Dataset Information

GEMA-Na and MELD 3.0 severity scores to address sex disparities for accessing liver transplantation: a nationwide retrospective cohort study.

ABSTRACT:

Background

The Gender-Equity Model for liver Allocation corrected by serum sodium (GEMA-Na) and the Model for End-stage Liver Disease 3.0 (MELD 3.0) could amend sex disparities for accessing liver transplantation (LT). We aimed to assess these inequities in Spain and to compare the performance of GEMA-Na and MELD 3.0.

Methods

Nationwide cohort study including adult patients listed for a first elective LT (January 2016-December 2021). The primary outcome was mortality or delisting for sickness within the first 90 days. Independent predictors of the primary outcome were evaluated using multivariate Cox's regression with adjusted relative risks (RR) and 95% confidence intervals (95% CI). The discrimination of GEMA-Na and MELD 3.0was assessed using Harrell c-statistics (Hc).

Findings

The study included 6071 patients (4697 men and 1374 women). Mortality or delisting for clinical deterioration occurred in 286 patients at 90 days (4.7%). Women had reduced access to LT (83.7% vs. 85.9%; p = 0.037) and increased risk of mortality or delisting for sickness at 90 days (adjusted RR = 1.57 [95% CI 1.09-2.28]; p = 0.017). Female sex remained as an independent risk factor when using MELD or MELD-Na but lost its significance in the presence of GEMA-Na or MELD 3.0. Among patients included for reasons other than tumours (n = 3606; 59.4%), GEMA-Na had Hc = 0.753 (95% CI 0.715-0.792), which was higher than MELD 3.0 (Hc = 0.726 [95% CI 0.686-0.767; p = 0.001), showing both models adequate calibration.

Interpretation

GEMA-Na and MELD 3.0 might correct sex disparities for accessing LT, but GEMA-Na provides more accurate predictions of waiting list outcomes and could be considered the standard of care for waiting list prioritization.

Funding

Instituto de Salud Carlos III, Agencia Estatal de Investigación (Spain), and European Union.

SUBMITTER: Rodriguez-Peralvarez ML

PROVIDER: S-EPMC11304699 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

GEMA-Na and MELD 3.0 severity scores to address sex disparities for accessing liver transplantation: a nationwide retrospective cohort study.

Rodríguez-Perálvarez Manuel Luis ML de la Rosa Gloria G Gómez-Orellana Antonio Manuel AM Aguilera María Victoria MV Pascual Vicente Teresa T Pereira Sheila S Ortiz María Luisa ML Pagano Giulia G Suarez Francisco F González Grande Rocío R Cachero Alba A Tomé Santiago S Barreales Mónica M Martín Mateos Rosa R Pascual Sonia S Romero Mario M Bilbao Itxarone I Alonso Martín Carmen C Otón Elena E González Diéguez Luisa L Espinosa María Dolores MD Arias Milla Ana A Blanco Fernández Gerardo G Lorente Sara S Cuadrado Lavín Antonio A Redín García Amaya A Sánchez Cano Clara C Cepeda-Franco Carmen C Pons José Antonio JA Colmenero Jordi J Guijo-Rubio David D Otero Alejandra A Amador Navarrete Alberto A Romero Moreno Sarai S Rodríguez Soler María M Hervás Martínez César C Gastaca Mikel M

EClinicalMedicine 20240718

<h4>Background</h4>The Gender-Equity Model for liver Allocation corrected by serum sodium (GEMA-Na) and the Model for End-stage Liver Disease 3.0 (MELD 3.0) could amend sex disparities for accessing liver transplantation (LT). We aimed to assess these inequities in Spain and to compare the performance of GEMA-Na and MELD 3.0.<h4>Methods</h4>Nationwide cohort study including adult patients listed for a first elective LT (January 2016-December 2021). The primary outcome was mortality or delisting ...[more]

PMID: 39114271

Similar Datasets

Project description:BackgroundControversy exists regarding the best predictive model of liver transplant waiting list (WL) mortality. Models for end-stage liver disease-glomerular filtration rate assessment in liver disease (MELD-GRAIL) and MELD-GRAIL-Na were recently described to provide better prognostication, particularly in females. We evaluated the performance of these scores compared to MELD and MELD-Na.MethodsConsecutive patients with cirrhosis waitlisted for liver transplant from 1998 to 2017 were examined in this single-center study. The primary outcome was 90-d WL mortality. MELD, MELD-Na, MELD-GRAIL, and MELD-GRAIL-Na at the time of WL registration were compared. Model discrimination was assessed with area under the receiver operating characteristic curves and Harrell's C-index after fitting Cox models. Model calibration was examined with Grønnesby and Borgan's modification of the Hosmer-Lemeshow formula and by comparing predicted/observed outcomes across model strata.ResultsThe study population comprised 1108 patients with a median age of 53.5 (interquartile range 48-59) y and male predominance (74.9%). All models had excellent areas under the receiver operating characteristic curves for the primary outcome (MELD 0.89, MELD-Na 0.91, MELD-GRAIL 0.89, MELD-GRAIL-Na 0.89; all comparisons P > 0.05). Youden index cutoffs for 90-d mortality were as follows: MELD, 19; MELD-Na, 22; MELD-GRAIL, 18; and MELD-GRAIL-Na, 17. Variables associated with 90-d mortality on multivariable Cox regression were sodium, bilirubin, creatinine, and international normalized ratio. There were no differences in model discrimination using Harrell's C-index. All models were well calibrated; however, divergence between observed and predicted mortality was noted with scores ≥25.ConclusionThere were no demonstrable differences in discrimination or calibration of GRAIL-based models compared with MELD or MELD-Na in our cohort. This suggests that GRAIL-based models may not have meaningful improvements in discriminatory ability when applied to other settings.

Project description:In the era of the "sickest first" policy, patients with very high model for end-stage liver disease (MELD) scores have been increasingly admitted to the intensive care unit with the expectation that they will receive a liver transplant (LT) in the absence of improvement on supportive therapies. Such patients are often admitted in a context of acute-on-chronic liver failure with extrahepatic failures. Sequential assessment of scores or classification based on organ failures within the first days after admission help to stratify the risk of mortality in this population. Although the prognosis of severely ill cirrhotic patients has recently improved, transplant-free mortality remains high. LT is still the only curative treatment in this population. Yet, the increased relative scarcity of graft resource must be considered alongside the increased risk of losing a graft in the initial postoperative period when performing LT in "too sick to transplant" patients. Variables associated with poor immediate post-LT outcomes have been identified in large studies. Despite this, the performance of scores based on these variables is still insufficient. Consideration of a patient's comorbidities and frailty is an appealing predictive approach in this population that has proven of great value in many other diseases. So far, local expertise remains the last safeguard to LT. Using this expertise, data are accumulating on favourable post-LT outcomes in very high MELD populations, particularly when LT is performed in a situation of stabilization/improvement of organ failures in selected candidates. The absence of "definitive" contraindications and the control of "dynamic" contraindications allow a "transplantation window" to be defined. This window must be identified swiftly after admission given the poor short-term survival of patients with very high MELD scores. In the absence of any prospect of LT, withdrawal of care could be discussed to ensure respect of patient life, dignity and wishes.

Dataset Information

GEMA-Na and MELD 3.0 severity scores to address sex disparities for accessing liver transplantation: a nationwide retrospective cohort study.

Background

Methods

Findings

Interpretation

Funding

Publications

GEMA-Na and MELD 3.0 severity scores to address sex disparities for accessing liver transplantation: a nationwide retrospective cohort study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets