Performances and Validation of MELD 3.0 and ReMELD-Na Scoring Systems: A German Clinical Cohort Study

Performances and Validation of MELD 3.0 and ReMELD-Na Scoring Systems: A German Clinical Cohort Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Performances and Validation of MELD 3.0 and ReMELD-Na Scoring Systems: A German Clinical Cohort Study Aghnia Jolanda Putri, Decan Jiang, Zoltan Czigany, Arianeb Mehrabi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7219777/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Sep, 2025 Read the published version in Langenbeck's Archives of Surgery → Version 1 posted 7 You are reading this latest preprint version Abstract Background The Model for End-Stage Liver Disease (MELD) score, introduced in 2002, has since been refined. MELD 3.0, launched in 2023 in the United States, improves mortality prediction by incorporating sex and albumin. Since March 2025, Germany started to use reMELD-Na to improve prioritization of patients on the waiting list for liver transplantation (LT). This study compares the performance of original MELD, MELD-Na, MELD 3.0 with and without albumin, and reMELD-Na for patients waitlisted for LT in a large German transplant center. Methods This retrospective single-center study included 206 listed patients from 2017 to 2021 for LT. Reclassification patterns along with predictive accuracy for three-month survival and overall survival (OS) of five different MELD scores were assessed using Harrell’s c-index and integrated area under the curve (iAUC). Results Over a median follow-up of 33.9 months, 100 patients (51.5%) underwent LT, with a post-transplant survival rate of 70%. Thirty-eight patients (18.4%) received LT and sixteen patients died within the first three months after listing. MELD 3.0 without albumin and reMELD-Na demonstrated the highest discrimination for three-month survival (iAUC 0.848 and 0.847; c-index 0.827 and 0.848, respectively). MELD 3.0 without albumin showed the best overall performance in predicting OS (iAUC and c-index 0.827), particularly in males, while reMELD-Na performed best in females (iAUC 0.707; c-index 0.705). Original MELD showed the poorest discrimination. Females with higher original MELD scores tended to receive even higher scores with MELD 3.0. Conclusion This is the first German study to validate reMELD-Na and MELD 3.0, showing superior predictive performance over original MELD. MELD 3.0 may better reflect disease severity in women at advanced stages due to more upward reclassification. Liver Transplantation MELD Scoring systems mortality equality Figures Figure 1 Figure 2 Introduction The Model for End-Stage Liver Disease (MELD) score has revolutionized the liver transplantation (LT) allocation systems. Originally developed to predict mortality following transjugular intrahepatic portosystemic shunt (TIPSS) placement, it was later found to be a valuable tool for estimating mortality risk among patients listed for LT. The MELD score was officially introduced for LT purpose in 2002 and has undergone several updates since then.( 1 , 2 ) The second iteration of the MELD system in 2007, MELD-Na, improved prognostic accuracy by incorporating serum sodium levels.( 3 ) In the United States, the most recent version of the MELD score, known as MELD 3.0, was implemented by United Network for Organ Sharing (UNOS) in 2023..( 3 ) By including new variables such as sex and serum albumin, MELD 3.0 provides a more nuanced assessment of mortality risk, that yielded to improvements in mortality prediction and transplant outcomes among LT recipients. This adjustment is particularly important given the systemic disadvantages women face at multiple stages of the transplant process from referral and evaluation to listing and ultimately receiving a LT.( 3 , 4 ) Notably, steatotic liver disease (SLD) has emerged as the leading indication for LT among women without hepatocellular carcinoma, a trend expected to continue in the coming years. Despite its widespread adoption, regional differences in patient demographics, healthcare access, and outcomes underscore the need for local validation. In Germany, for instance, the original MELD formula is still in use, which does not account for patient sex or serum albumin levels. Validating MELD 3.0 in the German context is essential, as it could significantly influence patient selection, waiting list mortality, and post-transplant survival, particularly among vulnerable groups such as women and patients with impaired renal function. ( 7 ) After relying on the original MELD score since its adoption in 2002, Germany has recently transitioned to using a refitted version of MELD-Na (reMELD-Na) as of March 2025,( 4 ) following its implementation by Eurotransplant (ET). It was developed by reweighing the coefficients and re-establishing clinically meaningful lower and upper bounds for creatinine, bilirubin, INR, and sodium. Unlike the original MELD score, which capped a significant proportion of patient values and potentially overemphasized renal dysfunction, reMELD-Na limits such distortions by refining parameter bounds and reducing the disproportionate influence of creatinine.( 5 ) Importantly, its calculation on sodium enhances its ability to identify high-risk patients with hyponatremia. The present study seeks to address a gap in the existing literature by examining the performance of both MELD 3.0 and reMELD-Na at one of Germany’s largest LT centers, and situating these findings within the broader international developments in MELD-based scoring systems. Methods Study Population This study is a retrospective clinical cohort study designed to assess the validity of the MELD 3.0( 3 ) and reMELD-Na( 5 ) scoring systems in predicting mortality and transplant outcomes among patients listed for LT at our center. Informed consent was obtained. The analysis is based on a dataset from the Department of General, Visceral and Transplant Surgery at the University Hospital of Heidelberg, Germany. The study included adults (≥ 18 years) listed for liver transplantation at our center between January 1, 2017, and December 31, 2021, with a minimum follow-up of three years. Patients with high-urgency status, re-transplantations, listings under standard exception criteria, or incomplete data were excluded. Statistical Analysis Patient characteristics were reported as mean ± standard deviation for continuous variables and as frequencies (percentages) for categorical variables. Subgroup and sensitivity analyses were conducted based on sex (male vs. female). A two-sided p-value < 0.05 was considered statistically significant. The predictive accuracy of MELD models was assessed using Harrell’s c-index and Heagerty’s integrated time-dependent AUC (iAUC). Differences in c-index estimates with 95% confidence intervals were analyzed. All analyses were conducted in R Studio (Posit PBC version 4.4.3) and in consultation with the Institute for Medical Biometry and Informatics, University of Heidelberg. The study was conducted in accordance with the latest version of the Declaration of Helsinki and the Declaration of Istanbul. Ethical approval was obtained from the local ethics committee (S-105/2025). Results Demographic and Clinical Characteristics During the study period from 2017 to 2021, a total of 392 patients were listed for LT. The mean age of the whole cohort was 53.19 ± 10.91 years, with 62.5% of the patients being male. Patients listed for high urgency transplantation (n = 42 patients), re-transplantation (n = 51 patients), or with malignancies qualifying for standard exception criteria (e.g., HCC, neuroendocrine tumors) (n = 87 patients) were excluded. In addition, six patients were excluded due to incomplete data resulting from transfer to another center. Upon exclusion, the final study population consisted of 206 patients, with a mean age of 53.08 ± 10.35 years and a male proportion of 59.2% (Table 1). Within three months of listing, 22 patients were removed from the waiting list due to worsening clinical condition, and nine patients due to improvement. Table 1 . Patient Characteristics Variables Sex All p M (n=122) F (n=84) Age (Mean, SD) Age (Median, IQR) 52.02 ± 10.14 54 (48-59) 54.62 ± 10.51 58 (52-61) 53.08 ± 10.35 55 (49-60) 0.08 Etiology (n, %) 0.25 Alcohol 51 (56.7) 39 (43.3) 90 (43.7) PSC 20 (66.7) 10 (33.3) 30 (14.6) MASLD 6 (42.8) 8 (57.2) 14 (6.8) Chronic HBV/HDV 7 (53.8) 6 (46.2) 13 (6.3) PBC 9 (75) 3 (25) 12 (5.8) Hepatitis C 8 (88.8) 1 (11.2) 9 (4.4) Autoimmune hepatitis 2 (33.3) 4 (66.7) 6 (2.9) SSC 3 (100) 0 (0) 3 (1.5) Cryptogenic 4 (40) 6 (60) 10 (4.9) Others 12 (63.1) 7 (36.9) 19 (9.2) MELD score at listing Original MELD 18.25 ± 8.02 17.32 ± 8.52 17.87 ± 8.22 0.42 MELD-Na 20.02 ± 8.49 18.82 ± 8.69 19.53 ± 8.57 0.32 MELD 3.0 without albumin 20.34 ± 8.58 19.10 ± 8.78 19.83 ± 8.67 0.31 MELD 3.0 with albumin 20.68 ± 8.80 19.54 ± 8.57 20.21 ± 8.87 0.36 ReMELD-Na 16.78 ± 7.15 15.80 ± 7.20 16.38 ± 7.17 0.41 Abbreviations: HBV, hepatitis B virus; HDV, hepatitis D virus; IQR, interquartile range; MELD, Model for End-Stage Liver Disease; Na, sodium; MASLD, metabolic dysfunction-associated steatotic liver disease; PSC, primary sclerosing cholangitis; SD, standard deviation; SSC, secondary sclerosing cholangitis. Alcohol-related cirrhosis was identified as the most common etiology of liver disease, accounting for 90 cases (43.7%), followed by primary sclerosing cholangitis (PSC), which was observed in 30 cases (14.6%). Metabolic dysfunction-associated steatotic liver disease (MASLD) was present in 14 patients (6.8%), of whom 57% were female. During the study period, 100 patients eventually underwent LT, leaving 106 patients untransplanted until the end of the observation period. MELD scores of the study cohort were calculated using various scoring systems. The original MELD score was 17.8 ± 8.22, MELD-Na was 19.53 ± 8.57, MELD 3.0 without albumin was 19.83 ± 8.67, MELD 3.0 with albumin was 20.21 ± 8.87, and ReMELD-Na was 16.38 ± 7.17. Across all MELD formulas, female patients exhibited lower scores. Age and etiology of liver disease showed no statistically significant association with either short-term or long-term survival in