Statistics – Machine Learning
Scientific paper
2008-09-23
Statistics
Machine Learning
Scientific paper
Background: Many authors have described MELD as a predictor of short-term mortality in the liver transplantation waiting list. However MELD score accuracy to predict long term mortality has not been statistically evaluated. Objective: The aim of this study is to analyze the MELD score as well as other variables as a predictor of long-term mortality using a new model: the Survival Tree analysis. Study Design and Setting: The variables obtained at the time of liver transplantation list enrollment and considered in this study are: sex, age, blood type, body mass index, etiology of liver disease, hepatocellular carcinoma, waiting time for transplant and MELD. Mortality on the waiting list is the outcome. Exclusion, transplantation or still in the transplantation list at the end of the study are censored data. Results: The graphical representation of the survival trees showed that the most statistically significant cut off is related to MELD score at point 16. Conclusion: The results are compatible with the cut off point of MELD indicated in the clinical literature.
Basto Samanta Teixeira
Braganca Pereira Basilio de
do Nascimento Emilia Matos
Filho Joaquim Ribeiro
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