Are MELD and MELDNa Still Reliable Tools to Predict Mortality on the Liver Transplant Waiting List? : Transplantation

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Are MELD and MELDNa Still Reliable Tools to Predict Mortality on the Liver Transplant Waiting List?

Tejedor, Marta MD1; Selzner, Nazia MD2; Berenguer, Marina MD3,4,5

Author Information
doi: 10.1097/TP.0000000000004163
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Since the first liver transplant (LT) in 1963,1 major advances have improved the outcomes of LT, which is now widely accepted as the definitive treatment for patients with end-stage liver disease (ESLD) or with liver cancer. However, the scarcity of donor organs for the increasing pool of potential recipients limits the access of patients to this life-saving procedure. For instance, in 2019 in the United States, 12 767 new recipients were added to the waiting list (WL), whereas only 8896 transplants were performed2 and about 25% of patients died on the WL. Consequently, the question of how to best allocate these organs is of paramount importance.

Several ethical principles are used to prioritize candidates on the WL3,4:

  1. Equity: this principle can be divided into 2 aspects. (a) Horizontal equity: based on applying equal treatment for equal needs, regardless of baseline disease or cause. It aims to guarantee fair and equal access to transplantation for all patients. (b) Vertical equity: it prioritizes patients based on the “sickest-first” policy (urgency model). The downside of this system is that the sickest patients likely have more complicated posttransplant outcomes.5
  2. Utility: it focuses on obtaining the greatest benefit posttransplant, measured by overall survival and disease-free survival posttransplant.3 The main disadvantage is that by aiming for the longest survival posttransplantation, it prioritizes patients who would likely also have a prolonged survival on the WL and raises the question of futility.
  3. Benefit: it balances the survival gain from transplantation by comparison with the best alternative therapies. The advantage of this principle is its ability to consider both pretransplant and posttransplant outcomes. Depending on the time horizon considered to assess the outcome of transplantation, it can reflect a utility allocation model (when the time horizon is too long) or an urgency model (when too short). In general, 5 to 10 y posttransplant is considered the ideal time to assess the usefulness of this approach.4,6,7

In summary, the ideal allocation system should compromise the reduction of waitlist mortality with the maximization of benefit obtained posttransplant, in a transparent, objective, and reproducible manner while guaranteeing equal access for all patients. Because such objectives are essentially contradictory, these policies should be periodically reviewed and adjusted according to changes in the target population.

This review intends to discuss the challenges associated with the current scoring systems based on the above-mentioned goals and explore future perspectives.


Over recent decades, a major change in the profile of waitlisted recipients has been noticeable, namely patients are older and have more advanced liver disease with concomitant comorbidities (Figure 1).2,8,9 For example, in 2019, 39% of the transplanted recipients had a model for end-stage liver disease (MELD) score ≥30 in the United States compared with 25% in 2009.2 A similar shift is also reported in Europe, with an increase to 6% in 2020 from 4% in 2011 for patients with a MELD score >30, whereas the proportions of patients with low or intermediate scores remain stable. Additionally, similar to aging in the general population, LT recipients are also older, with 17% of patients being aged ≥65 y in 2020 compared with 10% in 2011.8

A, Changes in the profile of waitlisted patients over the past 10 y in the United States.2 , 8 , 9 B, Severity of liver disease at the time of transplantation in the United States over the past 10 y.2 , 8 , 9 ALD, alcoholic liver disease; ALF, acute liver failure; HCC, hepatocellular carcinoma; HCV, hepatitis C virus; MELD, model for end-stage liver disease; NAFLD, nonalcoholic fatty liver disease and steatohepatitis.

Notably, the cause of liver disease has also evolved during the past decade, with alcoholic liver disease, nonalcoholic steatohepatitis (NASH), and NASH-related hepatocellular carcinoma (HCC) being the current leading LT indications in the United States compared with hepatitis C virus (HCV) a decade ago.2,9 This shift in indication for LT is because of both the impact of direct antiviral agents with >99% rate of viral eradication and the increasing worldwide rates of obesity-related liver disease. It is also expected that early treatment of HCV with direct antiviral agents will result in a reduction of the HCC rate in the future.10-12


Originally, the MELD score was developed to estimate short-term survival after insertion of a transjugular intrahepatic portosystemic shunt,13 and subsequently, it was validated as an indicator of survival in ESLD.14 Multiple derivations have been developed ever since (Table 1).13-34

