Buscar en
Annals of Hepatology
Toda la web
Inicio Annals of Hepatology Waitlist mortality in patients with autoimmune liver diseases
Journal Information
Share
Share
More article options
Visits
38
Original article
Open Access
Waitlist mortality in patients with autoimmune liver diseases
Visits
...
Daniela Goyesa, Romelia Barbab, Esli Medina-Moralesb, Behnam Saberib, Vilas Patwardhanb, Alan Bonderb,
Corresponding author
abonder@bidmc.harvard.edu

Corresponding author.
a Department of Medicine, Loyola Medicine—MacNeal Hospital, Berwyn, IL, United States
b Division of Gastroenterology, Hepatology, and Nutrition, Beth Israel Deaconess Medical Center, Boston, MA, United States
Article information
Abstract
Full Text
Bibliography
Statistics
Figures (1)
Abstract
Introduction and Objectives

Autoimmune liver diseases such as autoimmune hepatitis, primary biliary cholangitis, and primary sclerosing cholangitis are the primary indication for ∼24% of total liver transplants. The liver transplant allocation system is currently based upon the Model for End-Stage Liver Disease and it often underestimates the severity of autoimmune liver diseases. We aim to compare the rate of adverse waitlist removal among patients with all autoimmune liver diseases and other indications for liver transplant in the Model for End-Stage Liver -Na era.

Materials and Methods

Using the United Network for Organ Sharing database, we identified all patients listed for liver transplant from 2016 to 2019. The outcome of interest was waitlist survival defined as the composite outcome of death or removal for clinical deterioration. Competing risk analysis was used to evaluate the waitlist survival.

Results

Patients with autoimmune hepatitis had a higher risk of being removed from the waitlist for death or clinical deterioration (SHR 1.37, 95% CI 1.08–1.72; P<0.007), followed by primary biliary cholangitis (SHR 1.34, 95% CI 1.07–1.68; P<0.011).

Conclusions

High waitlist death or removal for clinical deterioration was observed in patients with PBC and AIH when compared to other etiologies. It may be useful to reassess the process of awarding MELD exception points to mitigate such disparity.

Keywords:
Disparities
Allocation
Autoimmune liver diseases
Abbreviation:
AIH
ALD
CI
HBV
HCV
IQR
LT
MELD
NASH
PBC
PSC
SHR
UNOS
Full Text
1Introduction

Autoimmune liver diseases, including autoimmune hepatitis (AIH), primary biliary cholangitis (PBC), and primary sclerosing cholangitis (PSC), are the primary indication for ∼24% of total liver transplants (LT) in the United States [1]. Despite their autoimmune nature, they have distinct clinical course, management, and outcomes. For instance, in patients with AIH, immunosuppressive therapy may impact the LT-free survival rate [2]. On the other hand, immunotherapy in patients with PBC remains a challenge due to a lack of target definition, leaving them without effective therapy while on the waitlist [3]. Patients with PSC seem to be at a lower risk of death on the waitlist. This may be attributed to exception rules that have been established within the United Network for Organ Sharing (UNOS) [4]. The LT allocation system is currently based upon the Model for End-Stage Liver Disease (MELD). Studies have shown that the MELD score often underestimates the severity of advanced liver diseases, especially in patients with autoimmune liver diseases who have higher waitlist mortality compared with waitlisted patients with other etiologies of end-stage liver disease [5–7]. While these studies have demonstrated increased waitlist mortality among candidates with PBC [5], those studies fail in accounting for AIH and PSC candidates. Therefore, we aim to compare the rate of adverse waitlist removal among patients with all autoimmune liver diseases and other indications for LT in the MELD-Na era.

2Methods2.1Study population

Using the UNOS database, we identified all patients listed for LT from 2016 to 2019. These dates were chosen to allow 3 years of analysis data after the implementation of MELD-Na. Although data from 2020 was available, it was not considered because of the unpredictable impact of the COVID-19 pandemic. We excluded children recipients (<18 years old), live donor recipients, multiple-organ transplants, acute liver failure, those who had a history of a previous liver transplant, and those who received exception points. This study was exempt from institutional review board approval as the database is publicly available and contains de–identified patient data.

