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Defining a Liver Transplant Benefit Threshold for the Model for End-Stage Liver Disease-Sodium Score

Objectives: The benefits of transplant are shown as the difference in survival posttransplant versus that shown if the patient had remained on the wait list. Serum sodium was added to improve prediction. We sought to revisit the question of which Model for End-Stage Liver Disease-Sodium score threshold cor-responded to a predicted benefit of liver transplant.

Materials and Methods: Data on adult patients (≥ 18 years old) were obtained from the United Network for Organ Sharing registry (date range of June 18, 2013 to December 2016). Exclusion criteria were individuals listed for multiple organs or liver retransplant, patients who eventually underwent living-donor liver trans-plant, and patients with MELD score < 12. We used multivariable Cox proportional hazards regression to determine a time-dependent covariate for undergoing transplant with either MELD or MELD-sodium scores to describe the variability in estimated transplant benefit within 6 months of listing.

Results: Our study included 14 352 patients. There were 902 patients with MELD score of 39 to 40 (6.3%) and 931 patients with MELD-Na score of 39 to 40 (6.5%). Using the original MELD score, we found that 90% of the cohort could derive benefit from transplant compared with 83% when MELD-Na was used. We found that 13% of patients had a predicted transplant benefit when determined using either MELD or MELD-Na but not both. The threshold for transplant benefit was 16 and 17 using MELD and MELD-Na, respectively.

Conclusions: Transition to MELD-Na did not define a more precise range at which patients benefited from transplant, and a similar percentage of patients was expected to derive benefit. Future revisions of donor liver allocation may allow better discrimination of expected transplant benefits among candidates currently assigned a high priority for donor livers.


Key words : Donor, MELD, MELD-Na, Mortality, Supply, Survival benefit, Transplant benefit, Wait list

Introduction

Liver transplant is an important, curative therapy for end-stage liver disease. The optimal timing for liver transplant during a patient’s disease course has not been well defined, and it is unclear when in the course of cirrhotic disease to offer transplant.1 In the present environment, during which the demand for donor livers far exceeds the supply, timing of transplant is especially important.2 Secondary to the severe shortage of donor organs, it is necessary to determine which patients will derive a significant benefit from transplant.1 The Model for End-Stage Liver Disease (MELD) was originally designed to predict mortality after transjugular intrahepatic portosystemic shunt.3 The MELD score is based on 3 laboratory parameters: total bilirubin, international normalized ratio, and creatinine. A patient’s MELD score has also been shown to predict the risk of mortality among cirrhotic patients more generally and has been used in the process of liver allocation since 2002.4-6

The properties of an ideal allocation score for liver transplant remain an open question, and there is no current consensus in the transplant community. There is a significant shortage of donor liver allografts, with 14 413 patients on the wait list for liver transplant and only 7841 liver transplants performed in 2016.7,8 With priority on transplant wait lists determined by the MELD score, a desirable use for the MELD score would be to define a cutoff above which liver transplant would confer a survival benefit. Furthermore, when donor livers are allocated based on MELD, there should be correspondence between higher MELD scores and greater benefit. To fulfill the principle of organ allocation according to medical need, the ideal allocation score would be able to define a relatively narrow group of patients, that is, one similar in size to the number of available donors, who would benefit most from receiving a liver transplant.

Prior studies have examined variations in trans-plant benefits, defined as the difference in survival posttransplant versus that shown if the patient had remained on the wait list.1,9-14 In a 2005 study, patients with MELD scores between 18 and 20 had a 38% reduction in mortality risk after transplant, whereas patients with MELD scores between 15 and 17 had 21% greater risk of mortality after transplant. Such comparisons highlighted the evident lack of transplant benefit for patients with low MELD.1 However, after a threshold of transplant benefit was described using the original MELD score, serum sodium was added to the MELD score to improve prediction of survival.15,16

The United Network for Organ Sharing (UNOS) implemented the resulting MELD-Na score in January 2016, using the following formula: MELD-Na = MELD + 1.32 × (137 – Na) – [0.033 × MELD × (137 – Na)].17 Therefore, we sought to revisit the question of which MELD and MELD-Na threshold corresponded to a predicted benefit of liver transplant and whether the threshold of MELD-Na defined a more selective group of patients to facilitate better matching of available donor organs to patients expected to benefit the most from transplant. Our secondary aim was to determine agreement between predicted transplant benefits when using MELD-Na compared with the original MELD score.