non-transplanted patients using the original MELD formula. In relation to sex, female patients have a 23% lower relative risk of survival during the observed period. However, this difference did not reach statistical significance (Table 2). For overall survival, the hazard ratio for female patients was 0.96, suggesting comparable long-term outcomes between sexes (p = 0.88) (Table 2). Table 2. Correlation of Patient Characteristics with Three-Month Survival and Overall Survival in Non-Transplanted Patients Within Three-Months of Listing, (Listed Using the Original MELD Score) Three-month survival Overall survival All n=168 Male n=99 Female n=69 P All n=168 Male n=99 Female n=69 P Age 0.18 0.12 Etiology 0.69 0.71 Sex 151 (89.8) 88 (88.8) ref. 63 (91.3) HR 0.77 0.62 98 (58.3) 58 (58.5) ref. 49 (71) HR 0.96 0.88 Reclassification of Waitlisted Patients using Different MELD Scores Reclassification across MELD scoring systems was performed to evaluate the pattern of score shifts resulting from the application of different calculation formulas. For this analysis, MELD Original, MELD-Na, and MELD 3.0 were included. ReMELD-Na was excluded due to its capped maximum value of 36, which hindered a direct one-to-one comparison with the other scoring systems. Reclassification of LT candidates was assessed by comparing categorical thresholds across the scoring systems: <10, 11-19, 20-29, 30-39, and ≥40. A contingency table was constructed to evaluate the extent of changes in patient categorization following the application of MELD 3.0, both with and without albumin. When MELD 3.0 was applied, notable reclassifications were observed, with some patients moving to higher and others to lower MELD categories. Upward reclassification occurred more frequently in male patients within the lower MELD categories, indicating a shift in perceived severity under the updated model. Conversely, among patients with higher original MELD scores, female patients were more likely to receive even higher MELD 3.0 scores, suggesting that MELD 3.0 may more accurately capture disease severity in women at advanced stages of liver disease in comparison to original MELD (Table 3). Table 3. Changes in Patient Classification on the Liver Transplant Waiting List Following MELD Score Adjustment (A. MELD Original vs. MELD 3.0; B. MELD-Na vs. MELD 3.0) A. MELD 3.0 without Albumin MELD Original <10 10-19 20-29 30-39 40 <10 19 (M:F, 11:8) 8 (M:F, 5:3) - - - 10-19 - 82 (M:F, 44:38) 20 (M:F, 11:9) - - 20-29 - 4 (M:F, 2:2) 46 (M:F, 32:14) 8 (M:F, 6:2) - 30-39 - - - 6 (M:F, 5:1) 6 (M:F, 2:4) 40 - - - - 7 (M:F, 4:3) MELD 3.0 with Albumin MELD Original <10 10-19 20-29 30-39 40 <10 19 (M:F, 11:8) 8 (M:F, 5:3) - - - 10-19 1 (M:F, 1:0) 77 (M:F, 42:35) 24 (M:F, 12:12) - - 20-29 - - 47 (M:F, 32:15) 11 (M:F, 8:3) - 30-39 - - - 3 (M:F, 3:0) 9 (M:F, 4:5) 40 - - - - 7 (M:F, 4:3) B. MELD 3.0 without Albumin MELD-Na <10 10-19 20-29 30-39 40 <10 18 (M:F, 11:7) 5 (M:F, 2:3) - - - 10-19 1 (M:F, 0:1) 83 (M:F, 46:37) 6 (M:F, 3:3) - - 20-29 - 6 (M:F, 3:3) 58 (M:F, 39:19) 5 (M:F, 4:1) - 30-39 - - 2 (M:F, 1:1) 9 (M:F, 7:2) 5 (M:F, 1:4) 40 - - - - 8 (M:F, 5:3) MELD 3.0 with Albumin MELD Na <10 10-19 20-29 30-39 40 <10 18 (M:F, 11:7) 5 (M:F, 2:3) - 0 - 10-19 2 (M:F, 1:1) 78 (M:F, 44:34) 10 (M:F, 4:6) 0 - 20-29 - 2 (M:F, 1:1) 59 (M:F, 39:20) 8 (M:F, 6:2) - 30-39 - - 2 (M:F, 1:1) 6 (M:F, 5:1) 8 (M:F, 3:5) 40 - - - - 8 (M:F, 5:3) Abbreviations: MELD: The Model for End-Stage Liver Disease; Na: Sodium. Among 19 patients initially classified with an original MELD score of <10, eight were reclassified into the 11-19 category under MELD 3.0 without albumin, while 19 remained in the same category. In the 11-19 group, the majority (82 patients) retained their classification, but 20 patients were reclassified into the 20-29 range, with a nearly equal distribution between male and female patients. In the original 20-29 category, 46 patients remained unchanged, whereas eight were reclassified into the higher-risk 30-39 category and four were downgraded to the 11-19 range. Among those originally scored in the 30-39 group, six remained in the same category, while another six were reclassified into the 40 group, two-thirds of whom were female. All patients originally categorized as MELD 40 remained in that category, indicating no downgrades among the highest-risk patients. When comparing MELD-Na to MELD 3.0 without albumin, most patients remained in their original risk category. However, reclassifications also occurred, but not as much in comparison to the original MELD. Among those initially scored <10 by MELD-Na, five patients were reclassified into the 10-19 group under MELD 3.0. Within the 10-19 group, 83 patients remained stable, but seven patients were reclassified; six into the 20-29 category and one downgraded to <10. Reclassifications into higher categories continued among MELD-Na 20-29 patients, where five patients shifted to either 30-39, where four of them were male. Among patients in the 30-39 category, seven were reclassified, which two moved downward to 20-29 and five moved upward into the ≥40 category, with four of the five being female. All eight patients originally categorized as MELD 40 retained their classification under MELD 3.0. A similar trend was observed when comparing MELD-Na to MELD 3.0 with albumin. Although fewer patients were reclassified compared to the version without albumin, upward shifts were still evident, particularly in female patients within higher MELD categories. For instance, of the eight patients moved into the MELD ≥40 category from 30-39, five were female. Overall, the reclassification trends show that MELD 3.0, particularly the version without albumin, more frequently increases MELD scores for women with advanced liver disease, whereas men were more commonly upshifted from lower MELD categories. Predictive Performance and Discriminative Ability of Different MELD Scores During the median observational period of 33.90 months (IQR 11.55-59.38), 100 patients (51.5 %) underwent LT, with a post-transplant survival rate of 70% at the end of the observation period. Of the 106 patients who did not undergo LT until the end of the study period, 54 patients (50.9%) remained alive by the end of the study. Thirty-eight patients (18.4%) received their transplant within three months of being listed according to original MELD score. Sixteen patients died within three months of being listed. The most common causes of death were liver failure (n=7) and sepsis (n=7), followed by bleeding, primarily due to fundic variceal hemorrhage. The predictive performance of MELD-based scoring systems was evaluated for both three-month and overall survival. Discriminative ability was assessed using Harrell’s concordance index (c-index) and the integrated area under the curve (iAUC) (Table 4). The analysis includes only patients who were still not transplanted within three months of listing, resulting in a cohort of 168 patients. Table 4 . Comparative Discrimination Ability of MELD Original and MELD 3.0 MELD Scores Three-month survival Overall survival iAUC Harrel c-index iAUC Harrel c-index All M W All M W All M W All M W MELD Original .824 .867 .740 .794 .861 .744 .794 .868 .695 .794 .860 .688 MELD-Na .843 .910 .698 .824 .908 .702 .824 .912 .685 .824 .907 .680 MELD 3.0 without Albumin .848 .920 .705 .827 .917 .709 .827 .922 .698 .827 .917 .682 MELD 3.0 with Albumin .828 .888 .707 .809 .885 .712 .809 .889 .698 .809 .884 .686 ReMELD-Na .847 .901 .752 .848 .897 .755 .819 .902 .707 .820 .897 .705 Abbreviations: c-index: Harrell’s concordance index; iAUC: integrated area under the curve; MELD: The Model for End-Stage Liver Disease; Na: Sodium. In our cohort, MELD 3.0 without albumin demonstrated the highest predictive accuracy for three-month survival, with an AUC of 0.848 and c-index of 0.827 in the overall population. ReMELD-Na followed closely with an AUC of 0.847 and a superior c-index of 0.848, indicating slightly more robust concordance across time. MELD-Na also performed well (AUC 0.843, c-index 0.824), while MELD 3.0 with albumin matched reMELD-Na’s AUC (0.847) but with slightly lower concordance (c-index 0.809). MELD Original consistently ranked lowest across all metrics (AUC 0.824, c-index 0.794). When stratified by sex (Figure 1), MELD 3.0 without albumin yielded the strongest performance among male patients, with an AUC of 0.920 and c-index of 0.917, followed by MELD-Na (AUC 0.910, c-index 0.908) and reMELD-Na (AUC 0.900, c-index 0.902. Female patients demonstrated generally lower model performance, consistent with existing literature on sex-based disparities in MELD-based scores. Nevertheless, reMELD-Na provided the best three-month survival prediction in women , with an AUC of 0.753 and c-index of 0.755, outperforming MELD 3.0 with albumin (AUC 0.707, c-index 0.712), MELD 3.0 without albumin (AUC 0.705, c-index 0.709), MELD-Na (AUC 0.698, c-index 0.702), and MELD Original (AUC 0.740, c-index 0.744). For overall survival (Figure 2) , MELD 3.0 without albumin maintained strong predictive led performance in the overall cohort, with both an AUC and c-index of 0.827. ReMELD-Na and MELD-Na each followed with an AUC of 0.819 and 0.824, with corresponding c-indices of 0.820 and 0.824, respectively. MELD Original remained the least accurate (AUC 0.794, c-index 0.794). Among men, MELD 3.0 without albumin was superior (AUC 0.922, c-index 0.827), followed by MELD-Na (AUC 0.912, c-index 0.824) and reMELD-Na (AUC 0.902, c-index 0.897). In women, reMELD-Na yielded the highest discriminative performance (AUC 0.707, c-index 0.705), however, slightly ahead of MELD 3.0 with albumin (AUC 0.698, c-index 0.686, MELD 3.0 without albumin (AUC 0.698, c-index 0.682), MELD-Na (AUC 0.718, c-index 0.702), and MELD Original (AUC 0.727, c-index 0.688). Discussion These findings suggest that MELD 3.0 (particularly without albumin) and reMELD-Na provide improved predictive accuracy for short-term and long-term survival in LT candidates in the study cohort, outperforming MELD-Na and original MELD. Notably, MELD 3.0 without albumin yielded the highest predictive accuracy in men, while reMELD-Na comparatively performed best in female patients and followed by MELD 3.0 without albumin. This finding aligns with the known limitations of prior MELD-based models in accurately estimating mortality risk in women.