration rate as men have a lower sCr level and resulting lower calculated MELD scores.16,17 Sex-based differences in sCr level are not accounted for by the MELD score, suggesting that the MELD score may not accurately identify and prioritize the sickest patients for liver transplant.

    Moreover, 30% of livers are not allocated to candidates within the top 3 positions on any given match run, suggesting that being ranked highest does not ensure receipt of a liver for transplant.10,18 Organs can be declined for reasons such as donor-recipient size mismatch, which has been shown to contribute significantly to sex-based disparities in access to liver transplant.10 Studies10,18 have shown that women at the top of the priority list are more likely to have donor livers declined on their behalf, with the reason for decline 4-fold more commonly because of donor-recipient size mismatch. Women with at least 1 organ decline have been shown to be 26% more likely to die on the waiting list than men.10 Sex-based differences in size are not accounted for within the existing MELD allocation system.

    Differences in local organ supply and demand are associated with geographic disparities in liver allocation.5 However, discussions surrounding equity in allocation require critical assumptions to be met, including that the MELD score accurately defines disease severity and patients prioritized as the sickest actually receive the liver transplant. Previous studies10,16-18 examining sex-based differences in wait list mortality and liver transplant rates have shown that these assumptions are routinely violated. To our knowledge, no study has attempted to quantify the associations of geographic location, MELD score, and candidate anthropometric and liver measurements with sex-based disparity in liver allocation. Without better understanding the degree of disparity associated with various factors, efforts made and resources used to inform the design of a more equitable liver allocation system may be misplaced. We estimated the proportion of the sex disparity in wait list mortality and deceased donor liver transplant (DDLT) associated with clinical and geographic characteristics.

    Methods
    Data Source

    This cohort study used data from the Scientific Registry of Transplant Recipients19; these data are submitted by members of the Organ Procurement and Transplantation Network on all donors, wait-listed candidates, and transplant recipients in the US. The Health Resources and Services Administration of the US Department of Health and Human Services provided oversight for Organ Procurement and Transplantation Network and Scientific Registry of Transplant Recipients activities. This study was approved by the institutional review board of the University of Alabama at Birmingham in Birmingham. All transplant candidates, recipients, and donors nationally consent to their data being collected and made publicly available for research purposes. The institutional review board of the University of Alabama at Birmingham granted a waiver of informed consent for this reason and because data were deidentified.

    Study Population

    Adults (aged ≥18 years) on the liver-only transplant waiting list from June 18, 2013, through March 1, 2018, were identified (n = 81 357) and categorized by sex (self-reported) at the time they were added to the liver transplant waiting list (eMethods and eFigure in the Supplement).

    Outcome Ascertainment

    Primary outcomes were wait list mortality and likelihood of DDLT. Wait list mortality was defined as time from addition to the waiting list to death, being censored for transplant, waiting list removal, or administrative end of study (March 1, 2019). DDLT was defined as time from addition to the waiting list to transplant, being censored for death, waiting list removal (defined as too sick for transplant, improvement in health, or medically unsuitable), or administrative end of study.

    Statistical Analyses
    Exploratory Data Analyses

    Waiting list characteristics were compared by sex. Continuous variables were reported as median and interquartile range because of their distribution and were analyzed using Wilcoxon rank sum tests, and categorical variables were examined using χ2 tests.

    Survival Analyses

    To permit inclusion of prevalent listings, Cox proportional hazards regression models were built using counting process data structure to accommodate delayed entry.20 Base models for wait list mortality and DDLT included all covariates from Scientific Registry of Transplant Recipients risk-adjustment models21,22 and waiting list status. Self-defined race was categorized as white, black, and other, and all continuous variables were modeled as such. Different functional forms for continuous variables were explored using Martingale residuals,23 and categorizations of continuous variables were assessed for their association with the effect size for sex. The proportional hazards assumption was assessed using Schoenfeld residuals. If 5% or less of any variable was missing, missing observations were coded as missing or unknown to permit incorporation in adjusted analyses.