TABLE 1. - Main MELD derivations developed over recent years
Score Formula Comment Reference
MELD 9.57 × loge Cr + 3.78 × loge bilirubin + 11.2 × loge INR + 6.43 Initially developed to assess short-term prognosis after TIPS insertion, 13 then validated for ESLD.Cr bounded at 1 and 4 mg/dL. Kamath et al 14
MELDNa MELDNa – [0.025 × MELD × (140 – Na)] + 140 Improves predictive capacity of MELD. The effect of natremia is greater in patients with low MELD scores.Na bounded at 125 and 140 mEq/L. Kim et al 15
In 2016, OPTN modified the original formulae to the currently used ones 16 :MELD: 0.957 × loge Cr + 0.378 × loge bilirubin + 0.112 × loge INR + 0.643If MELD >11 – >MELDNa: MELD + 1.32 × (137 – Na) – [0.033 × MELD × (137 – Na)].Na bound to 125 and 137 mEq/L
MELD derivations Formula Comment Reference
ΔMELD (2nd MELD – 1st MELD)/interval period Magnitude and direction of change in MELD score over a 1-mo interval has prognostic value.Cr bounded at 4 mg/dL. MELD bound at 40. Merion et al 17
MESO index (MELD/Na) × 10 Better prognostic ability than MELD. Correlates well with HVPG. Amplifies the opposing effects of MELD and natremia.Na bounded at 120 and 135 mEq/L. Huo et al 18
iMELD MELD +  0.3 × age – 0.7 × Na + 100 Natremia and age improve predictive capacity of MELD. Luca et al 19
MELD-XI 5.11 × loge bilirubin +  11.76 × loge Cr + 9.44 MELD excluding INR. Useful in patients who have been given anticoagulants. Heuman et al 20
SRTR MELD 1.27 × loge (1 + Cr) + 0.94 × loge (1 + bilirubin) + 1.66 × loge (1 + INR) Updates coefficients to assign lower weight to creatinine and INR and higher weight to bilirubin, improving prediction of WL mortality.Variables not caped. Sharma et al 21
ReFitMELDNa 4.26 × loge bilirubin + 6.79 × loge Cr + 8.29 × loge INR + 0.65 × (140 – Na) – 0.19 × (140 – Na) × bilirubin + 6.33 Updates the coefficients and improves mortality prediction. The decreased impact of natremia on mortality when bilirubin increases is considered.Cr bounded to 0.8 and 3 mg/dL. INR bounded to 1 and 3. Na bounded to 125 and 140 mEq/L. Bilirubin bounded to 1 and 20 mg/dL. Leise et al 22
UKELD 5.39 × loge INR + 1.49 × loge Cr + 3.13 × loge bilirubin – 81.57 × loge Na + 435 A UKELD score >49 predicts >9% 1-y mortality. Barber et al 23
5vMELD MELDNa + 5.28 × (4 – albumin) – 0.14 × MELDNa × (4 – albumin) Finds inverse linear relationship between hypoalbuminemia and WL mortality. Better prognostic ability for MELD <15. Myers et al 24
MELD-sarcopenia MELD + (sarcopenia/MELD) × sarcopenia Improves WL mortality prediction, especially for MELD <15 and refractory ascites. Montano-Loza et al 25
MELD-plus a 11.79 + 2.08 × log (1 + bilirubin) + 2.49 × log (1 + Cr) – 6.05 × log (1 + albumin) + 2.53 × log (81 + INR) + 1.91 × log (1 + WBC) + 0.02 × length of stay + 0.04 × age – 6.63 × log (1 + Na) – 1.45 × log (1 + cholesterol) Accurately stratifies the short-term mortality of cirrhotic patients after hospital admission. Kartoun et al 26
Uncapped-MELD 0.957 × loge Cr + 0.378 × loge bilirubin + 0.112 × loge INR + 0.643 Variables not bound. Removes upper limit of MELD at 40. Improves outcomes of patients with MELD >40. Nadim et al 27
BC-MELD MELD + 3.59 × low SMI + 5.42 × high IMAC + 2.06 × high VSR Improves WL mortality prediction, also in patients with MELD <15. Includes muscularity and visceral adiposity. Hamaguchi et al 28
MELD-GRAIL-Na 29.75 + 10.84 × log INR + 3.04 × log bilirubin – 5.05 × log GRAIL – 0.37 × log Na Replaces Cr with GRAIL. 29 Better predicts outcomes for higher MELD scores and women. Asrani et al 30
MELD-lactate a 0.25 + 5.53 × √lactate + 0.34 × MELD Early predictor of in-hospital mortality in cirrhosis. Better for MELD ≤15 or hospitalized for infection. 31 Sarmast et al 32
Sarco-Model 0.067 × MELDNa + 0.027 × age – 0.113 × TPA – 0.038 × height – 0.44 × albumin Good mortality prediction in sarcopenic patients with MELDNa <20 Lai et al 33
MELD3.0 1.33 (if female) + 4.56 × loge bilirubin + 0.82 × (137 – Na) – 0.24 × (137 – Na) × loge bilirubin + 9.09 × loge INR + 11.14 × loge Cr + 1.85 × (3.5 – albumin) –1.83 × (3.5 – albumin) × loge Cr + 6 Corrects the long-standing underprioritization of female WL candidates. Improves mortality prediction compared with MELDNa. Includes relevant clinical variables such as sex and albumin.Lower limits of bilirubin and INR bounded to 1 mg/dL. Cr bounded to 1 and 3 mg/dL. Na bounded to 125 and 137 mEq/L. Albumin bounded to 1.5 and 3.5 g/dL. Kim et al 34
aDeveloped in hospitalized cirrhotic patients, not specifically for patients on WL.
BC, body composition; Cr, creatinine; ESLD, end-stage liver disease; GRAIL, glomerular filtration rate assessment in liver disease; HVPG, hepatic venous pressure gradient; IMAC, intramuscular adipose tissue content; iMELD, integrated MELD; INR, international normalized ratio; MELD, model for end-stage liver disease; MELDNa, MELD sodium; MELD-XI, MELD excluding international normalized ratio; MESO, MELD-sodium index; 5vMELD, 5-variable MELD; OPTN, Organ Procurement and Transplantation Network; SMI, skeletal muscle mass index; SRTR, Scientific Registry of Transplant Recipients; TIPS, transjugular intrahepatic portosystemic shunt; TPA, total psoas area; UKELD, United Kingdom MELD; VSR, visceral-to-subcutaneous adipose tissue area ratio; WBC, white blood count; WL, waitlist.

Current organ allocation is based on MELD13,14 and MELD sodium (MELDNa) scores15,16 that are both validated to estimate WL mortality,22,35,36 allowing implementation of a sickest-first transplantation policy. The main advantage of these scoring systems is the use of only objective and easily obtainable laboratory parameters (creatinine, international normalized ratio [INR], bilirubin, sodium), avoiding subjective variables such as degree of ascites or encephalopathy.37 This, in turn, allows for policy implementation, because data elements are verifiable and auditable.36

MELD was first implemented in the United States in 2002 and thereafter in almost all Western countries.

