Prognostic Abilities and Quality Assessment of Models for the Prediction of 90-Day Mortality in Liver Transplant Waiting List Patients

Overview

Journal PLoS One

Specialties General Medicine
Science

Date 2017 Jan 28

PMID 28129338

Citations 1

Authors

Ricardo Salinas Saldana

Harald Schrem

Marc Barthold

Alexander Kaltenborn

Affiliations

Soon will be listed here.

Abstract

Background: Model of end-stage liver disease (MELD)-score and diverse variants are widely used for prognosis on liver transplant waiting-lists.

Methods: 818 consecutive patients on the liver transplant waiting-list included to calculate the MELD, MESO Index, MELD-Na, UKELD, iMELD, refitMELD, refitMELD-Na, upMELD and PELD-scores. Prognostic abilities for 90-day mortality were investigated applying Receiver-operating-characteristic-curve analysis. Independent risk factors for 90-day mortality were identified with multivariable binary logistic regression modelling. Methodological quality of the underlying development studies was assessed with a systematic assessment tool.

Results: 74 patients (9%) died on the liver transplant waiting list within 90 days after listing. All but one scores, refitMELD-Na, had acceptable prognostic performance with areas under the ROC-curves (AUROCs)>0.700. The iMELD performed best (AUROC = 0.798). In pediatric cases, the PELD-score just failed to reach the acceptable threshold with an AUROC = 0.699. All scores reached a mean quality score of 72.3%. Highest quality scores could be achieved by the UKELD and PELD-scores. Studies specifically lack statistical validity and model evaluation.

Conclusions: Inferior quality assessment of prognostic models does not necessarily imply inferior prognostic abilities. The iMELD might be a more reliable tool representing urgency of transplantation than the MELD-score. PELD-score is assumedly not accurate enough to allow graft allocation decision in pediatric liver transplantation.

Citing Articles

The Development and Appraisal of MELD 3.0 in Liver Diseases: Good Things Never Come Easy.

Guo G, Yang W, Li J, Yang Z, Liang J, Sun C J Clin Transl Hepatol. 2025; 13(1):62-68.

PMID: 39801783 PMC: 11712091. DOI: 10.14218/JCTH.2024.00303.

Predictors of Death in the Liver Transplantation Adult Candidates: An Artificial Neural Networks and Support Vector Machine Hybrid-Based Cohort Study.

Bagheri Lankarani K, Honarvar B, Shafi Pour F, Bagherpour M, Erjaee A, Rouhezamin M J Biomed Phys Eng. 2022; 12(6):591-598.

PMID: 36569570 PMC: 9759643. DOI: 10.31661/jbpe.v0i0.2010-1212.

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