Abstract |
The aim of our study was to develop simple and highly effective scores to estimate prognosis at 1 year for patients with parenchymal cirrhosis and to define the optimum time for liver transplantation with the same degree of accuracy as the prognosis estimation for primary biliary cirrhosis. The prognostic value of 19 variables was studied retrospectively in 91 patients with parenchymal cirrhosis using multivariate analysis and logistic regression. The best prognostic index was obtained with two independent variables: ascites and aminopyrine breath test. Although the receiver operating characteristic (ROC) curve for these two variables was better than the ROC curve for Pugh score, the percentage of correct prediction was excellent for both indices: 92% and 87%, respectively. The critical cut-off value of the Pugh score was 8.8. The prognostic value of a Pugh score < or = 8 or > 8 was confirmed in a prospective study of 145 cirrhotic patients with 78% correct prediction. During this period, 21 patients with parenchymal cirrhosis received transplants with a preoperative Pugh score of 9.5 +/- 2.0 (mean +/- SEM) and 60% 1- and 2-year survival. In conclusion in parenchymal cirrhosis, a Pugh score > 8 indicates a poor prognosis at 1 year. This is a simple, easy and highly effective tool to define the optimal time for liver transplantation in this category of patients.
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Authors | M Adler, N Bourgeois, J van de Stadt, M Gelin |
Journal | Transplant international : official journal of the European Society for Organ Transplantation
(Transpl Int)
Vol. 5 Suppl 1
Pg. S175-8
( 1992)
ISSN: 0934-0874 [Print] Switzerland |
PMID | 14621768
(Publication Type: Journal Article)
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Topics |
- Follow-Up Studies
- Humans
- Liver
(pathology)
- Liver Cirrhosis
(classification, surgery)
- Liver Cirrhosis, Alcoholic
(surgery)
- Liver Function Tests
- Liver Transplantation
(mortality, statistics & numerical data)
- Models, Statistical
- Multivariate Analysis
- Patient Selection
- ROC Curve
- Regression Analysis
- Retrospective Studies
- Survival Analysis
- Time Factors
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