Although the difference favoring MELDNa over MELD to predict WL mortality is statistically significant in large series, it is numerically modest (c-statistic 0.883 versus 0.868; P < 0.001).15 Still, the adoption of MELDNa in 2016 in the United States allowed an overall reduction in mortality by 33.5%, whereas transplantation probability increased by 19.7% compared with the MELD era.38

Additionally, the “Share 15” policy was implemented in 2005 in the United States, allowing local livers to be offered nationally if there were no local candidates with a MELD score >15. This MELD cutoff was adopted following a report by Merion et al39 showing a limited net survival benefit at 1 y post-LT for patients with low MELD scores. The “Share 15” policy resulted in a 36% decrease in the transplantation rate of patients with a score <15.40 Similarly, in 2013, “Share 35” policy was adopted in the United States to allow prioritization of patients with a MELD score of ≥35 for regional offers before local recipients with a lower MELD score.41 This policy translated into a 7% increase in LT rate in patients with higher MELD scores, along with a 30% reduction in mortality on the WL.42 More recently, a “Share 21” model has been proposed to replace “Share 15” in the United States to offer organs nationally, provided there are no local candidates with MELD >21.43 Indeed, with the generalization of MELDNa, the score distributions have shifted from low to mid values, both at registration and transplant, and the survival benefit has been shown to be definitive for patients with MELDNa scores of ≥21.38

Most European countries use MELD as the preferred allocation system. A recent study aimed to validate MELDNa in the Eurotransplant region using a retrospective cohort and concluded that MELDNa was better at predicting 90-d WL mortality.44

Since 2014, patients with MELD ≥30 have received national allocation priority in Italy. This policy was associated with a reduction of the median waiting time of these patients (4 versus 12 d; P < 0.001) and a higher probability of being transplanted (hazard ratio [HR], 2.27; 95% confidence interval [CI], 1.78-2.90; P = 0.001).45

With the demographic changes of LT recipients, concerns have been raised regarding the accuracy of MELD and MELDNa to predict WL mortality. Indeed, the discriminative ability of MELD for short-term WL mortality prediction has declined over time in several studies (c-statistic 0.8 in 2003 versus 0.7 in 2015).9,30,44 Some authors, although, have argued that the c-statistic was calculated using a binary method for 90-d mortality without inputs for contributed follow-up time. When a modified Harrell’s c-statistic (which accounts for censoring) is used, the discriminative power of MELD was found to increase to 0.84, not significantly different from 2002.35,46 Finally, a recent validation study in 2 independent cohorts of patients with cirrhosis showed that MELD performance was globally unsatisfactory in predicting mortality with c-statistics ranging from 0.66 to 0.76, with discrimination decreasing over the past 5 y, coincidental with a change in WL demographics.47 Altogether, the data indicate that MELD’s ability to predict outcomes may have decreased for current waitlisted recipients, because they differ from those in the initial studies.

The Problem of Creatinine

Serum creatinine is a variable used in MELD calculation because it is known that renal function has prognostic value in cirrhosis.48 Glomerular filtration rate (GFR) is the best index of renal function,49 with values <60 mL/min representing a ≥50% reduction of normal adult renal function.36 Creatinine is used clinically as a surrogate for GFR given its wide availability and reduced cost.50

Unfortunately, MELD and MELDNa formulae use a lower limit of creatinine of 1 mg/dL (Table 1), thereby disadvantaging patients with low creatinine values but abnormal renal function. A recent study found that 74% of transplant candidates had a creatinine value of <1 mg/dL. Of them, 14% had true GFR of <70 mL/min/1.73 m2.51

Mathematical formulae developed for estimation of GFR, mainly Modification of Diet in Renal Disease (MDRD) and Cockcroft-Gault,52,53 were derived from original cohorts that did not include patients with cirrhosis and are therefore inaccurate for patients with liver disease. Several studies have found that MDRD and Cockcroft-Gault overestimate true GFR in cirrhotic patients by >20% in 46% and 43% of cases, respectively.51,54,55 Younger age and ascites in one study and liver dysfunction, female sex, and sarcopenia in males in the another were independent predictors of overestimation.51,54

To overcome some of these limitations, specific equations have been developed for cirrhotic patients. The Royal Free Hospital cirrhosis GFR includes creatinine, urea, INR, age, sodium, sex, and moderate/severe ascites. Eighty-nine percent of GFR estimates were within 30% of true GFR, compared with 27% to 75% with the traditional MDRD and Chronic Kidney Disease Epidemiology Collaboration. When this new GFR estimation was applied to MELD scoring, 38% of patients received ≥3 points.55 In turn, the GFR assessment in liver disease (GRAIL) includes creatinine, blood urea nitrogen, age, gender, race, and albumin. It correctly classified 75% of patients as having a true GFR of <30 mL/min/1.73 m2 compared with 36% to 53% of patients correctly classified by the Chronic Kidney Disease Epidemiology Collaboration or MDRD (P < 0.001). It was more accurate and precise than the other equations, although all of them overestimated GFR in the presence of ascites.29

The Problem of INR

INR is a correction formula that adjusts for the variable sensitivities of different thromboplastin reagents and allows standardization of prothrombin time ratio to an international reference thromboplastin standard. It was exclusively developed to monitor vitamin K antagonist therapy. Several studies have shown that its use is not appropriate for patients with liver dysfunction because it may vary for a single sample according to the thromboplastin reagent used, the type of INR measuring device, and international sensitivity index chosen.56-58 For instance, a study by Trotter et al showed a 26% variation in INR results across different laboratories, resulting in a 20% difference in the MELD score (up to 9 points difference). Said variability seemed to increase with higher INR values.56 These findings raise a concern that priority for transplantation may be significantly altered on the basis of interlaboratory variations that may not reflect the true severity of illness. A couple of liver-specific recalibrated-INR alternatives have been proposed but have not been widely adopted.59,60

Other Factors That Impact WL Survival

In addition to the severity of liver disease, several other factors influence WL mortality (Figure 2). These include the following:

Factors influencing waitlist mortality. INR, international normalized ratio.