    Inverse Odds Ratio Weighting

    To estimate the proportion of sex disparity in wait list mortality and DDLT associated with clinical and geographic characteristics, we used inverse odds ratio (OR) weighting, which gives decomposition of an association into direct and indirect associations.24 We first generated the predicted probability of being female with covariates in the base model and a potential mediating variable. Resulting estimated probabilities were used to create weights. An unweighted Cox proportional hazards regression model adjusted for sex and base model covariates was fit for each outcome, providing an estimate of total disparity between men and women. Then, a weighted model estimated residual disparity between men and women after accounting for potential mediation through inclusion of the inverse OR weighting. The resulting estimate for disparity in this model represented the direct association, and the remaining unexplained disparity represented the indirect association. We calculated the proportion of disparity associated with the mediator as follows:

    Image description not available.

    where β1 is the unweighted coefficient for sex and β* is the weighted coefficient for sex. SEs were calculated using bootstrap analysis. We examined potential mediators in 3 domains: (1) geographic (United Network for Organ Sharing [UNOS] region, listing organ procurement organization, and listing center), (2) MELD score (laboratory MELD score, allocation MELD score, and all components of the MELD score), and (3) candidate anthropometric and liver measurements (height, weight, body mass index, body surface area, estimated liver volume, and estimated liver weight10,25-28). A factor was considered to mediate the association between sex and the outcome if the proportion of disparity associated with the mediator was statistically significant (2-sided, P < .05). The association could be positive or negative depending on the directionality of the difference between unweighted and weighted hazard ratios (HRs).

    Sensitivity Analyses

    Additional analyses were conducted and are described in eMethods in the Supplement. Statistical analyses were performed in SAS, version 9.4 (SAS Institute Inc) and Stata, version 15.1 (StataCorp).

    Results
    Study Population

    Of 81 357 adults with liver-only listings, 36.1% were women (mean [SD] age, 54.7 [11.3] years; interquartile range, 49.0-63.0 years) and 63.9% were men (mean [SD] age, 55.7 [10.1] years; interquartile range, 51.0-63.0 years) (P < .001). Women more commonly had previous abdominal surgery (55.6% vs 36.6%, P < .001), less commonly had hepatocellular carcinoma (7.3% vs 13.5%, P < .001), and had consistently lower anthropometric and liver measurements, as measured by body surface area, estimated liver volume, and estimated liver weight. No clinically meaningful differences in other baseline characteristics by sex were observed (Table 1).27-34

    Wait List Mortality

    Of 8827 individuals who died on the waiting list, 3615 (41.0%) were female and 5212 (59.0%) were male (P < .001). After adjustment, women had 8.6% greater risk of wait list mortality compared with men (adjusted HR [aHR], 1.09; 95% CI, 1.05-1.14) (Table 2 and Figure 1).

    After weighting for UNOS region, women had 11.1% greater risk of wait list mortality compared with men (aHR, 1.11; 95% CI, 1.06-1.17), representing a 22.8% increase in the disparity compared with the unweighted model. After weighting for listing center, women had 10.5% greater risk of wait list mortality compared with men (aHR, 1.11; 95% CI, 1.04-1.18), representing a 10.6% increase in disparity, but this was not statistically significant (Table 2 and Figure 1).

    After weighting for the laboratory MELD score, women had 14.4% greater risk of wait list mortality compared with men (aHR, 1.14; 95% CI, 1.09-1.19), corresponding to a 50.1% increase in the disparity between men and women. Allocation MELD score was associated with a 53.0% increase in the disparity between men and women, such that women had 14.4% greater risk of wait list mortality than men (aHR, 1.14; 95% CI, 1.09-1.20). When weighted by sCr level, women had a 13.6 greater risk of wait list mortality compared with men (aHR, 1.13; 95% CI, 1.08-1.18), representing a 35.5% increase in the disparity between men and women vs the unweighted model. Encephalopathy and INR were not significant mediators of the association between sex and wait list mortality (Table 2 and Figure 1).