(3,6) While the German and American transplant populations differ slightly, particularly in terms of racial diversity, the underlying disease profiles and allocation priorities are largely comparable.(7–9) Sex-based differences in liver disease and transplant allocation have been previously reported and appear relevant in our cohort. Female patients are disproportionately affected by conditions such as MASLD (metabolic dysfunction-associated steatotic liver disease), while male patients more commonly present with alcohol-related cirrhosis.(10,11) Additionally, women have historically had lower access to transplantation, even in systems with shorter waiting times, due to factors including smaller body size, lower creatinine levels, hence affecting the proportionate of the MELD score, and potential implicit bias.(12) The mean original MELD of this cohort is 17.87 ± 8.22, which is slightly higher than the trend in Europe and South Korea, but comparable to the United States.(6,13,14) The inclusion of sex as a variable is critical, as previous MELD versions consistently underestimated disease severity in women due to their lower serum creatinine levels, which often fail to reflect true renal impairment.(3,14) By explicitly adjusting for female sex and incorporating albumin, MELD 3.0 provides a more adequate risk profile for LT candidates, particularly those disadvantaged by prior models.(3,6,15) Compared to the U.S. cohort, in which MELD 3.0 achieved a c-index of 0.869, our cohort demonstrated a c-index of 0.809 for both three-month and overall survival. This is still relatively comparable and higher than that reported in a South Korean cohort, where the c-index was 0.564.(3,6) MELD 3.0 also reclassified a greater number of female patients to higher priority categories, thereby addressing a long-standing gender inequity in waitlist mortality, which has been shown in several cohorts.(3,6,15) This enhancement addresses one of the critical limitations of earlier MELD versions: the underestimation of disease severity in women, partly due to the reliance on serum creatinine. Thus, it was expected and confirmed in our study that MELD 3.0 improved predictive performance. On the other side, although not incorporating gender specifically in the formula, reMELD-Na focuses on optimizing the existing variables, which incorporates bilirubin, creatinine, INR, and sodium, by reweighing their coefficients and redefining their upper and lower bounds based on data-driven thresholds according to European database.(4,5,16) While reMELD-Na does not explicitly include sex or albumin, its parameter recalibration indirectly benefits female patients by down-weighting creatinine and optimizing the ratio of sodium in the formula.(5) In our study, these advantages were reflected in the comparative performance metrics in female cohort. This study confirmed that both MELD 3.0 and reMELD-Na outperform older scoring systems, offering enhanced prognostic accuracy and improved fairness. MELD 3.0 addresses systemic sex disparities by explicitly modeling female sex and hypoalbuminemia, while reMELD-Na achieves equitable performance through rigorous regional recalibration and parameter optimization. The strong performance of reMELD-Na in women, despite the absence of a sex term, also highlights the value of data-driven model refitting in improving transplant equity across diverse patient subgroups. Compared to MELD-Na, reMELD-Na more accurately reflects true mortality risk by using updated coefficients and narrower parameter caps, leading to a better fit for European patients.(5,16,17) The reclassification trends in our study show that MELD 3.0, particularly the version without albumin, more frequently increases MELD scores for women with advanced liver disease, whereas men were more commonly upshifted from lower MELD categories (MELD <20). For example, women with original MELD scores in the 30-39 range often gained additional points under MELD 3.0, which should have improved their prioritization. These results suggest that MELD 3.0 may better reflect sex-specific differences in disease severity, which has also been shown in several studies.(3,6) The pattern of upward reclassification was also more evident among non-HCC patients, with 27.6% experiencing a shift to a higher MELD category, compared to 12.5% among patients with HCC.(18) Despite the improvements introduced by MELD 3.0 and reMELD-Na, several important limitations persist. Both scores primarily capture hepatic and renal dysfunction, failing to account for extrahepatic organ failures, such as respiratory and circulatory problem or refractory ascites, which limits their prognostic value in patients with acute-on-chronic liver failure (ACLF). MELD-based models have been shown to underestimate mortality risk in ACLF, especially in patients requiring mechanical ventilation or vasopressor support.(19,20) Additionally, MELD 3.0’s use of serum albumin may be confounded by exogenous albumin administration, while both models lack dynamic inputs that reflect rapid clinical deterioration or systemic inflammation.(6) As one of the largest LT centers in Germany,(7) the findings likely reflect broader national trends. It is, however, important to recognize that MELD-based models, by design, do not incorporate disease etiology or predict post-transplant outcomes, but rather estimate pre-transplant mortality risk. Additionally, in our retrospective dataset, the information of albumin and sodium substitution were inconsistently documented, limiting the robustness of MELD 3.0 with albumin and sodium-based scoring models in the analysis. Prospective multicenter validation studies across the ET region are needed to determine whether reMELD-Na continues to improve equity and outcomes in diverse patient populations. Such studies should specifically assess whether sex-based disparities persist under reMELD-Na, and whether further refinements, such as incorporating a female-specific adjustment, could enhance equality in allocation. Both MELD 3.0 and reMELD-Na represent significant advancements in mortality prediction for liver transplant candidates. Their implementation, in comparison to the previous MELD models, can enhance organ allocation strategies and potentially reduce disparities, particularly for female patients. Declarations Acknowledgements Assistance with the study: none Presentation: none Financial Support and Sponsorship: None Conflicts of Interest: Nothing to report Authors Contributions: AJP, PH: Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Visualization, Writing; DJ: Methodology, Software; ZC, AM, MWB: Review & Editing; UM, CM: Supervision, Validation, Review & Editing References Forman L (2001) Predicting the prognosis of chronic liver disease: An evolution from child to MELD. Hepatol Februar 33(2):473–475 Malinchoc M, Kamath PS, Gordon FD, Peine CJ, Rank J, Ter Borg PCJ (2000) A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatol April 31(4):864–871 Kim WR, Mannalithara A, Heimbach JK, Kamath PS, Asrani SK, Biggins SW (2021) u. a. MELD 3.0: The Model for End-Stage Liver Disease Updated for the Modern Era. Gastroenterol Dezember 161(6):1887–1895e4 Eurotransplant Chapter 5: ET Liver Allocation System (ELAS). Eurotransplant Manual, Version 6.14, March 25, 2025. [Internet]. [zitiert 31. März 2025]. Verfügbar unter: https://www.eurotransplant.org/wp-content/uploads/2025/03/H5-ELAS-MELD-March-25th-2025-2.pdf Goudsmit BFJ, Putter H, Tushuizen ME, Vogelaar S, Pirenne J, Alwayn IPJ (2021) u. a. Refitting the Model for End-Stage Liver Disease for the Eurotransplant Region. Hepatol Juli 74(1):351–363 Yoo JJ, Chang JI, Moon JE, Sinn DH, Kim SG, Kim YS (2023) Validation of MELD 3.0 scoring system in East Asian patients with cirrhosis awaiting liver transplantation. Liver Transpl Oktober 29(10):1029–1040 Loosen SH, Bock HH, Hellmich M, Knoefel WT, Trautwein C, Keitel V Hospital Mortality and Current Trends in Liver Transplantation in Germany. Dtsch Ärztebl Int [Internet]. 26. Juli 2021 [zitiert 6. Juni 2025]; Verfügbar unter: https://www.aerzteblatt.de/ 10.3238/arztebl.m2021.0210 Terrault NA, Francoz C, Berenguer M, Charlton M, Heimbach J (2023) Liver Transplantation 2023: Status Report, Current and Future Challenges. Clin Gastroenterol Hepatol Juli 21(8):2150–2166 Bucher JN, Koenig M, Schoenberg MB, Crispin A, Thomas M, Angele MK (Oktober 2019) u. a. Liver transplantation in patients with a history of migration—A German single center comparative analysis. Dor FJ, Herausgeber. PLOS ONE. 22. 14(10):e0224116 Becker EC, Siddique O, O’Sullivan DM, Dar W, Einstein M, Morgan G (2024) u. a. Disparities in Liver Transplantation for Nonalcoholic Steatohepatitis in Women. Transplantation August 108(8):e181–e186 Battistella S, D’Arcangelo F, Grasso M, Zanetto A, Gambato M, Germani G (2023) u. a. Liver transplantation for non-alcoholic fatty liver disease: indications and post-transplant management. Clin Mol Hepatol 28 Februar 29(Suppl):S286–301 Tejedor M, Neria F, De La Rosa G, Almohalla Álvarez C, Padilla M (2024) Boscà Robledo A, u. a. Women Are Also Disadvantaged in Accessing Transplant Outside the United States: Analysis of the Spanish Liver Transplantation Registry. Transpl Int 7 Mai 37:12732 Di Maira T, Cailliez V, Domínguez-Gil B, Mahíllo B, Álvarez M, Belli LS (2025) Regional Variations and Trends in Liver Transplantation Practices Across Europe. JHEP Rep April ;101424 Lai JC, Ganger DR, Volk ML, Dodge JL, Dunn MA, Duarte-Rojo (März 2021) A, u. a. Association of Frailty and Sex With Wait List Mortality in Liver Transplant Candidates in the Multicenter Functional Assessment in Liver Transplantation (FrAILT) Study. JAMA Surg. 1. 