Because of the lower prevalence of viral-related disease and HCC among women, currently, only 40% of waitlisted patients are women.2,61-64 However, it is expected that the proportion of listed women will grow with an increasing prevalence of NASH.65 Several studies have shown women to be at higher risk of death or drop-out on the WL and less likely to receive an organ.63,66 Three large retrospective studies reviewed these issues using the United Network for Organ Sharing (UNOS) and the Scientific Registry of Transplant Recipients data sets. Women had 30% increased odds of death and a 10% higher risk of delisting for becoming too sick for LT compared with men.67,68 Delisted women were older, shorter, had a higher prevalence of encephalopathy, and were more often unable to care for themselves than men, despite similar MELDNa at listing or delisting. Importantly, there was no difference in survival between sexes neither among the delisted patients nor among the frailest patients who underwent transplantation. Taken together, this suggests that women are perceived to be frailer than men.68 A recent study found that women with cirrhosis had lower transplant rates despite not showing a difference in their all-cause or liver-related mortality compared with men.69

Additionally, the MELD allocation system is associated with a further reduction in rates of transplantation among women compared with the previous era (reduction compared with men by 9% in the pre-MELD era, versus by 14% in the MELD era, P <0.05). This difference was more evident for MELD scores ≥15 (reduction compared with men by 20% for MELD scores >20 and by 12% for MELD scores >30; P < 0.05).70

The most accepted explanation for the sex-based difference in access to LT is the use of creatinine for MELD, because renal dysfunction tends to be underestimated in women because of their lower muscle mass.71-73 In the study by Cholongitas et al,74 the MELD score correction by renal function accounted for a 2 to 3 MELD points increase in 65% of female LT candidates. Additionally, women have smaller stature than men, resulting in a more limited donor pool. Several studies have shown that shorter individuals have a higher WL mortality75 and are more likely to have organs declined.76 This disparity in organ offer declines was more notable for smaller women than for smaller men. A study assessed the monthly rate of transplant and found that women had a 25% lower probability of receiving an organ than men. After controlling for MELD score and liver volume and weight, women still had a 13% lower chance of transplant. At least half of the sex disparity remained unexplained.77

Race and Ethnicity

The racial composition of the WL has remained relatively unchanged over the past 10 y in the United States: 69% White, 7% Black, 18% Hispanic, and 5% Asian.2 The UK WL presents a similar distribution (87% White, 8% Asian, 3% Black, 1% Chinese, and 1% other ethnicities).61

In the United States, ethnic minorities encounter several barriers at each step of the LT process, namely referral for LT evaluation, access to WL, and transplantation itself.78

In the ethnic minority groups, the prevalence of viral hepatitis and associated HCC is greater than in White patients.79-81 NASH is rising in the Hispanic population, currently the leading cause of liver disease in this subgroup.82

Comprehensive US national data are not available on the exact burden of ESLD, but it seems that only a minority undergo transplant assessment. Relative to White patients, African Americans were less likely to be referred and examined for LT, even with similar risks of death without an LT.83,84 The transplant to listing ratio (number of patients who underwent LT by number of patients listed for LT) evaluates how efficiently patients are transplanted while on the WL. Black patients had the highest transplant to listing ratio (0.53) compared with White (0.49), Asian (0.46), and Hispanic (0.46) patients (all P < 0.01).85

Moylan et al compared 2 large UNOS cohorts of pre- and post-MELD African Americans and White patients listed for LT. In the pre-MELD era, Black patients had higher mortality (odds ratio [OR], 1.5; 95% CI, 1.15-1.98) and lower probability of receiving an organ (OR, 0.75; 95% CI, 0.59-0.97) than White patients. Such differences were found to be reversed in the post-MELD era.67 These results were confirmed by Rosenblatt et al,85 suggesting that MELD does address race disparity once listed.

Although Hispanic patients seem to have similar access to WL compared with Caucasians, they do have lower transplant rates compared with other ethnicities. Additionally, Hispanics were 21% more likely to receive low-quality grafts than Whites (OR, 1.21; P = 0.002), although they had significantly lower graft failure rates compared with White patients.86,87 A study analyzed the influence of race in delisting for being “too sick” for LT, finding that Hispanic patients were delisted at a MELD score of 0.8 points higher than White patients (P < 0.05) and Asians at a MELD score of 1.2 points lower (P = 0.01). Although the reasons for Hispanics to be removed from the list at a higher MELD score remain obscure, it is hypothesized that the increased risk of underlying malignancy in Asian patients could justify removal at lower MELD scores.88

In summary, it is unclear whether race per se influences LT outcomes or whether the observed differences are down mainly to socioeconomic factors. In-depth well-designed studies are needed to clarify this gap in knowledge.

Socioeconomic and Geographic Factors

Social determinants of health are defined as “conditions in the environments under which people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risks.”89 In the context of LT, there are certain vulnerable populations at risk of experiencing inequities in access to healthcare resources and risk of progression to ESLD. These populations include ethnic minorities, patients with low socioeconomic status or low education, and certain geographical profiles.