    After weighting for body surface area, women had a 22.1% greater risk of wait list mortality compared with men (aHR, 1.22; 95% CI, 1.09-1.30), corresponding to a 125.8% increase in risk compared with the unweighted model. Estimated liver volume (aHR, 1.19; 95% CI, 1.10-1.31) significantly increased the disparity between men and women by 98.8% and estimated liver weight (aHR, 1.20; 95% CI, 1.12-1.29) increased the disparity between men and women by 108.6%. Body mass index was not a substantial mediator of the association between female sex and wait list mortality (Table 2 and Figure 1).

    In an unweighted model adjusted for laboratory MELD score and other clinical characteristics, female sex was associated with a 10.9% greater risk of wait list mortality compared with men (aHR, 1.11; 95% CI, 1.08-1.14). After weighting for body surface area, this risk increased among women compared with men by 23.7% (aHR, 1.24; 95% CI, 1.13-1.35), with an associated increase in disparity of 105.6%. After weighting for estimated liver volume, women had 23.1% increased risk of wait list mortality compared with men (aHR, 1.23; 95% CI, 1.15-1.32), with an increase in disparity of 101.2% compared with the unweighted model. A similar increase in the disparity between men and women was found after weighting for estimated liver weight (aHR, 1.23; 95% CI, 1.15-1.32). Body mass index did not mediate the association between female sex and wait list mortality (Table 2 and Figure 1).

    In an unweighted model adjusting for laboratory MELD score, anthropometric and liver measurements, and UNOS region, female sex was associated with 25.2% greater risk of wait list mortality compared with male sex (aHR, 1.25; 95% CI, 1.21-1.29) (Figure 1).

    Likelihood of DDLT

    Of 37 114 individuals who received a DDLT, 12 370 (33.3%) were female and 24 744 (66.7%) were male (P < .001). After adjustment, women were 14.4% less likely to receive a DDLT compared with men (aHR, 0.86; 95% CI, 0.84-0.88) (Table 3 and Figure 2).

    After weighting for UNOS region, women were 15.0% less likely to have a DDLT compared with men (aHR, 0.85; 95% CI, 0.85-0.86), with an increase in disparity of 3.9%, which was not statistically significant. Similarly, wait listing center and organ procurement organization did not mediate the association between sex and DDLT (Table 3 and Figure 2).

    After weighting for laboratory MELD score, women were 13.1% less likely to receive a DDLT compared with men (aHR, 0.87; 95% CI; 0.86-0.88), representing a 10.3% decrease in the disparity between men and women. Allocation MELD was associated with a 13.4% increase and sCR level with a 5.4% decrease in the disparity between men and women. After weighting for INR, the disparity between men and women increased by 12.7% such that women were 16.1% less likely to receive a DDLT (aHR, 0.84; 95% CI, 0.82-0.86) (Table 3 and Figure 2).

    After weighting for estimated liver volume, women were 8.5% less likely to receive a transplant than men (aHR, 0.92; 95% CI, 0.90-0.95), corresponding to a 49.0% decrease in disparity. Similarly, body surface area was associated with a 44.2% reduction in the disparity and estimated liver weight with a 44.1% reduction in the disparity between men and women (aHR, 0.92 [95% CI, 0.89-0.94] vs. 0.92 [95% CI, 0.89-0.84]). Body mass index was also a substantial mediator of the association between sex and DDLT; it was associated with an increased disparity between men and women of 6.8% such that women were 15.3% less likely to receive a transplant (aHR, 0.85; 95% CI, 0.83-0.86) (Table 3 and Figure 2).

    In an unweighted model adjusting for size and other clinical characteristics, women were 8.4% less likely to receive a DDLT (aHR, 0.92; 95% CI, 0.91-0.94). Weighting for sCr level was associated with a 35.5% reduced disparity between men and women such that women were only 4.7% less likely to receive a DDLT compared with men (aHR, 0.95; 95% CI, 0.94-0.96). Weighting for INR associated with a 43.4% increased disparity between men and women, and weighting for encephalopathy was associated with a 14.9% reduced disparity. Geographic location did not mediate the association between sex and DDLT when controlling for candidate anthropometric and liver measurements (Table 3 and Figure 2).