156(3):256 Bittermann T, Mahmud N, Weinberg EM, Reddy KR (2023) MELD 3.0 leads to heterogeneous prioritization of men and women on the liver transplant waiting list. Liver Transpl Juni 29(6):655–657 Biggins SW, Kim WR, Terrault NA, Saab S, Balan V, Schiano T (2006) u. a. Evidence-Based Incorporation of Serum Sodium Concentration Into MELD. Gastroenterol Mai 130(6):1652–1660 Ruf AE, Kremers WK, Chavez LL, Descalzi VI, Podesta LG, Villamil FG (2005) Addition of serum sodium into the MELD score predicts waiting list mortality better than MELD alone. Liver Transpl März 11(3):336–343 Kim K, Kim DG, Lee JG, Joo DJ, Lee HW (2023) The Effect of Model for End-Stage Liver Disease 3.0 on Disparities between Patients with and without Hepatocellular Carcinoma in Korea. Yonsei Med J 64(11):647 Sundaram V, Jalan R, Wu T, Volk ML, Asrani SK, Klein AS (2019) u. a. Factors Associated with Survival of Patients With Severe Acute-On-Chronic Liver Failure Before and After Liver Transplantation. Gastroenterol April 156(5):1381–1391e3 Sundaram V, Shah P, Mahmud N, Lindenmeyer CC, Klein AS, Wong RJ (2020) u. a. Patients with severe acute-on‐chronic liver failure are disadvantaged by model for end‐stage liver disease‐based organ allocation policy. Aliment Pharmacol Ther Oktober 52(7):1204–1213 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Sep, 2025 Read the published version in Langenbeck's Archives of Surgery → Version 1 posted Editorial decision: Revision requested 09 Aug, 2025 Reviews received at journal 08 Aug, 2025 Reviewers agreed at journal 05 Aug, 2025 Reviewers invited by journal 29 Jul, 2025 Editor assigned by journal 29 Jul, 2025 Submission checks completed at journal 28 Jul, 2025 First submitted to journal 26 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7219777","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492376757,"identity":"37bb2f18-c4fd-4570-82e8-46f3969b1b86","order_by":0,"name":"Aghnia Jolanda Putri","email":"data:image/png;base64,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","orcid":"","institution":"University Hospital of Heidelberg","correspondingAuthor":true,"prefix":"","firstName":"Aghnia","middleName":"Jolanda","lastName":"Putri","suffix":""},{"id":492376759,"identity":"61266c1d-beb8-4f97-a23b-6d1c3c1f960e","order_by":1,"name":"Decan Jiang","email":"","orcid":"","institution":"University Hospital of Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Decan","middleName":"","lastName":"Jiang","suffix":""},{"id":492376760,"identity":"660810d4-be6b-447c-b30f-0422f13ae22e","order_by":2,"name":"Zoltan Czigany","email":"","orcid":"","institution":"University Hospital of Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Zoltan","middleName":"","lastName":"Czigany","suffix":""},{"id":492376761,"identity":"4f30eca1-a1b2-4ac6-bc96-dfcacfe62e83","order_by":3,"name":"Arianeb Mehrabi","email":"","orcid":"","institution":"University Hospital of Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Arianeb","middleName":"","lastName":"Mehrabi","suffix":""},{"id":492376762,"identity":"36bcf129-ed16-4b5d-9b54-3301c2f21989","order_by":4,"name":"Markus Wolfgang Buechler","email":"","orcid":"","institution":"University Hospital of Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"Wolfgang","lastName":"Buechler","suffix":""},{"id":492376763,"identity":"18f4de55-5406-4386-a69c-1bfac0894883","order_by":5,"name":"Uta Merle","email":"","orcid":"","institution":"University Hospital of Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Uta","middleName":"","lastName":"Merle","suffix":""},{"id":492376765,"identity":"885532bd-5285-4dd6-ba23-2a2859222ab8","order_by":6,"name":"Christoph Michalski","email":"","orcid":"","institution":"University Hospital of Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Christoph","middleName":"","lastName":"Michalski","suffix":""},{"id":492376766,"identity":"abe39848-c1e5-4c35-81d7-0a348e2c97f5","order_by":7,"name":"Peri Husen","email":"","orcid":"","institution":"University Hospital of Heidelberg","correspondingAuthor":false,"prefix":"","firstName":"Peri","middleName":"","lastName":"Husen","suffix":""}],"badges":[],"createdAt":"2025-07-26 08:38:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7219777/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7219777/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00423-025-03846-x","type":"published","date":"2025-09-25T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":88036934,"identity":"cc425bff-b2ce-4386-8a65-c2355295150a","added_by":"auto","created_at":"2025-07-31 16:24:05","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":85964,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive Value of MELD Scores for 3-Month Survival in Waitlisted Patients in (A) All Patients, (B) Men and (C) Women.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7219777/v1/89289ffd3d79f3603493712e.png"},{"id":88036933,"identity":"cc643847-abdd-495d-8200-6a86cd9539a8","added_by":"auto","created_at":"2025-07-31 16:24:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77597,"visible":true,"origin":"","legend":"\u003cp\u003ePredictive Value of MELD Scores for Overall Survival in Waitlisted Patients in (A) All Patients, (B) Men and (C) Women.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7219777/v1/200f3182a9d243b6f57b57a0.png"},{"id":92431094,"identity":"812c92be-f200-4b59-a190-6199d74894d1","added_by":"auto","created_at":"2025-09-29 16:08:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1287083,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7219777/v1/00e51256-efe7-473a-aa25-9934eb043ad5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Performances and Validation of MELD 3.0 and ReMELD-Na Scoring Systems: A German Clinical Cohort Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe Model for End-Stage Liver Disease (MELD) score has revolutionized the liver transplantation (LT) allocation systems. Originally developed to predict mortality following transjugular intrahepatic portosystemic shunt (TIPSS) placement, it was later found to be a valuable tool for estimating mortality risk among patients listed for LT. The MELD score was officially introduced for LT purpose in 2002 and has undergone several updates since then.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) The second iteration of the MELD system in 2007, MELD-Na, improved prognostic accuracy by incorporating serum sodium levels.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eIn the United States, the most recent version of the MELD score, known as MELD 3.0, was implemented by United Network for Organ Sharing (UNOS) in 2023..(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) By including new variables such as sex and serum albumin, MELD 3.0 provides a more nuanced assessment of mortality risk, that yielded to improvements in mortality prediction and transplant outcomes among LT recipients. This adjustment is particularly important given the systemic disadvantages women face at multiple stages of the transplant process from referral and evaluation to listing and ultimately receiving a LT.(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) Notably, steatotic liver disease (SLD) has emerged as the leading indication for LT among women without hepatocellular carcinoma, a trend expected to continue in the coming years. Despite its widespread adoption, regional differences in patient demographics, healthcare access, and outcomes underscore the need for local validation. In Germany, for instance, the original MELD formula is still in use, which does not account for patient sex or serum albumin levels. Validating MELD 3.0 in the German context is essential, as it could significantly influence patient selection, waiting list mortality, and post-transplant survival, particularly among vulnerable groups such as women and patients with impaired renal function. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eAfter relying on the original MELD score since its adoption in 2002, Germany has recently transitioned to using a refitted version of MELD-Na (reMELD-Na) as of March 2025,(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) following its implementation by Eurotransplant (ET). It was developed by reweighing the coefficients and re-establishing clinically meaningful lower and upper bounds for creatinine, bilirubin, INR, and sodium. Unlike the original MELD score, which capped a significant proportion of patient values and potentially overemphasized renal dysfunction, reMELD-Na limits such distortions by refining parameter bounds and reducing the disproportionate influence of creatinine.(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) Importantly, its calculation on sodium enhances its ability to identify high-risk patients with hyponatremia.\u003c/p\u003e\u003cp\u003eThe present study seeks to address a gap in the existing literature by examining the performance of both MELD 3.0 and reMELD-Na at one of Germany\u0026rsquo;s largest LT centers, and situating these findings within the broader international developments in MELD-based scoring systems.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cb\u003eStudy Population\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThis study is a retrospective clinical cohort study designed to assess the validity of the MELD 3.0(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) and reMELD-Na(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) scoring systems in predicting mortality and transplant outcomes among patients listed for LT at our center. Informed consent was obtained. The analysis is based on a dataset from the Department of General, Visceral and Transplant Surgery at the University Hospital of Heidelberg, Germany. The study included adults (\u0026ge;\u0026thinsp;18 years) listed for liver transplantation at our center between January 1, 2017, and December 31, 2021, with a minimum follow-up of three years. Patients with high-urgency status, re-transplantations, listings under standard exception criteria, or incomplete data were excluded.\u003c/p\u003e\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003ePatient characteristics were reported as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation for continuous variables and as frequencies (percentages) for categorical variables. Subgroup and sensitivity analyses were conducted based on sex (male vs. female). A two-sided p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. The predictive accuracy of MELD models was assessed using Harrell\u0026rsquo;s c-index and Heagerty\u0026rsquo;s integrated time-dependent AUC (iAUC). Differences in c-index estimates with 95% confidence intervals were analyzed. All analyses were conducted in R Studio (Posit PBC version 4.4.3) and in consultation with the Institute for Medical Biometry and Informatics, University of Heidelberg. The study was conducted in accordance with the latest version of the Declaration of Helsinki and the Declaration of Istanbul. Ethical approval was obtained from the local ethics committee (S-105/2025).