2.2Definition of outcomes

The outcome of interest was waitlist survival defined as the composite outcome of death or removal for the clinical deterioration that corresponds to UNOS removal codes 5, 8, and 13. We compared waitlist survival among groups using competing risk analysis with liver transplantation as a competing risk.

2.3Statistical analysis

The primary diagnosis was used to stratify clinical and demographic characteristics. Categorical variables were presented as frequencies and percentages and were compared using Pearson's chi-squared test (χ2). Continuous variables that were not normally distributed were reported as medians and interquartile range (IQR) and were analyzed with the Kruskal‐Wallis test.

Competing risk analysis was used to evaluate the cumulative incidence of death or delisting for deterioration. Univariate analysis was performed for each variable to determine which covariates would be included in the adjusted model. Variables with a p< 0.10 in the univariate analysis and those of clinical significance were included in the model. Patients with incomplete data were excluded from the multivariable analysis. The final model was adjusted for underlaying etiology, age, sex, race/ethnicity, blood type, diabetes, obesity, laboratory MELD score, UNOS region. We report adjusted associations of covariates and overall survival as sub-distribution hazard ratio (SHR) and 95% confidence intervals (CI). Analyses were performed using Stata version 14.0 (College Station, TX StataCorp LP).

3Results3.1Characteristics of the population: descriptive statistics

Baseline patient characteristics are detailed in Table 1. We identified 36,537 patients of which 1301 (4%) were listed with AIH, 1203 (3%) with PBC, 1658 (5%) with PSC, 983 (3%) with hepatitis B virus (HBV), 9032 (25%) with hepatitis C virus (HCV), 12,890 (35%) with alcoholic liver disease (ALD), and 9470 (26%) with non-alcoholic steatohepatitis (NASH). A greater proportion of females had AIH (72%) and PBC (86%). Diabetes (59%) and obesity (66%) were more prevalent in patients with NASH. The median MELD score was higher for ALD and AIH (21 and 19), respectively.

Table 1.

Baseline demographic and clinical characteristics.

  AIH n = 1301  PBC n = 1203  PSC n = 1658  HBV n = 983  HCV n = 9032  ALD n = 12,890  NASH n = 9470 
Age, median (IQR)  52 (38–62)  59 (52–65)  49 (36–59)  57 (49–63)  61 (56–64)  54 (47–61)  61 (55–66) 
Sex (male), n (%)  359 (28)  172 (141098 (66)  791 (81)  6788 (75)  9278 (72)  5133 (54) 
Race/ethnicity, n (%)               
White  732 (56)  815 (68)  1229 (74)  272 (28)  6006 (66)  9808 (76)  7249 (76) 
Black  234 (18)  92 (8)  275 (17122 (121107 (12454 (4166 (2
Hispanic  277 (21)  237 (20)  95 (642 (442 (42158 (171716 (18
Asian  37 (338 (347 (3526 (53)  282 (3231 (2200 (2
Other  22 (223 (214 (122 (2168 (2266 (2159 (2
Blood type, n (%)               
O  622 (48)  566 (47)  748 (45)  398 (40)  4249 (47)  5974 (46)  4440 (47) 
A  469 (36)  471 (39)  614 (37)  306 (31)  3384 (37)  4970 (38)  3632 (38) 
B  167 (13128 (11230 (14225 (23)  1117 (121460 (111040 (11
AB  44 (340 (368 (455 (6300 (3513 (4378 (4
Diabetes, n (%)  317 (24)  232 (19)  166 (10262 (27)  2383 (26)  2468 (19)  5550 (59) 
Obesity, n (%)  490 (38)  344 (29)  303(18)  242 (25)  3367 (37)  4592 (36)  6277 (66) 
MELD score, median at listing (IQR)  19 (14-27)  17 (13-23)  17 (11-23)  13 (8-23)  12 (8-19)  21 (15-29)  17 (12-23) 
UNOS region, n (%)               
1  47 (464 (561 (448 (5479 (5901 (7420 (4
2  112 (9116 (10153 (9129 (131320 (151737 (13936 (10
3  214 (16193 (16265 (16115 (121269 (141703 (131525 (16
4  190 (15157 (13143 (981 (81237 (141521 (12110 (12
5  238 (18206 (17209 (13203 (21)  1439 (162036 (161376 (15
6  39 (336 (378 (553 (5391 (4373 (3212 (2
7  85 (7111 (9142 (988 (9533 (61186 (9729 (8
8  85 (670 (6117 (721 (2.1)  469 (5726 (6530 (6
9  77 (667 (6117 (7144 (15526 (6755 (6439 (5
10  108 (8105 (9200 (1256 (6644 (71062 (81024 (11
11  108 (880 (7175 (1146 (5728 (8917 (71189 (13
Waitlist time in days, median (IQR)  130 (22–354)  165 (42–373)  167 (48–395)  240 (46–434)  218 (82–374)  106 (16-339)  160 (42–349) 