    In an unweighted model adjusting for laboratory MELD score, anthropometric and liver measurements, and UNOS region, women were 4.8% less likely to receive a DDLT compared with men (aHR, 0.95; 95% CI, 0.92-0.97) (Figure 2).

    Discussion

    In this, to our knowledge, first national study, we performed mediation analyses through inverse OR weighting to examine sex differences in allocation across 3 domains, including geographic location, MELD score, and candidate anthropometric and liver measurements, and assessed their associations with the current sex-based disparities in liver allocation. We found that women had 8.6% greater risk of wait list mortality and were 14.4% less likely to receive a transplant compared with men. Geographic location was strongly associated with increased disparities in wait list mortality (22.8%), but candidate anthropometric and liver measurements and laboratory MELD scores had more statistically significant associations (representing 125.8% and 50.1% of the sex-based disparity, respectively); thus, size mismatch between the donor and intended recipient and incorrect assessments of liver disease severity were more strongly associated with the observed sex disparity in wait list mortality than local supply of organs. For DDLT, the associations with geographic differences were not statistically significant (only 3.9% of sex-based disparity), whereas metrics of candidate anthropometric and liver measurements (49.0%) and liver disease severity (MELD score) (10.3%) had the strongest associations with inequities in DDLT between women and men.

    Ensuring equity in settings with limited resources is not without challenges. Because there are too few organs for those in need, priority for liver transplant is given to those with the greatest medical urgency. Equity is achieved when the sickest patients are allocated and receive livers first. Although introduction of MELD-based allocation has been associated with significant reductions in wait list mortality,35,36 studies8,9,17 have shown that as a metric of medical urgency, MELD score is imperfect and has exacerbated certain inequities. There is inherent bias in sCr level as a measure of kidney function, and use of glomerular filtration rate to replace sCr level in the MELD score calculation would be a step toward rectifying this bias. Higher than expected organ declines for women with small body stature could be addressed by preferential allocation of small donor livers or left lateral segments from mandated split livers.10,17,37-39 Despite objective data showing sex-based disparities in liver allocation, currently no change in policy has occurred. Whether these measures would mitigate sex-based disparities remains unclear because UNOS, which holds the Organ Procurement and Transplantation Network contract and is responsible for deceased donor organ allocation in the US, has never modeled such changes to our knowledge.

    Currently, the transplant community is considering geographic redistribution, the most significant proposed allocation change since the introduction of the MELD score and Share-35 (which prioritized regional allocation of deceased donor livers to candidates with a MELD score exceeding 35), to redefine local organ supply by replacing donor service areas with fixed concentric circles around donor hospitals.40 However, newly proposed geographic models rely on the same metric for medical urgency, the MELD score,5,6,41-46 and offer no solution for candidates with small body stature who may appear at the top of the match run yet are routinely skipped secondary to discrepancies in donor-recipient size.10 On the basis of our data, geographic redistribution may not ameliorate the sex disparities in transplant access. Although geographic factors matter, examining geographic access alone may be insufficient. We believe that efforts should simultaneously focus on ensuring that the definition of medical urgency, the MELD score, is equitable and that those who are prioritized for transplant, even those with small body stature, actually receive the transplant. We propose that a better course of action is to simultaneously address the attributes of the existing allocation system that were most strongly associated with increased sex disparities in wait list mortality and DDLT in our study: the MELD score and candidate anthropomorphic and liver measurements. Findings from our study support such process improvement in liver allocation.

    Strengths and Limitations

    A strength of our study is the novel application of inverse OR weighting to characterize factors associated with sex-based disparities in liver allocation. Weighting created independent associations between treatment and mediators, eliminating indirect pathways for mediators. This framework, however, is limited by the reliability and accuracy of variables captured by the Organ Procurement and Transplantation Network. Moreover, it is plausible that other factors not routinely captured by the Organ Procurement and Transplantation Network may be associated with disparities in allocation or may confound our findings specific to sex. In addition, variances of estimates derived from inverse OR weighting can be wider than in other mediation methods, and we may not have detected smaller mediating factors, such as geographic factors.