\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003eDemographic and Clinical Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eDuring the study period from 2017 to 2021, a total of 392 patients were listed for LT. The mean age of the whole cohort was 53.19\u0026thinsp;\u0026plusmn;\u0026thinsp;10.91 years, with 62.5% of the patients being male. Patients listed for high urgency transplantation (n\u0026thinsp;=\u0026thinsp;42 patients), re-transplantation (n\u0026thinsp;=\u0026thinsp;51 patients), or with malignancies qualifying for standard exception criteria (e.g., HCC, neuroendocrine tumors) (n\u0026thinsp;=\u0026thinsp;87 patients) were excluded. In addition, six patients were excluded due to incomplete data resulting from transfer to another center. Upon exclusion, the final study population consisted of 206 patients, with a mean age of 53.08\u0026thinsp;\u0026plusmn;\u0026thinsp;10.35 years and a male proportion of 59.2% (Table\u0026nbsp;1). Within three months of listing, 22 patients were removed from the waiting list due to worsening clinical condition, and nine patients due to improvement.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e.\u0026nbsp;\u003cstrong\u003ePatient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"592\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 209px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 226px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 110px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 47px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eM (n=122)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003eF (n=84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003eAge (Mean, SD)\u003c/p\u003e\n \u003cp\u003eAge (Median, IQR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e52.02 \u0026plusmn; 10.14\u003c/p\u003e\n \u003cp\u003e54 (48-59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e54.62 \u0026plusmn; 10.51\u003c/p\u003e\n \u003cp\u003e58 (52-61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e53.08 \u0026plusmn; 10.35\u003c/p\u003e\n \u003cp\u003e55 (49-60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" style=\"width: 545px;\"\u003e\n \u003cp\u003eEtiology (n, %)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;Alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e51 (56.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e39 (43.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e90 (43.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;PSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e20 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e10 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e30 (14.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;MASLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6 (42.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e8 (57.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e14 (6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;Chronic HBV/HDV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7 (53.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6 (46.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e13 (6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;PBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e9 (75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e12 (5.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;Hepatitis C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e8 (88.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e1 (11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e9 (4.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;Autoimmune hepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e2 (33.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4 (66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e6 (2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;SSC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e3 (100)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e3 (1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;Cryptogenic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e4 (40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e6 (60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e10 (4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;Others\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e12 (63.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e7 (36.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e19 (9.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 545px;\"\u003e\n \u003cp\u003eMELD score at listing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;Original MELD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e18.25 \u0026plusmn; 8.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e17.32 \u0026plusmn; 8.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e17.87 \u0026plusmn; 8.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;MELD-Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e20.02 \u0026plusmn; 8.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e18.82 \u0026plusmn; 8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e19.53 \u0026plusmn; 8.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;MELD 3.0 without albumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e20.34 \u0026plusmn; 8.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e19.10 \u0026plusmn; 8.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e19.83 \u0026plusmn; 8.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026emsp;MELD 3.0 with albumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e20.68 \u0026plusmn; 8.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e19.54 \u0026plusmn; 8.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e20.21 \u0026plusmn; 8.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 209px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; ReMELD-Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e16.78 \u0026plusmn; 7.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 113px;\"\u003e\n \u003cp\u003e15.80 \u0026plusmn; 7.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 110px;\"\u003e\n \u003cp\u003e16.38 \u0026plusmn; 7.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: HBV, hepatitis B virus; HDV, hepatitis D virus; IQR, interquartile range; MELD, Model for End-Stage Liver Disease; Na, sodium; MASLD, metabolic dysfunction-associated steatotic liver disease; PSC, primary sclerosing cholangitis; SD, standard deviation; SSC, secondary sclerosing cholangitis.\u003c/p\u003e\n\u003cp\u003eAlcohol-related cirrhosis was identified as the most common etiology of liver disease, accounting for 90 cases (43.7%), followed by primary sclerosing cholangitis (PSC), which was observed in 30 cases (14.6%). Metabolic dysfunction-associated steatotic liver disease (MASLD) was present in 14 patients (6.8%), of whom 57% were female. During the study period, 100 patients eventually underwent LT, leaving 106 patients untransplanted until the end of the observation period. MELD scores of the study cohort were calculated using various scoring systems. The original MELD score was 17.8 \u0026plusmn; 8.22, MELD-Na was 19.53 \u0026plusmn; 8.57, MELD 3.0 without albumin was 19.83 \u0026plusmn; 8.67, MELD 3.0 with albumin was 20.21 \u0026plusmn; 8.87, and ReMELD-Na was 16.38 \u0026plusmn; 7.17. Across all MELD formulas, female patients exhibited lower scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eetiology of liver disease\u003c/strong\u003e\u0026nbsp;\u003c/strong\u003eshowed no statistically significant association with either short-term or long-term survival in non-transplanted patients using the original MELD formula. In relation to sex, female patients have a 23% lower relative risk of survival during the observed period. However, this difference did not reach statistical significance (Table 2). For overall survival, the hazard ratio for female patients was 0.96, suggesting comparable long-term outcomes between sexes (p = 0.88) (Table 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003e \u003cstrong\u003eCorrelation of Patient Characteristics with Three-Month Survival and Overall Survival in Non-Transplanted Patients Within Three-Months of Listing, (Listed Using the Original MELD Score)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"596\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 213px;\"\u003e\n \u003cp\u003eThree-month survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 204px;\"\u003e\n \u003cp\u003eOverall survival\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003cp\u003en=168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003en=99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003en=69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003cp\u003en=168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003cp\u003en=99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003cp\u003en=69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eAge\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 219px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eEtiology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 219px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 76px;\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 55px;\"\u003e\n \u003cp\u003e151 (89.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 73px;\"\u003e\n \u003cp\u003e88 (88.8) ref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e63 (91.3)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHR 0.77\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 58px;\"\u003e\n \u003cp\u003e98 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e58 (58.5) ref.