Type of insurance is directly related to access to healthcare and outcomes. Recently, a US study found that states that expanded Medicaid had 8.3 fewer liver-related deaths per million populations than expected had Medicaid not been expanded.90 Wahid et al showed that improvement in mortality rates of patients with ESLD was associated with both Medicaid expansion and leniency of HCV coverage under Medicaid. It has been postulated that increased insurance coverage allowed for better access to essential treatments to prevent the progression of liver disease.91,92 Patients with private insurance were more likely to be referred, examined, and listed for transplant.83 A transplant registry study found that being covered by Medicare (OR, 0.81; 95% CI, 0.78-0.84) or Medicaid (OR, 0.76; 95% CI, 0.73-0.79) was independently associated with a lower chance of receiving a transplant compared with private insurance.93 Several studies have assessed the role of income and distance to transplant centers in access to LT. Kanwal et al found that patients with lower annual income were less likely to access WL (HR, 0.67; 95% CI, 0.57-0.79) or LT (HR, 0.65; 95% CI, 0.53-0.80). Reasons not to refer patients for LT assessment included age and comorbidities (10%), ongoing alcohol use (38%), and social reasons (17%). In 21% of cases, there was no evident contraindication for LT in patients who were not referred.84 In the United States, greater distance between a patient’s local hospital and a transplant center was associated with a lower likelihood of being placed on the WL when indicated. Once waitlisted, longer distances were also associated with a lower likelihood of receiving an LT and increased mortality (Figure 3).94-101

Density of transplant programs across different countries. For example, the United States is 18 times bigger than Spain and has 7 times its population but only triple the amount of transplant centers. Traveling distances between place of residence and transplant center can determine the likelihood of being referred for a transplant assessment or receiving a transplant if it involves relocation of patient and family. In the United States, a threshold of 160 km (100 miles) has been found to mark such a difference.94 , 97-101

The source for inequities can also arise from implicit bias of the healthcare providers. It is defined as attitudes, beliefs, or thoughts beyond the realm of conscious awareness that affect how we view and interact with our environment.102,103 This may affect how patients are viewed and result in negative attitudes toward vulnerable groups, with consequent lower quality of care. In a survey of 61 transplant programs in the United States, inadequate or unstable health insurance (reported by 69% of the programs), chaotic social environment (64%), lack of a caregiver (61%), lack of transportation (49%), low educational level (36%), inadequate housing (23%), language barrier (20%), lack of a reliable contact the patient (16%), difficulty in obtaining child care (12%), and food insecurity (8%) were identified as barriers to listing. Programs with higher proportions of minority patients were more likely to use a formal tool for evaluating social support (71% versus 37%; P = 0.01). Sixty-seven percent of the programs reported that when patients had evident social barriers to transplantation, the formal evaluation process may not be initiated at all; this was not statistically associated with the proportions of minority, rural, and Medicaid patients in those programs.104 Importantly, uneducated patients were removed from the WL for being “too sick” for LT at a MELD score of 2.7 points lower than educated patients (P = 0.02).88

A recent Canadian study did not demonstrate social determinant differences (including patient’s income and education) between patients who ultimately received a transplant and those who did not, nor is there any significant association between listing MELDNa scores and distance from a transplant center. The authors suggest that it may be because of universal healthcare availability, allowing for listing and delisting to be based on disease severity alone.105

Determinants in Low MELD Patients

More than half of patients are initially listed with low MELD scores (<16).106 Although mortality is lower in this subgroup of cirrhotic patients (12% with MELDNa ≤15 versus 41% with MELDNa >15, P < 0.001), half of them die because of liver-related complications.107,108 Hyponatremia, ascites, hypoalbuminemia, and hepatic encephalopathy (HE) are predictors of WL mortality among patients with low MELD score.109 A study showed higher WL mortality in patients with moderate ascites after adjusting for MELDNa (HR, 1.42; 95% CI, 1.28-1.58). The effect was more prominent if MELDNa was <21 (HR, 3.22, 95% CI, 2.64-3.94).110 Hypoalbuminemia is present in nearly half of the low MELD patients listed for LT. Its role in predicting WL mortality was explored in different studies. Each 1 g/dL decrease in albumin concentration was associated with 1.44 times higher mortality after adjusting for MELD. The addition of albumin to MELD offered a significantly greater benefit in mortality prediction for patients with low MELD.24,111,112 Additionally, overt HE has been shown to increase WL mortality 3- to 4-fold, and even 5-fold if MELD was <15, independently of MELD or ascites. The addition of 7 MELD points was able to correct the mortality excess derived from HE.113-115

D’Amico et al116,117 described discrete stages by which patients with cirrhosis can be classified. Each stage predicts 1-y mortality risk. Stage 1 includes cirrhotic patients who have never had varices, ascites, or variceal bleeding. Stage 2 patients have had documented varices in the past but have not developed variceal bleeding or ascites. Stage 3 patients have had variceal bleeding but have never had ascites. Stage 4 patients have had ascites with or without varices but have never had variceal bleeding. Stage 5 patients have had ascites and variceal bleeding. Each stage is associated with an increased risk of death, from 1% to 30%. A case-control study of patients listed for LT with a MELD score ≤20 found that decompensation in this subset of patients has an increased risk of death (stage 3: OR, 8.1 [95% CI, 1.4-48.3]; stage 4: OR, 6.1 [95% CI, 0.9-40.3]; stage 5: OR, 12.9 [95% CI, 1.2-135.7]).118 Therefore, clinical complications of cirrhosis should be considered for close monitoring and prioritization purposes in patients with low MELD scores.

Sarcopenia and Frailty

In cirrhosis, sarcopenia generally refers to loss of muscle mass, whereas frailty focuses on physical frailty and impaired muscle function. There is a degree of overlap between the 2 and in the factors involved in their development. One can lead to the other but they can occur separately.119

Sarcopenia is a clinical syndrome characterized by progressive and generalized loss of muscle mass and strength with an increased propensity for adverse outcomes.120 Sarcopenia is highly prevalent (40%–60%) in patients with ESLD. There is a lack of standardization in its definition and cutoff values or measurement modality.119 Although it can be used as a dichotomic variable, measurement of muscle mass as a continuous variable provides more granular data. Several methods have been proposed to measure sarcopenia in the cirrhotic population. Skeletal muscle index (SMI) assessed by CT constitutes the best-studied technique for estimating sarcopenia in these patients. In an attempt to reach a standard definition of sarcopenia in ESLD, a large multicentric North American study by Carey et al established the cutoff value of SMI <50 cm2/m2 for males and <39 cm2/m2 for females and showed the association of said cutoffs with WL mortality (men: HR, 1.7 [95% CI, 1.1-2.7]; women: HR, 2.8 [95% CI, 1.6-5.1]).121,122 Another recent study used a cohort of young donors to establish the SMI cutoff values for sarcopenia in their population, considering patients with an SMI <1 standard deviation of the sex-specific young adult mean value to have sarcopenia.123 They found similar cutoffs to those validated by Carey et al and a correlation between sarcopenia and WL mortality. Interestingly, low SMI is more prevalent in males and better correlates with mortality than in females, whereas in females, low subcutaneous adiposity is a better predictor of outcome.54,123-125