Materials and Methods

This study was deemed exempt from review by the Institutional Review Board of The Ohio State University. Data on adult patients (≥ 18 years old) were obtained from the UNOS registry (date range from June 18, 2013 to December 2016).18 Individuals listed for multiple organs, those who had previously undergone a liver transplant, those who underwent living-donor liver transplant, and those with MELD score < 12 were excluded. The beginning of the study corresponded to the introduction of “Share 35” and was after the date of incorporation of sodium at listing in the UNOS registry.19,20 Candidates with hepatocellular carcinoma and others assigned exception points at or after listing and prior to organ allocation were censored at the date at which exception points were requested and were therefore not included in the analysis. In this manner, our analyses only included candidates for whom the medical team agreed that the original MELD score adequately represented their clinical status.

Patients were in the pretransplant group while they were on the wait list and were in the transplant group after they received a deceased-donor liver transplant. Patients who were removed from the candidate pool due to recovery were censored at the time of their removal from the wait list. The MELD and MELD-Na scores of patients were calculated according to the last set of values reported while a patient was on the wait list. Transplant benefit was estimated as the change in mortality hazard (hazard ratio [HR]) associated with transitioning from the pretransplant state to the posttransplant state or the change in adjusted mortality hazard (HR) associated with transitioning from the pretransplant state to the posttransplant state.

Statistical analyses
Using multivariable Cox proportional hazards re-gression models, we determined the time-dependent covariate for undergoing transplant with either MELD or MELD-Na scores to describe variability in estimated transplant benefit. Model for End-Stage Liver Disease scores were initially centered at 20 to demonstrate the estimated transplant benefit for a candidate with a MELD or MELD-Na of 20, respectively. Cox models were adjusted to the covariates assessed at the time of listing, including candidate age, sex, race/ethnicity, body mass index (BMI), diagnosis, diabetes status, education, insurance coverage, and year of listing.1 In addition, adjusted HRs for mortality hazard comparing the posttransplant and pretransplant conditions were estimated for the entire study population, based on both MELD and MELD-Na. Agreement between these estimates (ie, agreement between predicted benefit of transplant according to MELD and according to MELD-Na) was characterized using the Spearman correlation coefficient.

Results

From June 18, 2013 to December 2016, 36 891 patients were placed on the liver transplant wait list. Patients who were less than 18 years old (n = 2231), those who had undergone previous transplant (n = 1615), and those who had undergone multiorgan (n = 3569), split liver (n = 839), or living-donor liver transplant (n = 15) were not included in our analyses. Patients were also excluded if they had petitioned for exception points (n = 6466) or if their MELD score was less than 12 (n = 7804).

Overall, 146352 patients met the inclusion criteria (Table 1). Median age and BMI were 56 years old (interquartile range [IQR], 48-62 y) and 28 kg/m2 (IQR, 25-33 kg/m2). Most patients were male (n = 9094, 63%) and white (n = 10 259, 72%). The most common causes of liver disease were alcoholic liver disease (n = 4766, 33%), viral causes (n = 2933, 20%), nonalcoholic steatohepatitis (NASH; n = 2302, 16%), and auto-immune diseases (n = 1506, 10%). Most patients had private insurance (n = 7894, 55%), Medicare (n = 3099, 22%), or Medicaid (n = 2664, 19%). Most patients had at least a high school degree (n = 6350, 48%), with others having some college (n = 3414, 26%) or a full college degree (n = 3406, 26%).