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e49 (71) \u003cstrong\u003eHR 0.96\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 47px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReclassification of Waitlisted Patients using Different MELD Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eReclassification across MELD scoring systems was performed to evaluate the pattern of score shifts resulting from the application of different calculation formulas. For this analysis, MELD Original, MELD-Na, and MELD 3.0 were included. ReMELD-Na was excluded due to its capped maximum value of 36, which hindered a direct one-to-one comparison with the other scoring systems. Reclassification of LT candidates was assessed by comparing categorical thresholds across the scoring systems: \u0026lt;10, 11-19, 20-29, 30-39, and \u0026ge;40. A contingency table was constructed to evaluate the extent of changes in patient categorization following the application of MELD 3.0, both with and without albumin. When MELD 3.0 was applied, notable reclassifications were observed, with some patients moving to higher and others to lower MELD categories. Upward reclassification occurred more frequently in male patients within the lower MELD categories, indicating a shift in perceived severity under the updated model. Conversely, among patients with higher original MELD scores, female patients were more likely to receive even higher MELD 3.0 scores, suggesting that MELD 3.0 may more accurately capture disease severity in women at advanced stages of liver disease in comparison to original MELD (Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Changes in Patient Classification on the Liver Transplant Waiting List Following MELD Score Adjustment (A. MELD Original vs. MELD 3.0; B. MELD-Na vs. MELD 3.0)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD 3.0 without Albumin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD Original\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20-29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e(M:F, 11:8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(M:F, 5:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e10-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e82\u003c/p\u003e\n \u003cp\u003e(M:F, 44:38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003cp\u003e(M:F, 11:9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003cp\u003e(M:F, 2:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003cp\u003e(M:F, 32:14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(M:F, 6:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(M:F, 5:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(M:F, 2:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(M:F, 4:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD 3.0 with Albumin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD Original\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20-29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003cp\u003e(M:F, 11:8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(M:F, 5:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e10-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(M:F, 1:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003cp\u003e(M:F, 42:35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003cp\u003e(M:F, 12:12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e47\u003c/p\u003e\n \u003cp\u003e(M:F, 32:15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003cp\u003e(M:F, 8:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003cp\u003e(M:F, 3:0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(M:F, 4:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003cp\u003e(M:F, 4:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD 3.0 without Albumin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD-Na\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20-29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003cp\u003e(M:F, 11:7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(M:F, 2:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e10-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e(M:F, 0:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003cp\u003e(M:F, 46:37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(M:F, 3:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(M:F, 3:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003cp\u003e(M:F, 39:19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(M:F, 4:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(M:F, 1:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003cp\u003e(M:F, 7:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(M:F, 1:4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(M:F, 5:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"5\" style=\"width: 460px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD 3.0 with Albumin\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD Na\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10-19\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e20-29\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e30-39\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026lt;10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e18\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(M:F, 11:7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003cp\u003e(M:F, 2:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e10-19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(M:F, 1:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003cp\u003e(M:F, 44:34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003cp\u003e(M:F, 4:6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e20-29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(M:F, 1:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003cp\u003e(M:F, 39:20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(M:F, 6:2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e30-39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003cp\u003e(M:F, 1:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(M:F, 5:1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(M:F, 3:5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 124px;\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 83px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 94px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 97px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 98px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003cp\u003e(M:F, 5:3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAbbreviations: MELD: The Model for End-Stage Liver Disease; Na: Sodium.\u003c/p\u003e\n\u003cp\u003eAmong 19 patients initially classified with an original MELD score of \u0026lt;10, eight were reclassified into the 11-19 category under MELD 3.0 without albumin, while 19 remained in the same category. In the 11-19 group, the majority (82 patients) retained their classification, but 20 patients were reclassified into the 20-29 range, with a nearly equal distribution between male and female patients. In the original 20-29 category, 46 patients remained unchanged, whereas eight were reclassified into the higher-risk 30-39 category and four were downgraded to the 11-19 range. Among those originally scored in the 30-39 group, six remained in the same category, while another six were reclassified into the 40 group, two-thirds of whom were female. All patients originally categorized as MELD 40 remained in that category, indicating no downgrades among the highest-risk patients. When comparing MELD-Na to MELD 3.0 without albumin, most patients remained in their original risk category. However, reclassifications also occurred, but not as much in comparison to the original MELD. Among those initially scored \u0026lt;10 by MELD-Na, five patients were reclassified into the 10-19 group under MELD 3.0. Within the 10-19 group, 83 patients remained stable, but seven patients were reclassified; six into the 20-29 category and one downgraded to \u0026lt;10. Reclassifications into higher categories continued among MELD-Na 20-29 patients, where five patients shifted to either 30-39, where four of them were male. Among patients in the 30-39 category, seven were reclassified, which two moved downward to 20-29 and five moved upward into the \u0026ge;40 category, with four of the five being female. All eight patients originally categorized as MELD 40 retained their classification under MELD 3.0. A similar trend was observed when comparing MELD-Na to MELD 3.0 with albumin. Although fewer patients were reclassified compared to the version without albumin, upward shifts were still evident, particularly in female patients within higher MELD categories. For instance, of the eight patients moved into the MELD \u0026ge;40 category from 30-39, five were female. Overall, the reclassification trends show that MELD 3.0, particularly the version without albumin, more frequently increases MELD scores for women with advanced liver disease, whereas men were more commonly upshifted from lower MELD categories.