The concept of frailty was developed in Geriatric medicine to describe a syndrome of decreasing physiological reserve and increasing vulnerability to health stressors.126 It includes a derangement across several organs and systems translating into decreased physical function and functional capacity.119 Several tools have been developed to assess frailty. The most extensively validated one is the Fried frailty score,126 designed to be used in the elderly population. Some small studies applied the Fried frailty score to cirrhotic patients listed for LT, finding an increased WL mortality among frail patients. For every 1-unit increase in the Fried frailty score, a 45% increase in WL mortality was observed.127,128 A specific liver frailty index (LFI) was developed to capture extrahepatic manifestations of cirrhosis such as muscle wasting, malnutrition, and functional decline. It comprises 3 performance-based metrics: grip strength, chair stands, and balance testing. It can improve WL mortality prediction when combined with MELDNa score, correctly reclassifying 19% of cases.129 A multicentric study explored the relationship between portal hypertensive complications and frailty and their impact on WL survival. The odds of being frail were 1.5- and 2.5-fold higher in patients with ascites or encephalopathy, respectively, than in those without. Rates of WL mortality were significantly higher in frail patients, regardless of the presence of ascites or encephalopathy.130 A single time point measurement not only predicts outcomes but also changes over time in LFI. Worsening frailty is associated with increased mortality, regardless of baseline LFI and liver disease severity. A recent study showed that a 0.1-unit change in delta-LFI per 3 mo translates into a 2-fold increase in the risk of death or being delisted. In contrast, improvement of LFI carried a lower risk of death or delisting. These results lay out the importance of early intervention to improve the frailty status of patients with ESLD.131

Do We Have Any Alternatives?

Several MELD derivations scores have been developed over the years (Table 1). As discussed earlier, the only variant from MELD that has reached clinical practice has been the addition of sodium to the original formula.15,109,132-135 Some of the reasons that might have precluded the implementation of new scores include selection biases in retrospective studies or lack of external validation. Important limitations of the MELD-based scoring systems include the lack of donor-recipient (D-R) age matching and their inability to predict transplant futility. Another potential limitation of the MELDNa score is the possibility that diuretic-induced hyponatremia in patients with ascites does not reflect the true severity of liver disease and circulatory dysfunction.136

Special Mentions Deserve a Couple of Recently Developed Updates

In 2020, Asrani et al30 published the MELD-GRAIL-Na that replaces creatinine with GRAIL.29 This score also reestimates coefficients for bilirubin, INR, and sodium (Table 1). MELD-GRAIL-Na better predicted 90-d mortality on the WL compared with MELDNa (c-statistic 0.83 versus 0.82; P < 0.001). This was particularly true for MELD scores of >25 (improvement of 0.02 in the c-statistic value when compared with MELDNa; P < 0.001) and among women, regardless of disease severity (improvements in the c-statistic value between 0.02 and 0.06 depending on MELD severity; P < 0.001). Notably, this study suggests that mortality is underestimated by any of these scores in the lowest MELD deciles.30

In 2021, Kim et al34 published on the MELD3.0. This new version of the MELD score updates creatinine, INR, bilirubin, and sodium coefficients and incorporates important variables, such as sex and albumin (Table 2). Estimated GFR was not considered because most used equations for estimated GFR include race, sex, and creatinine. The inclusion of race was considered problematic because the reasons for worse outcomes on the WL of ethnic minorities are not fully understood and often relate to socioeconomic factors rather than biological reasons. Height was found to be collinear with sex and was not included. The score is built in such a way that for low MELD scores, hyponatremia is the most relevant variable, whereas hypoalbuminemia and female sex gain weight for medium MELD scores. Finally, as creatinine increases, albumin becomes less important, mainly for the highest MELD scores. Therefore, this should not discourage physicians to administer albumin when clinically indicated.137 MELD3.0 improves WL mortality prediction for all the studied population (c-statistic 0.87 for MELD3.0 versus 0.86 for MELDNa; P < 0.01). However, its main advantage is that it corrects the long-standing underprioritization of female WL candidates (correctly reclassified women 14.9% versus 4.1% of men).34 Soon, this score will be presented to the UNOS Liver and Intestine Committee for consideration as the new allocation policy in LT.138

TABLE 2. - Indications of standard exception MELD points in different countries16,125-127
MELD exception United States Canada Spain Austria Slovenia The Netherlands Germany BelgiumLuxembourg Croatia Hungary
Cystic fibrosis
Familial amyloid polyneuropathy
Hepatic artery thrombosis
Hepatopulmonary syndrome
Portopulmonary hypertension
Primary hyperoxaluria
Hepatocellular carcinoma
Biliary atresia
Polycystic liver disease
Persistent hepatic dysfunction (indication for retransplantation)
Hereditary hemorrhagic telangiectasia
Hepatic hemangioendothelioma
Biliary sepsis
Primary sclerosing cholangitis
Neuroendocrine tumor
Failed transplant (biliary/vascular complication)
MELD, model for end-stage liver disease.

Exception Points

There are several diseases associated with a higher mortality rate on the WL, the severity of which is not well reflected by the MELD score. For this reason, an exception MELD points system was invented, provided these patients fulfill a few criteria.16,139-141 Although most countries agree on the indications, there are country-specific variations (Table 2). Furthermore, for some patients who are not eligible for standard exception points but have a perceived risk of poor outcome (ie, refractory ascites109,110), referral to a national panel of experts to request nonstandard exception points is an option.16,141,142 All the above illustrate some of the limitations of MELD and MELDNa to accurately estimate prognosis across the wide variety of situations that can be encountered within liver disease.