Figure 1A demonstrates the frequency of each MELD score, and Figure 1B demonstrates the frequency of each MELD-Na score. Most patients had high MELD or MELD-Na scores, with a median MELD of 19 (IQR, 15-29) and median MELD-Na of 22 (IQR, 17-30). There were 902 patients with MELD score of 39 to 40 (6.3%) and 931 patients with MELD-Na score of 39 to 40 (6.5%).

Two Cox regression models were fitted, with the model for MELD score shown in Table 2, and the model for MELD-Na shown in Table 3. The relevant covariates included in the models were age, sex, race/ethnicity (white, black, other), BMI, cause of liver disease (viral, cryptogenic, autoimmune, NASH, alcoholic, hepato-cellular carcinoma, other), diabetes, insurance status at transplant (private, Medicaid, Medicare, other), and educational attainment (high school, some college, college degree). We also interacted the time-dependent covariate for undergoing transplant with either the MELD or MELD-Na scores to describe variability in estimated transplant benefit according to each allocation score.

Using the MELD score (Table 2), we determined the instantaneous change in mortality hazard (HR) associated with transitioning from pretransplant to posttransplant. The adjusted model showed HR of 0.39 (95% confidence interval [95% CI], 0.32-0.47) for patients having a MELD score of 20; that is, this score showed that patients who underwent transplant had a 61% lower likelihood of mortality. The HR for the interaction between transplant and MELD score was 0.87 (95% CI, 0.85-0.88), indicating that the transplant benefit increased (below HR = 1 and further from HR = 1) with increasing MELD score.

Variations in transplant benefit across quadratic and cubic functions of MELD were also modeled (data not shown). Modeling transplant benefit as a polynomial function of MELD did not improve model fit. In our Cox regression model examining transplant benefit based on MELD score and adjusted for age, sex, race/ethnicity, BMI, cause of liver disease, diabetes, insurance status at transplant, and educational attainment, factors associated with survival included age (HR = 1.02; 95% CI, 1.02-1.03; P < .001), male sex (HR = 0.82; 95% CI, 0.73-0.92; P < .001), hepatocellular carcinoma (HR = 1.48; 95% CI, 1.11-1.97; P = .007), and having a college degree (HR = 0.87; 95% CI, 0.76-0.99; P = .040). The MELD cutoff at which transplant benefit reached statistical significance was 16.

Using the MELD-Na score (Table 3), we deter-mined that the instantaneous change in mortality hazard associated with transitioning from pretrans-plant to posttransplant in the adjusted model was 0.49 (95% CI, 0.40-0.60) at a MELD-Na score of 20. This indicated that patients with a MELD-Na of 20 who underwent transplant had a 51% lower likelihood of mortality. The HR for the interaction of transplant and MELD-Na score was 0.87 (95% CI, 0.85-0.88), indicating that the transplant benefit increased with increasing MELD-Na score. Figure 2 demonstrates the HRs associated with transplant across values of MELD and MELD-Na. The Cox regression model examined transplant benefit based on MELD-Na score adjusted for age, sex, race, BMI, etiology, diabetes, insurance status at transplant, and educational attainment. Factors associated with survival were age (HR = 1.03; 95% CI, 1.02-1.03; P < .001), male sex (HR = 0.82; 95% CI, 0.73-0.92; P < .001), hepatocellular carcinoma (HR = 1.51; 95% CI, 1.14-2.02; P = .004), having Medicaid (HR = 1.16; 95% CI, 1.01-1.34; P = .033), and having a college degree (HR = 0.87; 95% CI, 0.76-0.99; P = .037). The MELD-Na cutoff associated with a statistically significant transplant benefit was 17.