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePredictive Performance and Discriminative Ability of Different MELD Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the median observational period of 33.90 months (IQR 11.55-59.38), 100 patients (51.5 %) underwent LT, with a post-transplant survival rate of 70% at the end of the observation period. Of the 106 patients who did not undergo LT until the end of the study period, 54 patients (50.9%) remained alive by the end of the study. Thirty-eight patients (18.4%) received their transplant within three months of being listed according to original MELD score. Sixteen patients died within three months of being listed. The most common causes of death were liver failure (n=7) and sepsis (n=7), followed by bleeding, primarily due to fundic variceal hemorrhage. The predictive performance of MELD-based scoring systems was evaluated for both three-month and overall survival. Discriminative ability was assessed using Harrell\u0026rsquo;s concordance index (c-index) and the integrated area under the curve (iAUC) (Table 4). The analysis includes only patients who were still not transplanted within three months of listing, resulting in a cohort of 168 patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e. \u003cstrong\u003eComparative Discrimination Ability of MELD Original and MELD 3.0\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMELD Scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 229px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eThree-month survival\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"6\" style=\"width: 208px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall survival\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 131px;\"\u003e\n \u003cp\u003eiAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 98px;\"\u003e\n \u003cp\u003eHarrel c-index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 108px;\"\u003e\n \u003cp\u003eiAUC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 100px;\"\u003e\n \u003cp\u003eHarrel c-index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003eAll\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003eW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eMELD Original\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e.868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.688\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eMELD-Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.843\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e.824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.680\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eMELD 3.0 without Albumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e.922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eMELD 3.0 with Albumin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.888\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e.885\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e.889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.698\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e.809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 119px;\"\u003e\n \u003cp\u003eReMELD-Na\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 41px;\"\u003e\n \u003cp\u003e.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 45px;\"\u003e\n \u003cp\u003e.752\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 34px;\"\u003e\n \u003cp\u003e.848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 29px;\"\u003e\n \u003cp\u003e.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 35px;\"\u003e\n \u003cp\u003e.755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 39px;\"\u003e\n \u003cp\u003e.819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36px;\"\u003e\n \u003cp\u003e.902\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31px;\"\u003e\n \u003cp\u003e.820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 37px;\"\u003e\n \u003cp\u003e.897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 32px;\"\u003e\n \u003cp\u003e.705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAbbreviations: c-index: Harrell\u0026rsquo;s concordance index; iAUC: integrated area under the curve; MELD: The Model for End-Stage Liver Disease; Na: Sodium.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn our cohort, MELD 3.0 without albumin \u003cstrong\u003edemonstrated the highest predictive accuracy for three-month survival, with an AUC of 0.848\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003ec-index of 0.827 in the overall population. ReMELD-Na\u003c/strong\u003e\u0026nbsp;\u003c/strong\u003efollowed closely with an\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003eAUC of 0.847\u003c/strong\u003e\u0026nbsp;\u003c/strong\u003eand a superior\u003cstrong\u003e\u0026nbsp;\u003cstrong\u003ec-index of 0.848,\u0026nbsp;\u003c/strong\u003e\u003c/strong\u003eindicating slightly more robust concordance across time. MELD-Na also performed well (AUC 0.843, c-index 0.824), while MELD 3.0 with albumin matched reMELD-Na\u0026rsquo;s AUC (0.847) but with slightly lower concordance (c-index 0.809). MELD Original consistently ranked lowest across all metrics (AUC 0.824, c-index 0.794).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhen stratified by sex (Figure 1), MELD 3.0 without albumin yielded the strongest performance among male patients, with an AUC of 0.920 and c-index of 0.917, followed by MELD-Na (AUC 0.910, c-index 0.908) and reMELD-Na (AUC 0.900, c-index 0.902. Female patients \u003cstrong\u003edemonstrated generally lower model performance, consistent with existing literature on sex-based disparities in MELD-based scores. Nevertheless, reMELD-Na provided the best three-month survival prediction in women\u003c/strong\u003e\u003cstrong\u003e,\u003c/strong\u003e with an AUC of 0.753 and c-index of 0.755, outperforming MELD 3.0 with albumin (AUC 0.707, c-index 0.712), MELD 3.0 without albumin (AUC 0.705, c-index 0.709), MELD-Na (AUC 0.698, c-index 0.702), and MELD Original (AUC 0.740, c-index 0.744).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor overall survival (Figure 2)\u003cstrong\u003e,\u003c/strong\u003e MELD 3.0 without albumin maintained strong predictive led performance in the overall cohort, with both an AUC and c-index of 0.827. ReMELD-Na and MELD-Na each followed with an AUC of 0.819 and 0.824, with corresponding c-indices of 0.820 and 0.824, respectively. MELD Original remained the least accurate (AUC 0.794, c-index 0.794). Among men, MELD 3.0 without albumin was superior (AUC 0.922, c-index 0.827), followed by MELD-Na (AUC 0.912, c-index 0.824) and reMELD-Na (AUC 0.902, c-index 0.897). In women, reMELD-Na yielded the highest discriminative performance (AUC 0.707, c-index 0.705), however, slightly ahead of MELD 3.0 with albumin (AUC 0.698, c-index 0.686, MELD 3.0 without albumin (AUC 0.698, c-index 0.682), MELD-Na (AUC 0.718, c-index 0.702), and MELD Original (AUC 0.727, c-index 0.688).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThese findings suggest that MELD 3.0 (particularly without albumin) and reMELD-Na provide improved predictive accuracy for short-term and long-term survival in LT candidates in the study cohort, outperforming MELD-Na and original MELD. Notably, MELD 3.0 without albumin yielded the highest predictive accuracy in men, while reMELD-Na comparatively performed best in female patients and followed by MELD 3.0 without albumin. This finding aligns with the known limitations of prior MELD-based models in accurately estimating mortality risk in women.(3,6)\u0026nbsp;While the German and American transplant populations differ slightly, particularly in terms of racial diversity, the underlying disease profiles and allocation priorities are largely comparable.(7\u0026ndash;9)\u0026nbsp;Sex-based differences in liver disease and transplant allocation have been previously reported and appear relevant in our cohort. Female patients are disproportionately affected by conditions such as MASLD (metabolic dysfunction-associated steatotic liver disease), while male patients more commonly present with alcohol-related cirrhosis.(10,11)\u0026nbsp;Additionally, women have historically had lower access to transplantation, even in systems with shorter waiting times, due to factors including smaller body size, lower creatinine levels, hence affecting the proportionate of the MELD score, and potential implicit bias.(12)\u0026nbsp;The mean original MELD of this cohort is\u0026nbsp;17.87 \u0026plusmn; 8.22, which is slightly higher than the trend in Europe and South Korea, but comparable to the United States.(6,13,14)\u0026nbsp;The inclusion of sex as a variable is critical, as previous MELD versions consistently underestimated disease severity in women due to their lower serum creatinine levels, which often fail to reflect true renal impairment.(3,14)\u0026nbsp;By explicitly adjusting for female sex and incorporating albumin, MELD 3.0 provides a more adequate risk profile for LT candidates, particularly those disadvantaged by prior models.(3,6,15)\u0026nbsp;Compared to the U.S. cohort, in which MELD 3.0 achieved a c-index of 0.869, our cohort demonstrated a c-index of 0.809 for both three-month and overall survival. This is still relatively comparable and higher than that reported in a South Korean cohort, where the c-index was 0.564.(3,6)\u0026nbsp;MELD 3.0 also reclassified a greater number of female patients to higher priority categories, thereby addressing a long-standing gender inequity in waitlist mortality, which has been shown in several cohorts.(3,6,15)\u0026nbsp;This enhancement addresses one of the critical limitations of earlier MELD versions: the underestimation of disease severity in women, partly due to the reliance on serum creatinine.\u0026nbsp;Thus, it was expected and confirmed in our study that MELD 3.0 improved predictive performance. On the other side, although not incorporating gender specifically in the formula, reMELD-Na focuses on optimizing the existing variables, which incorporates bilirubin, creatinine, INR, and sodium, by reweighing their coefficients and redefining their upper and lower bounds based on data-driven thresholds according to European database.(4,5,16)\u0026nbsp;While reMELD-Na does not explicitly include sex or albumin, its parameter recalibration indirectly benefits female patients by down-weighting creatinine and optimizing the ratio of sodium in the formula.(5)\u0026nbsp;In our study, these advantages were reflected in the comparative performance metrics in female cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study confirmed that both MELD 3.0 and reMELD-Na outperform older scoring systems, offering enhanced prognostic accuracy and improved fairness. MELD 3.0 addresses systemic sex disparities by explicitly modeling female sex and hypoalbuminemia, while reMELD-Na achieves equitable performance through rigorous regional recalibration and parameter optimization. The strong performance of reMELD-Na in women, despite the absence of a sex term, also highlights the value of data-driven model refitting in improving transplant equity across diverse patient subgroups. Compared to MELD-Na, reMELD-Na more accurately reflects true mortality risk by using updated coefficients and narrower parameter caps, leading to a better fit for European patients.