    Conclusions

    Our findings suggest that the MELD score does not accurately estimate disease severity in women and that the lack of consideration of candidate anthropomorphic and liver measurements in the current allocation system may have a greater association with the sex disparity in liver allocation than geographic factors. These data further suggest that proposed policies designed to mitigate geographic disparities alone in liver allocation may not mitigate existing sex-based inequities.

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    Article Information

    Accepted for Publication: March 1, 2020.

    Corresponding Author: Jayme E. Locke, MD, MPH, Division of Transplantation, Department of Surgery, University of Alabama at Birmingham, Birmingham School of Medicine, 701 19th St S, LHRB 780, Birmingham, AL 35294 (jlocke@uabmc.edu).

    Published Online: May 20, 2020. doi:10.1001/jamasurg.2020.1129

    Author Contributions: Drs Locke and Shelton had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

    Concept and design: Locke, Shelton, Pomfret, Sawinski, Gray, Ascher.

    Acquisition, analysis, or interpretation of data: Locke, Shelton, Olthoff, Pomfret, Forde, Sawinski.

    Drafting of the manuscript: Locke, Shelton, Sawinski.

    Critical revision of the manuscript for important intellectual content: All authors.

    Statistical analysis: Locke, Shelton, Pomfret, Forde.

    Administrative, technical, or material support: Locke.

    Supervision: Locke, Olthoff, Sawinski, Ascher.

    Conflict of Interest Disclosures: Dr Locke reported receiving personal fees from Sanofi and Hansa Medical outside the submitted work. Dr Sawinski reported serving on the external advisory boards of CareDx, Natera, and Veloxis outside the submitted work. No other disclosures were reported.

    Disclaimer: Interpretation and reporting of these data are the responsibility of the authors and should not be seen as an official policy of or interpretation by the Scientific Registry of Transplant Recipients or the US Government.

    Additional Information: The data reported here have been supplied by the Minneapolis Medical Research Foundation as the contractor for the Scientific Registry of Transplant Recipients.

