Predicting outcome after liver transplantation: utility of the model for end-stage liver disease and a newly derived discrimination function.
BACKGROUND
The Model for End-Stage Liver Disease (MELD) has been found to accurately predict pretransplant mortality and is a valuable system for ranking patients in greatest need of liver transplantation. It is unknown whether a higher MELD score also predicts decreased posttransplant survival.


METHODS
We examined a cohort of patients from the United Network for Organ Sharing (UNOS) database for whom the critical pretransplant recipient values needed to calculate the MELD score were available (international normalized ratio of prothrombin time, total bilirubin, and creatinine). In these 2,565 patients, we analyzed whether the MELD score predicted graft and patient survival and length of posttransplant hospitalization.


RESULTS
In contrast with its ability to predict survival in patients with chronic liver disease awaiting liver transplant, the MELD score was found to be poor at predicting posttransplant outcome except for patients with the highest 20% of MELD scores. We developed a model with four variables not included in MELD that had greater ability to predict 3-month posttransplant patient survival, with a c-statistic of 0.65, compared with 0.54 for the pretransplant MELD score. These pretransplant variables were recipient age, mechanical ventilation, dialysis, and retransplantation. Recipients with any two of the three latter variables showed a markedly diminished posttransplant survival rate.


CONCLUSIONS
The MELD score is a relatively poor predictor of posttransplant outcome. In contrast, a model based on four pretransplant variables (recipient age, mechanical ventilation, dialysis, and retransplantation) had a better ability to predict outcome. Our results support the use of MELD for liver allocation and indicate that statistical modeling, such as reported in this article, can be used to identify futile cases in which expected outcome is too poor to justify transplantation.
Desai
Niraj M
NM
Department of Surgery, Washington University School of Medicine, St. Louis, Missouri, USA.
Mange
Kevin C
KC
Crawford
Michael D
MD
Abt
Peter L
PL
Frank
Adam M
AM
Markmann
Joseph W
JW
Velidedeoglu
Ergun
E
Chapman
William C
WC
Markmann
James F
JF
D000328:Adult; D015331:Cohort Studies; D016208:Databases, Factual; D016002:Discriminant Analysis; D005260:Female; D006085:Graft Survival; D006295:Health Resources / Q000706:statistics & numerical data; D006801:Humans; D017093:Liver Failure / Q000601:surgery*; D016031:Liver Transplantation; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D011182:Postoperative Care; D011379:Prognosis; D016016:Proportional Hazards Models; D016019:Survival Analysis; D016896:Treatment Outcome