(5,16,17) The reclassification trends in our study show that MELD 3.0, particularly the version without albumin, more frequently increases MELD scores for women with advanced liver disease, whereas men were more commonly upshifted from lower MELD categories (MELD \u0026lt;20). For example, women with original MELD scores in the 30-39 range often gained additional points under MELD 3.0, which should have improved their prioritization. These results suggest that MELD 3.0 may better reflect sex-specific differences in disease severity, which has also been shown in several studies.(3,6) The pattern of upward reclassification was also more evident among non-HCC patients, with 27.6% experiencing a shift to a higher MELD category, compared to 12.5% among patients with HCC.(18)\u003c/p\u003e\n\u003cp\u003eDespite the improvements introduced by MELD 3.0 and reMELD-Na, several important limitations persist. Both scores primarily capture hepatic and renal dysfunction, failing to account for extrahepatic organ failures, such as respiratory and circulatory problem or refractory ascites, which limits their prognostic value in patients with acute-on-chronic liver failure (ACLF). MELD-based models have been shown to underestimate mortality risk in ACLF, especially in patients requiring mechanical ventilation or vasopressor support.(19,20) Additionally, MELD 3.0\u0026rsquo;s use of serum albumin may be confounded by exogenous albumin administration, while both models lack dynamic inputs that reflect rapid clinical deterioration or systemic inflammation.(6)\u003c/p\u003e\n\u003cp\u003eAs one of the largest LT centers in Germany,(7) the findings likely reflect broader national trends. It is, however, important to recognize that MELD-based models, by design, do not incorporate disease etiology or predict post-transplant outcomes, but rather estimate pre-transplant mortality risk. Additionally, in our retrospective dataset, the information of albumin and sodium substitution were inconsistently documented, limiting the robustness of MELD 3.0 with albumin and sodium-based scoring models in the analysis. Prospective multicenter validation studies across the ET region are needed to determine whether reMELD-Na continues to improve equity and outcomes in diverse patient populations. Such studies should specifically assess whether sex-based disparities persist under reMELD-Na, and whether further refinements, such as incorporating a female-specific adjustment, could enhance equality in allocation.\u003c/p\u003e\n\u003cp\u003eBoth MELD 3.0 and reMELD-Na represent significant advancements in mortality prediction for liver transplant candidates. Their implementation, in comparison to the previous MELD models, can enhance organ allocation strategies and potentially reduce disparities, particularly for female patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssistance with the study: none\u003c/p\u003e\n\u003cp\u003ePresentation: none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial Support and Sponsorship:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e Nothing to report\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors Contributions:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAJP, PH: Conceptualization, Methodology, Investigation, Data Curation, Formal Analysis, Visualization, Writing; DJ: Methodology, Software; ZC, AM, MWB: Review \u0026amp; Editing; UM, CM: Supervision, Validation, Review \u0026amp; Editing\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eForman L (2001) Predicting the prognosis of chronic liver disease: An evolution from child to MELD. Hepatol Februar 33(2):473\u0026ndash;475\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMalinchoc M, Kamath PS, Gordon FD, Peine CJ, Rank J, Ter Borg PCJ (2000) A model to predict poor survival in patients undergoing transjugular intrahepatic portosystemic shunts. Hepatol April 31(4):864\u0026ndash;871\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKim WR, Mannalithara A, Heimbach JK, Kamath PS, Asrani SK, Biggins SW (2021) u. a. MELD 3.0: The Model for End-Stage Liver Disease Updated for the Modern Era. Gastroenterol Dezember 161(6):1887\u0026ndash;1895e4\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEurotransplant Chapter 5: ET Liver Allocation System (ELAS). Eurotransplant Manual, Version 6.14, March 25, 2025. [Internet]. [zitiert 31. M\u0026auml;rz 2025]. Verf\u0026uuml;gbar unter: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eurotransplant.org/wp-content/uploads/2025/03/H5-ELAS-MELD-March-25th-2025-2.pdf\u003c/span\u003e\u003cspan address=\"https://www.eurotransplant.org/wp-content/uploads/2025/03/H5-ELAS-MELD-March-25th-2025-2.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGoudsmit BFJ, Putter H, Tushuizen ME, Vogelaar S, Pirenne J, Alwayn IPJ (2021) u. a. Refitting the Model for End-Stage Liver Disease for the Eurotransplant Region. Hepatol Juli 74(1):351\u0026ndash;363\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoo JJ, Chang JI, Moon JE, Sinn DH, Kim SG, Kim YS (2023) Validation of MELD 3.0 scoring system in East Asian patients with cirrhosis awaiting liver transplantation. Liver Transpl Oktober 29(10):1029\u0026ndash;1040\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLoosen SH, Bock HH, Hellmich M, Knoefel WT, Trautwein C, Keitel V Hospital Mortality and Current Trends in Liver Transplantation in Germany. Dtsch \u0026Auml;rztebl Int [Internet]. 26. Juli 2021 [zitiert 6. 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Aliment Pharmacol Ther Oktober 52(7):1204\u0026ndash;1213\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"langenbecks-archives-of-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"laos","sideBox":"Learn more about [Langenbeck's Archives of Surgery](http://link.springer.com/journal/423)","snPcode":"423","submissionUrl":"https://submission.nature.com/new-submission/423/3","title":"Langenbeck's Archives of Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Liver Transplantation, MELD, Scoring systems, mortality, equality","lastPublishedDoi":"10.21203/rs.3.rs-7219777/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7219777/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe Model for End-Stage Liver Disease (MELD) score, introduced in 2002, has since been refined. MELD 3.0, launched in 2023 in the United States, improves mortality prediction by incorporating sex and albumin. Since March 2025, Germany started to use reMELD-Na to improve prioritization of patients on the waiting list for liver transplantation (LT). This study compares the performance of original MELD, MELD-Na, MELD 3.0 with and without albumin, and reMELD-Na for patients waitlisted for LT in a large German transplant center.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis retrospective single-center study included 206 listed patients from 2017 to 2021 for LT. Reclassification patterns along with predictive accuracy for three-month survival and overall survival (OS) of five different MELD scores were assessed using Harrell\u0026rsquo;s c-index and integrated area under the curve (iAUC).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOver a median follow-up of 33.9 months, 100 patients (51.5%) underwent LT, with a post-transplant survival rate of 70%. Thirty-eight patients (18.4%) received LT and sixteen patients died within the first three months after listing. MELD 3.0 without albumin and reMELD-Na demonstrated the highest discrimination for three-month survival (iAUC 0.848 and 0.847; c-index 0.827 and 0.848, respectively). MELD 3.0 without albumin showed the best overall performance in predicting OS (iAUC and c-index 0.827), particularly in males, while reMELD-Na performed best in females (iAUC 0.707; c-index 0.705). Original MELD showed the poorest discrimination. Females with higher original MELD scores tended to receive even higher scores with MELD 3.0.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eThis is the first German study to validate reMELD-Na and MELD 3.0, showing superior predictive performance over original MELD. MELD 3.0 may better reflect disease severity in women at advanced stages due to more upward reclassification.\u003c/p\u003e","manuscriptTitle":"Performances and Validation of MELD 3.0 and ReMELD-Na Scoring Systems: A German Clinical Cohort Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-31 16:23:32","doi":"10.21203/rs.3.rs-7219777/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-09T07:52:56+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-08T19:51:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"135614262476980316384498321315850764479","date":"2025-08-05T11:54:50+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-29T06:34:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-29T06:28:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-28T07:07:13+00:00","index":"","fulltext":""},{"type":"submitted","content":"Langenbeck's Archives of Surgery","date":"2025-07-26T08:30:14+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"langenbecks-archives-of-surgery","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"laos","sideBox":"Learn more about [Langenbeck's Archives of Surgery](http://link.springer.com/journal/423)","snPcode":"423","submissionUrl":"https://submission.nature.com/new-submission/423/3","title":"Langenbeck's Archives of Surgery","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"3e99dd2f-400e-47ee-9be3-dc0541fe44ff","owner":[],"postedDate":"July 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T16:07:35+00:00","versionOfRecord":{"articleIdentity":"rs-7219777","link":"https://doi.org/10.1007/s00423-025-03846-x","journal":{"identity":"langenbecks-archives-of-surgery","isVorOnly":false,"title":"Langenbeck's Archives of Surgery"},"publishedOn":"2025-09-25 15:57:36","publishedOnDateReadable":"September 25th, 2025"},"versionCreatedAt":"2025-07-31 16:23:32","video":"","vorDoi":"10.1007/s00423-025-03846-x","vorDoiUrl":"https://doi.org/10.1007/s00423-025-03846-x","workflowStages":[]},"version":"v1","identity":"rs-7219777","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7219777","identity":"rs-7219777","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}