Defining transplant benefit as any HR < 1, 90% of the cohort was expected to benefit from transplant when using the MELD score and 83% was expected to benefit when using MELD-Na (Table 4). There were 338 patients (3%) with no expected benefit of trans-plant according to the MELD score but an expected benefit of transplant according to the MELD-Na score. There were 1233 patients (10%) who were expected to benefit when using the MELD score who were not categorized as experiencing benefit when using the MELD-Na score. There were 777 patients (7%) who were not identified as experiencing benefit using either model. Most importantly, there were 9551 patients (80%) who were identified as expected to benefit from transplant using either the MELD or MELD-Na score. There was a significant correlation between HRs for expected benefit based on MELD and MELD-Na with a Spearman coefficient of 0.91 (P < .001), indicating high agreement between the 2 measures.

Discussion

The Model for End-Stage Liver Disease was originally evaluated for predicting mortality after transjugular intrahepatic portosystemic shunt and is based on 3 laboratory parameters: total bilirubin, international normalized ratio, and creatinine.3 The MELD score has also been demonstrated to predict the risk of mortality among cirrhotic patients more generally and has been used in the process of liver allocation since 2002.4-6 The incorporation of serum sodium into the MELD score (MELD-Na) has been demonstrated to improve prediction of wait list mortality; therefore, MELD-Na has been used in liver allocation since January 2016.16,21 Our present study was important because it demonstrated that the transition to MELD-Na did not define a more precise range at which patients benefited from transplant. In fact, a similar percentage of patients were expected to derive benefit when using the MELD-NA versus when MELD was used. Furthermore, the specific covariates that influenced outcomes when controlling for MELD-Na as opposed to MELD were similar, suggesting that implementing MELD-Na did not account for other confounding influences on survival after wait listing for liver transplant.

The results of our study contribute to a growing literature seeking to better define which patients benefit the most from liver transplant. In 2005, Merion and associates reported that individuals with MELD scores between 18 and 20 had a 38% less risk of mortality if they underwent transplant versus patients who remained on the wait list.1 In contrast, patients with MELD scores between 15 and 17 had a 21% higher risk of mortality if they underwent transplant versus remaining on the wait list.1 Overall, liver transplant recipients had a 79% lower mortality risk than patients who remained on the wait list. As such, the investigators concluded that, among patients with a low risk of pretransplant death, a significant survival benefit was not achieved with transplant.1 Subsequently, these authors expanded on their work to include an estimate of survival benefit according to cross-classification of candidate MELD score and deceased-donor risk index (DRI). In this study, adjusted HRs were calculated for each liver transplant recipient using a specific MELD with an organ of a given DRI. Posttransplant mortality rates of transplant recipients were compared with patients who continued on the wait list with possible future transplant with an organ of lower DRI. The authors concluded that high-DRI organs provided survival benefit for patients with high, but not low, MELD scores. Patients with a MELD ≥ 20 experienced a benefit regardless of DRI.10,22 Given our objective of determining a MELD-Na threshold for transplant and comparing MELD and MELD-Na, we did not include cross-classification with DRI in our study. Although a slightly different MELD cutoff was identified in our study, our results were comparable to those reported by Merion and associates1 in that patients with MELD or MELD-Na scores < 15 were not expected to benefit from liver transplant.

There are a variety of potential methods to determine which patients should be prioritized for liver transplant. Urgency is defined by a patient’s wait list mortality, but it fails to take into consideration a patient’s posttransplant life expectancy. Utility is defined by a patient’s expected posttransplant mortality, but it fails to consider a patient’s wait list mortality. Transplant benefit combines the concepts of both urgency and utility and is defined as the difference between survival posttransplant and survival if the patient had remained on the wait list. The disadvantage of this approach is that there is no consensus for calculating the components of transplant benefit or expressing the contrast between these components as an arithmetic difference or a ratio. The literature in this area demonstrates multiple methods for calculating transplant benefit.10,23-25 Vitale and associates calculated transplant benefit by subtracting the area under the survival curve after alternative therapies (or no therapy) from the survival curve after liver transplant to estimate transplant benefit for patients with hepatocellular carcinoma.23 This approach was also used by Rana and colleagues to examine survival benefits of solid-organ trans-plant in the United States.24 In contrast, Schaubel and associates utilized the direct output of a Cox regression model to report a covariate-adjusted ratio of post- to pretransplant mortality rates.10 This comparison of MELD versus MELD-Na could be repeated with alternative ways of quantifying transplant benefit, but we used this particular method because it described the moderation of transplant benefit in a straightforward way with a single interaction coefficient in the regression model. Posttransplant survival is included in the follow-up period; therefore, a lower mortality HR after transplant factors into the estimation of the HR associated with undergoing transplant (relative to remaining on the wait list). Although the statistical approaches vary, all methods used to date, including the mathematical approach employed in our study, have suggested a MELD threshold of transplant benefit beginning in the high teens. In our study, this threshold persisted even after simplifying as-sumptions about the benefits associated with transplant, such as the assumption of instantaneous change in the mortality hazard.