    References
    1.
    Malinchoc  M, Kamath  PS, Gordon  FD, Peine  CJ, Rank  J, ter Borg  PC.  A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts.   Hepatology. 2000;31(4):864-871. doi:10.1053/he.2000.5852 PubMedGoogle ScholarCrossref
    2.
    Wiesner  R, Edwards  E, Freeman  R,  et al; United Network for Organ Sharing Liver Disease Severity Score Committee.  Model for end-stage liver disease (MELD) and allocation of donor livers.   Gastroenterology. 2003;124(1):91-96. doi:10.1053/gast.2003.50016 PubMedGoogle ScholarCrossref
    3.
    Croome  KP, Lee  DD, Burns  JM, Keaveny  AP, Taner  CB.  Intraregional model for end-stage liver disease score variation in liver transplantation: disparity in our own backyard.   Liver Transpl. 2018;24(4):488-496. doi:10.1002/lt.25021 PubMedGoogle ScholarCrossref
    4.
    Dzebisashvili  N, Massie  AB, Lentine  KL,  et al.  Following the organ supply: assessing the benefit of inter-DSA travel in liver transplantation.   Transplantation. 2013;95(2):361-371. doi:10.1097/TP.0b013e3182737cfb PubMedGoogle ScholarCrossref
    5.
    Haugen  CE, Ishaque  T, Sapirstein  A, Cauneac  A, Segev  DL, Gentry  S.  Geographic disparities in liver supply/demand ratio within fixed-distance and fixed-population circles.   Am J Transplant. 2019;19(7):2044-2052. doi:10.1111/ajt.15297 PubMedGoogle ScholarCrossref
    6.
    Rana  A, Kaplan  B, Riaz  IB,  et al.  Geographic inequities in liver allograft supply and demand: does it affect patient outcomes?   Transplantation. 2015;99(3):515-520. doi:10.1097/TP.0000000000000372 PubMedGoogle ScholarCrossref
    7.
    Pullen  LC.  Lawsuits drive transplant community debate over liver allocation.   Am J Transplant. 2019;19(5):1251-1256. doi:10.1111/ajt.15382 PubMedGoogle ScholarCrossref
    8.
    Cholongitas  E, Marelli  L, Kerry  A,  et al.  Different methods of creatinine measurement significantly affect MELD scores.   Liver Transpl. 2007;13(4):523-529. doi:10.1002/lt.20994 PubMedGoogle ScholarCrossref
    9.
    Trotter  JF, Brimhall  B, Arjal  R, Phillips  C.  Specific laboratory methodologies achieve higher model for endstage liver disease (MELD) scores for patients listed for liver transplantation.   Liver Transpl. 2004;10(8):995-1000. doi:10.1002/lt.20195 PubMedGoogle ScholarCrossref
    10.
    Nephew  LD, Goldberg  DS, Lewis  JD, Abt  P, Bryan  M, Forde  KA. Exception points and body size contribute to gender disparity in liver transplantation. Clin Gastroenterol Hepatol. 2017;15(8):1286-1293 e2.
    11.
    Kim  WR, Biggins  SW, Kremers  WK,  et al.  Hyponatremia and mortality among patients on the liver-transplant waiting list.   N Engl J Med. 2008;359(10):1018-1026. doi:10.1056/NEJMoa0801209 PubMedGoogle ScholarCrossref
    12.
    Cholongitas  E, Papatheodoridis  GV, Vangeli  M, Terreni  N, Patch  D, Burroughs  AK.  Systematic review: the model for end-stage liver disease—should it replace Child-Pugh’s classification for assessing prognosis in cirrhosis?   Aliment Pharmacol Ther. 2005;22(11-12):1079-1089. doi:10.1111/j.1365-2036.2005.02691.x PubMedGoogle ScholarCrossref
    13.
    Fraley  DS, Burr  R, Bernardini  J, Angus  D, Kramer  DJ, Johnson  JP.  Impact of acute renal failure on mortality in end-stage liver disease with or without transplantation.   Kidney Int. 1998;54(2):518-524. doi:10.1046/j.1523-1755.1998.00004.x PubMedGoogle ScholarCrossref
    14.
    Levey  AS, Perrone  RD, Madias  NE.  Serum creatinine and renal function.   Annu Rev Med. 1988;39:465-490. doi:10.1146/annurev.me.39.020188.002341 PubMedGoogle ScholarCrossref
    15.
    Perrone  RD, Madias  NE, Levey  AS.  Serum creatinine as an index of renal function: new insights into old concepts.   Clin Chem. 1992;38(10):1933-1953. doi:10.1093/clinchem/38.10.1933 PubMedGoogle ScholarCrossref
    16.