An example of these cases is HCC. LT is the therapy with the highest chances of curing HCC. Posttransplant outcomes and WL mortality depend on different factors, such as liver function, tumor burden, stage and biology, and alternative effective treatments. Because conventional MELD scores do not accurately predict WL drop-out rate (up to 30%), exception points for listed patients (within Milan criteria) are allowed to reflect true WL mortality rates. Patients with HCC represent around 10% to 30% or the WL population in Western countries but have historically received 70% of exception scores. Because patients with HCC tend to have better liver function and lower MELD scores compared with patients without malignancy, these patients are disadvantaged if an urgency principle for allocation is applied. In contrast, a utility principle will advantage HCC patients who can benefit from nontransplant locoregional treatments. To try and compromise both above, and after a number of policy changes, a cap-and-delay strategy is in place in the United States since 2015, updated in 2019. No exception points will be assigned for the first 6 mo to allow accounting for tumor biology. Thereafter, an initial MELD score equivalent to the regional median MELD at transplant minus 3 points is assigned, with no further escalation in exception points. MELD exception is capped at 34 points.142-147 In Canada, HCC patients will get a baseline of 22 points and a 3-point increase every 90 d up to a maximum of 30 points, provided they fulfill certain criteria (tumor volume ≤145 cm3 and alpha fetoprotein ≤1000 mg/dL).140 In most of the Eurotransplant countries, patients listed within Milan criteria can be registered at a MELD score equivalent to a 15% probability of patient death within 3 mo and upgraded every 90 d to a MELD score that reflects an increase in mortality by 10%.141


Acute on chronic liver failure (ACLF) is a syndrome that affects patients with chronic liver disease and is characterized by intense systemic inflammation, organ failure, and a poor short-term prognosis.148-153 The higher the number of extrahepatic organ failures, the lower the overall survival.149,151,154 It is an extremely dynamic condition that can worsen or improve rapidly within days. If no early response to treatment is achieved, the only definitive therapy is LT. Indeed, 1-y post-LT survival is >80%, compared with a dismal of 8% without transplantation in severe ACLF (≥3 organ failures).155-157 Therefore, early predictors of transplant-free survival are needed. Although MELD score is predictive of 30-d mortality,149,150 the fact that it does not account for extrahepatic organ failures disadvantages patients with ACLF-3 on the WL.158,159 Several scores have been developed to overcome this limitation. In 2014, the Chronic Liver Failure Consortium ACLF score was published and proved to better predict 28-d mortality compared with MELD and MELDNa (c-statistic, 0.760 versus 0.687 and 0.684, respectively; P < 0.001 for both comparisons).154 In 2017, the Asia-Pacific region developed the Asian Pacific Association for the Study of the Liver ACLF Research Consortium (AARC). AARC was observed to be superior to MELD in predicting mortality (c-statistic 0.804 versus 0.763; P < 0.001).151,160 Finally, in 2018, the North American Consortium for the Study of End-Stage Liver Disease’s definition of ACLF score was published in its final form. Compared with AARC, it had a numerically higher c-statistic value (0.8240 versus 0.7783) but was not significantly different in its ability to predict patient survival.149,150 However, none of these scores have been validated in the context of transplantation or implemented. Further research to establish the best way to prioritize ACLF patients on the WL is needed.


As discussed, there are multiple factors that influence WL mortality (Figure 2), and not all of them can be adequately solved by the chosen allocation system alone. Thus, distribution policies are also important to maximize the survival benefit derived from transplantation by fairly offering organs to patients and not centers.41

The French gravity model was implemented in 2011. It relies on a multiplicative interaction between medical criteria (MELD) and travel time to the procurement center. It is the sum of the MELD score (higher MELD, higher score) and the “distance score” (longer distance, lower score). Basically, this system creates dynamic areas for each patient according to the severity of their condition: for sicker patients, longer distances are allowed, reaching a balance to reduce cold ischemia time and avoid regional disparities. After implementation of the gravity model, MELD scores at transplantation were found to be significantly higher, and the percentage of patients transplanted with MELD scores of <15 decreased by 9.7%.161,162

In 2018, the United Kingdom moved from an urgency-based allocation system to a benefit-based one: the National Liver Offering Scheme. It is based on the Transplant Benefit Score (TBS) that includes recipient variables (age, sex, waiting time, location, liver disease, previous transplant, previous abdominal surgery, bilirubin, INR, creatinine, renal replacement therapy, sodium, potassium, albumin, encephalopathy, ascites, diabetes) and donor variables (age, cause of death, body mass index, diabetes, brain, or circulatory death, splittable, blood group). A patient’s TBS is the difference between the patient’s expected utility from the transplant and the patient’s predicted need. The patient with the greatest difference between these 2 quantities will score the highest TBS. There is now a national WL so that livers are offered on a patient basis. It has only been implemented for donation after brain death and is constantly monitored.6,61,163-165

To minimize geographic disparities in access to LT, in 2020, the United States implemented the acuity circle distribution model. It is based on concentric geographic circles around the donor site hospital. After being initially offered to any super urgent patients within 500 nautical miles of the donor hospital, the organ is then offered to patients with a MELD score of ≥37 within 150 miles of the donor hospital; then to similarly ill patients within 250 miles and finally within 500 miles of the donor hospital. If the organ is not accepted, then it is allocated to patients with decreasing MELD scores in expanding geographic circles at each MELD score tier (thresholds 33, 29, and 15) as above before being allocated nationally, until finally being offered to patients with a MELD score of <15.145,166 The geographic hard boundaries preclude more urgent patients closer to the donor center to receive an offer if outside a given circle until no more potential recipients are available in said circle. Therefore, the transplant community is moving toward a “continuous distribution” policy that would allow considering all individual patient characteristics simultaneously. Following the lead of the lung transplant community, a Continuous Allocation Score is in active development for LT (Figure 4). This system combines several attributes (acuity and WL survival, posttransplant outcomes, candidate biology, patient access, and placement efficiency) to provide a final score for each patient. Different attributes will be weighted by importance. For instance, in lung transplant, WL survival and posttransplant outcome account for 50% of the total weight.167-169