HBV, hepatitis B virus. HCV, hepatitis C virus. ALD, alcohol related liver disease. NASH, nonalcoholic steatohepatitis. AIH, autoimmune hepatitis. MELD, model for end-stage liver disease. UNOS, United Network for Organ Sharing. IQR, interquartile range.

3.2Waitlist survival: analytical statistic

On competing risk analysis patients with AIH had a higher risk of being removed from the waitlist for death or clinical deterioration (SHR 1.37, 95% CI 1.08–1.72; P<0.007), followed by PBC (SHR 1.34, 95% CI 1.07–1.68; P<0.011) and HCV (SHR 1.29, 95% CI 1.06–1.57; P<0.008) when compared with other groups (Table 2, Fig. 1).

Table 2.

Multivariable analysis.

Variables  SHR*  95% CI*  p Value* 
HBV  Ref     
HCV  1.29  1.06 – 1.57  0.008 
ALD  0.98  0.81 – 1.19  0.877 
NASH  1.19  0.98 – 1.44  0.071 
PBC  1.34  1.07 – 1.68  0.011 
PSC  1.15  0.91 – 1.45  0.236 
AIH  1.37  1.08 – 1.72  0.007 

HBV, hepatitis B virus. HCV, hepatitis C virus. ALD, alcohol related liver disease. NASH, nonalcoholic steatohepatitis. AIH, autoimmune hepatitis. MELD, model for end-stage liver disease. SHR, subdistribution hazard ratio. Ref, reference. CI, confidence interval.

*Multivariable analysis adjusted for age, sex, race/ethnicity, blood type, diabetes, obesity, laboratory MELD score, and UNOS region.

Fig. 1.

Competing risk regression demonstrating relative removal from waitlist for death or clinical deterioration.

(0.29MB).
4Discussion

In this study, we sought to compare the rate of adverse waitlist removal among all primary diagnoses after the MELD-Na score was adopted using the UNOS database. We found high waitlist mortality in candidates with PBC and AIH when compared with other populations.

Our study is consistent with prior retrospective analysis. For instance, among patients with cirrhosis and acute-on-chronic liver failure, Singal et al. found a cumulative incidence of waitlist mortality of 20.1% in patients with PBC within 90 days of listing. It was the highest incidence when compared with other etiologies [7]. Consistently, Zhou et al. showed higher waitlist mortality for PBC (20%) when compared with most common etiologies such as ALD (13%) and NASH (18%) under the MELD-Na allocation system [5]. Several non-specific symptoms frequently impair the quality of life of patients with PBC. These symptoms include but are not limited to intractable pruritus [8], fatigue [9], and anxiety [10]. MELD-Na score is a metric of waitlist mortality and unfortunately may not adequately reflect a candidate's health status. In other words, it does not consider the candidate's quality of life [11]. Granting exception points for these patients is a potential alternative to accelerate their access to transplants. However, since symptoms such as pruritus, fatigue, or metabolic bone disease do not correlate with mortality, the regional review board may not approve the exception points [12]. However, the addition of variables that are meaningfully associated with short-term mortality, as seen with the recent MELD 3.0, can improve mortality prediction compared to the current system. For instance, there is evidence that shows the correlation between lower albumin and symptoms such as fatigue [9]. MELD 3.0 has included albumin in its model given its higher coefficient, this potentially can improve patient allocation [13].