    Allen  AM, Heimbach  JK, Larson  JJ,  et al.  Reduced access to liver transplantation in women: role of height, MELD exception scores, and renal function underestimation.   Transplantation. 2018;102(10):1710-1716. doi:10.1097/TP.0000000000002196 PubMedGoogle ScholarCrossref
    17.
    Cholongitas  E, Marelli  L, Kerry  A,  et al.  Female liver transplant recipients with the same GFR as male recipients have lower MELD scores—a systematic bias.   Am J Transplant. 2007;7(3):685-692. doi:10.1111/j.1600-6143.2007.01666.x PubMedGoogle ScholarCrossref
    18.
    Goldberg  DS, French  B, Lewis  JD,  et al.  Liver transplant center variability in accepting organ offers and its impact on patient survival.   J Hepatol. 2016;64(4):843-851. doi:10.1016/j.jhep.2015.11.015 PubMedGoogle ScholarCrossref
    19.
    Leppke  S, Leighton  T, Zaun  D,  et al.  Scientific Registry of Transplant Recipients: collecting, analyzing, and reporting data on transplantation in the United States.   Transplant Rev (Orlando). 2013;27(2):50-56. doi:10.1016/j.trre.2013.01.002PubMedGoogle ScholarCrossref
    20.
    Andersen  PK, Gill  RD.  Cox’s regression model for counting processes: a large sample study.   Ann Stat. 1982;10(4):1100-1120. doi:10.1214/aos/1176345976 Google ScholarCrossref
    21.
    Scientific Registry of Transplant Recipients. SRTR risk adjustment model documentation: waiting list models; 2019. Accessed August 20, 2019. https://www.srtr.org/reports-tools/risk-adjustment-models-waiting-list/
    22.
    Snyder  JJ, Salkowski  N, Kim  SJ,  et al.  Developing statistical models to assess transplant outcomes using national registries: the process in the United States.   Transplantation. 2016;100(2):288-294. doi:10.1097/TP.0000000000000891 PubMedGoogle ScholarCrossref
    23.
    Therneau  TM, Grambsch  PM, Fleming  TR.  Martingale-based residuals for survival models.   Biometrika. 1990;77(1):147-160. doi:10.1093/biomet/77.1.147 Google ScholarCrossref
    24.
    Nguyen  QC, Osypuk  TL, Schmidt  NM, Glymour  MM, Tchetgen Tchetgen  EJ.  Practical guidance for conducting mediation analysis with multiple mediators using inverse odds ratio weighting.   Am J Epidemiol. 2015;181(5):349-356. doi:10.1093/aje/kwu278 PubMedGoogle ScholarCrossref
    25.
    Fukazawa  K, Yamada  Y, Nishida  S, Hibi  T, Arheart  KL, Pretto  EA  Jr.  Determination of the safe range of graft size mismatch using body surface area index in deceased liver transplantation.   Transpl Int. 2013;26(7):724-733. doi:10.1111/tri.12111 PubMedGoogle ScholarCrossref
    26.
    Mindikoglu  AL, Emre  SH, Magder  LS.  Impact of estimated liver volume and liver weight on gender disparity in liver transplantation.   Liver Transpl. 2013;19(1):89-95. doi:10.1002/lt.23553 PubMedGoogle ScholarCrossref
    27.
    Urata  K, Kawasaki  S, Matsunami  H,  et al.  Calculation of child and adult standard liver volume for liver transplantation.   Hepatology. 1995;21(5):1317-1321. doi:10.1002/hep.1840210515 PubMedGoogle ScholarCrossref
    28.
    Vauthey  JN, Abdalla  EK, Doherty  DA,  et al.  Body surface area and body weight predict total liver volume in Western adults.   Liver Transpl. 2002;8(3):233-240. doi:10.1053/jlts.2002.31654 PubMedGoogle ScholarCrossref
    29.
    DuBois  D, DuBois  EF. Clinical calorimetry (tenth paper): a formula to estimate the approximate surface area if height and weight be known. Arch Intern Med (Chic). 1916;17(6_2):863-871.
    30.
    Mosteller  RD.  Simplified calculation of body-surface area.   N Engl J Med. 1987;317(17):1098. doi:10.1056/NEJM198710223171717PubMedGoogle Scholar
    31.
    Heinemann  A, Wischhusen  F, Püschel  K, Rogiers  X.  Standard liver volume in the Caucasian population.   Liver Transpl Surg. 1999;5(5):366-368. doi:10.1002/lt.500050516PubMedGoogle ScholarCrossref
    32.