The lung allocation score used for allocation for lung transplant is an example of survival benefit-based transplant prioritization. The lung allocation score estimates 1-year posttransplant life expectancy using 8 patient characteristics, with 1-year wait-list life expectancy estimated using 13 patient charac-teristics.26 In turn, a calculated normalized difference between 1-year posttransplant life expectancy and 1-year wait-list life expectancy is used to produce a score ranging from 0 to 100.26 In our study, MELD-Na was not associated with a more specific range at which patients were expected to benefit from transplant. Furthermore, a similar percentage of patients and a similar group of patients were expected to benefit under both MELD and MELD-Na. To this point, the same percentage of patients had the maximum MELD (6.3%) or MELD-Na (6.5%) scores of 39 to 40. These data make it difficult to discriminate among patients who may derive the largest benefit from liver transplant. As such, discrimination of candidates expected to receive the most benefit might be improved if the ceiling of the MELD score were raised.

There were strengths and limitations to our present study. The use of the UNOS database allowed for a large sample size that facilitated robust statistical analyses. However, similar to any study that utilized administrative data, our results may have been susceptible to variations in reporting practices and may have incompletely reported covariates. Although the data entered in the UNOS database are standardized and have been previously demonstrated to be robust, information entered at listing, transplant, and follow-up, as well as consistency across sites, cannot be guaranteed. Furthermore, there may have been selection bias in how patients were chosen to undergo transplant that cannot be fully accounted for in any retrospective study. Additionally, we used calculated MELD/MELD-Na values at listing. Liver transplant candidates are unlikely to remain at the same MELD/MELD-Na score throughout their time on the wait list. However, recording of changes of the MELD/MELD-Na score after listing selects for longer times on the wait list. Furthermore, transplant centers do not have an incentive to report decreases in candidates’ MELD/MELD-Na scores.

Conclusions

Use of the MELD or MELD-Na resulted in a similar percentage of patients expected to derive benefit from transplant. The data strongly suggest that the transition to MELD-Na was not better at defining a more precise range at which patients benefited from transplant. Improvements to liver allocation are still needed that incorporate anticipated transplant benefit. Future revisions of donor liver allocation will need to incorporate better discrimination of expected transplant benefit among candidates currently assigned a high priority for donor livers.


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DOI : 10.6002/ect.2018.0346


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From the 1Department of General Surgery, Division of Transplantation, The Ohio State University Wexner Medical Center, Columbus, Ohio; the 2Department of Anesthesiology and Pain Medicine, Nationwide Children’s Hospital, Columbus, Ohio; the 3Department of Pediatrics, The Ohio State University College of Medicine, Columbus, Ohio; the 4Department of General Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, Columbus, Ohio, USA; and the 5Henri-Mondor Hospital, APHP, University Paris Est Créteil, Paris, France
Acknowledgements: The authors have no sources of funding for this study and have no conflicts of interest to declare.
Corresponding author: Timothy M. Pawlik, Department of Surgery, the Ohio State University, Wexner Medical Center, 395 W. 12th Ave., Suite 670, Columbus, OH 43210, USA
Phone: +1 614 293 8701
E-mail: Tim.Pawlik@osumc.edu