As we previously found, there is increased mortality in patients with cirrhosis secondary to AIH when compared with other autoimmune liver diseases [14]. It can be entirely attributed to liver-related complications and no other factors, as va den Brand et al. shown. Their study found that liver disease was responsible for approximately one-third of the deaths in patients with AIH [15]. However, it has been documented that in patients with AIH on long‐term use of non‐steroidal immune suppressive therapy, the overall risk of extrahepatic malignancy is increased compared to the general population [16]. Furthermore, poorly controlled disease due to non-compliance, partial compliance, or a true non-response to standard treatment, could also remove these patients from the list secondary to flares [14, 17].

Patients with a diagnosis of AIH and PBC may have difficulty obtaining deceased donors due to their low MELD scores (Table 1) and the disproportion between available organs and candidates waiting for a LT [12]. This may reflect the underestimation of the severity of the disease. Furthermore, patients with PBC and AIH are usually middle-aged white women without an increased risk of non-liver comorbidities that can potentially delay the referral to LT [18]. Education about the timing for referral could improve the waitlist outcomes among these populations, however, it may not be enough. As previously stated, modification in the current score system can help to mitigate such disparities. MELD 3.0 has added another point to the female sex compared to a male patient with identical laboratory test values, it was previously associated with a 3% increase in 90-day mortality [13].

The strengths of our study include the use of a well-characterized, nationwide database of transplant candidates, allowing our results to be generalizable to most US transplant centers. Also, the use of competing risk analysis allowed for simultaneous assessment of the effects of competing risks such as waitlist removal for death or deterioration and transplant. However, this study is limited by the retrospective nature of the analysis. Therefore, the limitation in the available data creates difficulties in drawing more conclusive arguments.

5Conclusion

A persistent high waitlist death or removal for clinical deterioration was observed in patients with PBC and AIH when compared to other etiologies. Focused efforts to more optimally manage these patients before listing for LT, such as adequate treatment with standard first-line therapies, management of comorbidities and extrahepatic manifestations may potentially lower disease severity. It may also be useful to reassess the process of awarding MELD exception points for certain patients with autoimmune liver diseases to mitigate this disparity.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Authors contribution

All persons who meet authorship criteria are listed as authors, and all authors certify that they have participated sufficiently in the work to take public responsibility for the content, including participation in the concept, design, analysis, writing, or revision of the manuscript. All the authors gave their final approval of the version to be published, and agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the article are appropriately investigated and resolved. Study concept and design D.G., R.B., E.M.M., B.S., V.P., & A.B.; acquisition of data D.G., & A.B.; analysis and interpretation of data D.G., & A.B.; drafting of the manuscript D.G., R.B., E.M.M.; critical revision of the manuscript for important intellectual content D.G., B.S., V.P., & A.B.; statistical analysis D.G.; study supervision B.S., V.P., & A.B.

Data availability

This work was conducted using the United Network for Organ Sharing https://unos.org/data/

Acknowledgements

We acknowledge the efforts of the UNOS/OPTN in the creation of this database.