    Yoshizumi  T, Taketomi  A, Kayashima  H,  et al.  Estimation of standard liver volume for Japanese adults.   Transplant Proc. 2008;40(5):1456-1460. doi:10.1016/j.transproceed.2008.02.082PubMedGoogle ScholarCrossref
    33.
    Choukèr  A, Martignoni  A, Dugas  M,  et al.  Estimation of liver size for liver transplantation: the impact of age and gender.   Liver Transpl. 2004;10(5):678-685. doi:10.1002/lt.20113PubMedGoogle ScholarCrossref
    34.
    DeLand  FH, North  WA.  Relationship between liver size and body size.   Radiology. 1968;91(6):1195-1198. doi:10.1148/91.6.1195PubMedGoogle ScholarCrossref
    35.
    Freeman  RB, Wiesner  RH, Edwards  E, Harper  A, Merion  R, Wolfe  R; United Network for Organ Sharing/Organ Procurement and Intestine Transplantation Network Liver and Transplantation Committee.  Results of the first year of the new liver allocation plan.   Liver Transpl. 2004;10(1):7-15. doi:10.1002/lt.20024 PubMedGoogle ScholarCrossref
    36.
    Kanwal  F, Dulai  GS, Spiegel  BMR, Yee  HF, Gralnek  IM.  A comparison of liver transplantation outcomes in the pre- vs. post-MELD eras.   Aliment Pharmacol Ther. 2005;21(2):169-177. doi:10.1111/j.1365-2036.2005.02321.x PubMedGoogle ScholarCrossref
    37.
    Cullaro  G, Sarkar  M, Lai  JC.  Sex-based disparities in delisting for being “too sick” for liver transplantation.   Am J Transplant. 2018;18(5):1214-1219. doi:10.1111/ajt.14608 PubMedGoogle ScholarCrossref
    38.
    Ge  J, Gilroy  R, Lai  JC.  Receipt of a pediatric liver offer as the first offer reduces waitlist mortality for adult women.   Hepatology. 2018;68(3):1101-1110. doi:10.1002/hep.29906 PubMedGoogle ScholarCrossref
    39.
    Wan  P, Li  Q, Zhang  J, Xia  Q.  Right lobe split liver transplantation versus whole liver transplantation in adult recipients: a systematic review and meta-analysis.   Liver Transpl. 2015;21(7):928-943. doi:10.1002/lt.24135 PubMedGoogle ScholarCrossref
    40.
    Deshpande  R, Hirose  R, Mulligan  D.  Liver allocation and distribution: time for a change.   Curr Opin Organ Transplant. 2017;22(2):162-168. doi:10.1097/MOT.0000000000000397 PubMedGoogle ScholarCrossref
    41.
    Bowring  MG, Zhou  S, Chow  EKH, Massie  AB, Segev  DL, Gentry  SE.  Geographic disparity in deceased donor liver transplant rates following Share 35.   Transplantation. 2019;103(10):2113-2120. doi:10.1097/TP.0000000000002643 PubMedGoogle ScholarCrossref
    42.
    Goldberg  DS, French  B, Sahota  G, Wallace  AE, Lewis  JD, Halpern  SD.  Use of population-based data to demonstrate how waitlist-based metrics overestimate geographic disparities in access to liver transplant care.   Am J Transplant. 2016;16(10):2903-2911. doi:10.1111/ajt.13820 PubMedGoogle ScholarCrossref
    43.
    Mehrotra  S, Kilambi  V, Bui  K,  et al.  A concentric neighborhood solution to disparity in liver access that contains current UNOS districts.   Transplantation. 2018;102(2):255-278. doi:10.1097/TP.0000000000001934 PubMedGoogle ScholarCrossref
    44.
    Parikh  ND, Marrero  WJ, Sonnenday  CJ, Lok  AS, Hutton  DW, Lavieri  MS.  Population-based analysis and projections of liver supply under redistricting.   Transplantation. 2017;101(9):2048-2055. doi:10.1097/TP.0000000000001785 PubMedGoogle ScholarCrossref
    45.
    Reed  A, Chapman  WC, Knechtle  S, Chavin  K, Gilroy  R, Klintmalm  GBG.  Equalizing MELD scores over broad geographies is not the most efficacious way to allocate a scarce resource in a value-based environment.   Ann Surg. 2015;262(2):220-223. doi:10.1097/SLA.0000000000001331 PubMedGoogle ScholarCrossref
    46.
    Snyder  JJ, Salkowski  N, Wey  A, Pyke  J, Israni  AK, Kasiske  BL.  Organ distribution without geographic boundaries: a possible framework for organ allocation.   Am J Transplant. 2018;18(11):2635-2640. doi:10.1111/ajt.15115 PubMedGoogle ScholarCrossref
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