Continuous Allocation Score principles. The weights of each of the attributes will be assigned on the basis of their relative importance for each type of transplanted organ. The 2 pillars are likely waitlist survival and posttransplant outcome. Patient access will prioritize pediatric population and previous living donors. Candidate biology accounts for the likelihood of finding a compatible match based on the candidate’s characteristics. Placement efficiency factors the number of resources required to identify a suitable candidate willing to accept the organ and deliver the organ for transplant.167 , 169


Role of Machine Learning in D-R Matching

Assignment of a suitable donor to the most appropriate recipient to ensure the best possible outcomes post-LT constitutes the base of D-R matching. Machine learning (ML) uses artificial intelligence to generate predictive models through detection of hidden patterns within large data sets. In LT, various donor variables are combined with recipient factors and some logistic events to obtain 1 of 2 outcomes: survival of graft and recipient or loss of 1 or both. Different results will be obtained on the same data set depending on the chosen ML classifier. The most used ones in LT are artificial neural networks and random forests.170 Their goal is to discriminate D-R pairs with better outcome expectations within a set of them.171,172 Artificial neural networks are excellent tools for finding patterns that are far too complex for clinicians and nearly reach a 95% prediction capability for 3-mo graft survival.173,174 Random forests may allow for improved confidence with the use of marginal organs and better outcome after LT.175 A promising tool, the optimized prediction of mortality, has been recently developed using an ML approach based on Optimal Classification Trees to predict 3-mo WL mortality. In simulations run on the Standard Transplant Analysis Research data set, optimized prediction of mortality reduced mortality on average by 418 deaths every year (18%), increasing the number of female and non-HCC patients receiving a transplant. It also better prioritized patients based on disease severity compared with MELD-based systems.176 However, the predictability of a model depends on the database homogeneity and can only be applied to the database in which it was created.177,178

Expanding the Donor Pool

Other opportunities to increase the number of available deceased donor organs include the use of extended criteria donor livers (advanced age, steatosis, donation after circulatory death, and HCV positive among others).179,180 These organs entail a high risk of ischemia-reperfusion injury and potentially graft failure.181 Ex situ machine perfusion technology seems a promising technology to improve marginal graft’s quality.182

Preferentially allocating offers from pediatric liver donors to small size adults after all viable pediatric candidates have been exhausted reduced WL mortality in that subset of patients.183 Women who received a pediatric donor liver offer as their first offer were more likely to accept this offer (63%) compared with men in the same position (55%). In women, overall acceptance rates for pediatric versus adult donor livers were higher (63% versus 48%), whereas for men, it was the contrary (63% versus 80%).183 A simulation was performed to estimate the impact of implementing a height-based rule for the allocation of pediatric donor livers preferentially to adult recipients with height <166 cm after exhausting pediatric candidates. The model resulted in no change in overall WL deaths for adults and children but eliminated 42% of the sex gap in the percentages of adults who received transplants.184

Split LT (SPLIT), which divides a deceased donor liver into 2 partial liver grafts, is a promising strategy for increasing graft availability for transplantation and ameliorating organ disposition. The liver can be split into a smaller graft for a pediatric recipient and a larger right graft for an adult or into 2 full hemi-liver grafts for 2 adult recipients. The former is the most accepted technique because the latter has variable results and is more challenging in terms of surgical technique and potential postoperative complications. Proper donor and recipient selection is of the utmost importance for the success of SPLIT and affects long-term graft and patient survival. There is an urgent need to establish a formal SPLIT program under current organ transplantation organizations.185

Living Donor Liver Transplantation

Living donation allows patients to attract an organ without waiting for a deceased donor and thereby faster access to LT. Patients with autoimmune liver disease (who are less likely to receive exception points), smaller sized patients, and women seem to be ideal candidates for this practice. However, socioeconomic characteristics of patients with a potential live donor (younger, married, nonimmigrant, English as a first language, higher income) may limit the generalization of this type of transplant in Western countries.105,186 Taking unpaid time off work is a significant barrier preventing people of lower socioeconomic status from living donor LT (LDLT) in certain countries, such as the United States. There is evidence of decreased access to LDLT for African American and Hispanic patients. For instance, the proportion of WL patients who had at least 1 donor was lower for Black compared with White patients in the United States.187 In Asian countries, there is a remarkable shortage in deceased donations, which has led to a 10-fold increase in living donation over the past decades. LDLT represents >75% of transplant activity in this region. Despite significant technical improvements, donor mortality remains around 1 in 8000 hepatectomies, constituting the main limitation for LDLT, because donor safety is of paramount importance and cannot be compromised.163,188,189 A strong culture supporting live donation should be implemented, building full-time living donation teams, supporting living donors financially, and raising awareness among the general public.105


MELD and MELDNa scores are easy to use and are based on readily available laboratory values. Their implementation as allocation policies in LT around the world has significantly reduced WL mortality while maintaining similar posttransplant outcomes. However, several challenges remain unsolved and some groups, including women, patient with sarcopenia, and frailty, are disadvantaged by MELD and MELDNa. Similarly, both MELD and MELDNa disadvantage patients without portal hypertension and cholestatic liver diseases. Future work should focus on improving D-R matching (for which artificial intelligence seems a promising tool), expanding the pool of donor organs (LDLT, SPLIT, size reduction, marginal grafts), and addressing inequities in access to WL and LT.


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