References
[1]
J.A. Ilyas, C.A. O'Mahony, J.M. Vierling.
Liver transplantation in autoimmune liver diseases.
Best Pract Res Clin Gastroenterol [Internet], 25 (2011), pp. 765-782
[2]
M. Biewenga, A. Inderson, M.E. Tushuizen, A.S.L.P. Crobach, B. van Hoek.
Early Predictors of Short-Term Prognosis in Acute and Acute Severe Autoimmune Hepatitis.
Liver Transplant, 26 (2020), pp. 1573-1581
[3]
A.F. Gulamhusein, G.M. Hirschfield.
Primary biliary cholangitis: pathogenesis and therapeutic opportunities.
Nat Rev Gastroenterol Hepatol [Internet], 17 (2020), pp. 93-110
[4]
K. Staufer, D. Kivaranovic, S. Rasoul-Rockenschaub, T. Soliman, M. Trauner, G. Berlakovich.
Waitlist mortality and post-transplant survival in patients with cholestatic liver disease – Impact of changes in allocation policy.
Hpb [Internet], 20 (2018), pp. 916-924
[5]
K. Zhou, J.L. Dodge, E. Xu, J. Emamaullee, J. Kahn.
Excess liver transplant waitlist mortality for patients with primary biliary cholangitis under MELD-Na allocation.
Clin Transplant, (2021), pp. 6-13
[6]
G. Cholankeril, H.C. Gonzalez, S.K. Satapathy, S.A. Gonzalez, M. Hu, M.A. Khan, et al.
Increased Waitlist Mortality and Lower Rate for Liver Transplantation in Hispanic Patients With Primary Biliary Cholangitis.
Clin Gastroenterol Hepatol [Internet], 16 (2018), pp. 965-973e2
[7]
A.K. Singal, R.J. Wong, R. Jalan, S. Asrani, Y.F. Kuo.
Primary biliary cholangitis has the highest waitlist mortality in patients with cirrhosis and acute on chronic liver failure awaiting liver transplant.
Clin Transplant, 35 (2021),
[8]
H.D. Trivedi, B. Lizaola, E.B. Tapper, A. Bonder.
Management of Pruritus in Primary Biliary Cholangitis: a Narrative Review.
Am J Med [Internet], 130 (2017), pp. 744.e1-744.e7
[9]
L. Montali, A. Gragnano, M. Miglioretti, A. Frigerio, L. Vecchio, A. Gerussi, et al.
Quality of life in patients with primary biliary cholangitis: a cross-geographical comparison.
J Transl Autoimmun, 4 (2021),
[10]
T. Beiko, C. Strange.
Anxiety and depression in patients with alpha-1 antitrypsin deficiency: current insights and impact on quality of life.
Ther Clin Risk Manag, 15 (2019), pp. 959-964
[11]
L.C. Pullen.
Liver Allocation for Rare Disease: does the MELD Score Suffice?.
Am J Transplant, 20 (2020), pp. 3271-3272
[12]
Vandana Khungar, M.D. Ms.
David Seth Goldberg M. Liver Transplantation for Cholestatic Liver Diseases in Adults.
Clin Liver Dis, 20 (2016), pp. 191-203
[13]
W.R. Kim, A. Mannalithara, J.K. Heimbach, P.S. Kamath, S.K. Asrani, S.W. Biggins, et al.
MELD 3.0: the Model for End-Stage Liver Disease Updated for the Modern Era.
Gastroenterology [Internet], 161 (2021), pp. 1887-1895e4
[14]
Danford Suri, Bonder Patwardhan.
Mortality on the UNOS Waitlist for Patients with Autoimmune Liver Disease.
J Clin Med, 9 (2020), pp. 319
[15]
F.F. van den Brand, K.S. van der Veen, Y.S. de Boer, N.M. van Gerven, B.I. Lissenberg-Witte, U. Beuers, et al.
Increased Mortality Among Patients With vs Without Cirrhosis and Autoimmune Hepatitis.
Clin Gastroenterol Hepatol [Internet], 17 (2019), pp. 940-947e2
[16]
M.J. Mayo.
Management of autoimmune hepatitis.
Curr Opin Gastroenterol, 27 (2011), pp. 224-230
[17]
A.W. Lohse, O. Chazouillères, G. Dalekos, J. Drenth, M. Heneghan, H. Hofer, et al.
EASL clinical practice guidelines: autoimmune hepatitis.
J Hepatol [Internet], 63 (2015), pp. 971-1004
[18]
M. Sayiner, M. Stepanova, L. De Avila, P. Golabi, A. Racila, Z.M. Younossi.
Outcomes of Liver Transplant Candidates with Primary Biliary Cholangitis: the Data from the Scientific Registry of Transplant Recipients.
Dig Dis Sci [Internet], 65 (2020), pp. 416-422
Copyright © 2022. Fundación Clínica Médica Sur, A.C.
Article options
Tools
es en pt

¿Es usted profesional sanitario apto para prescribir o dispensar medicamentos?

Are you a health professional able to prescribe or dispense drugs?

Você é um profissional de saúde habilitado a prescrever ou dispensar medicamentos