A model to predict severe HCV-related disease following liver transplantation

Marina Berenguer, Jeffrey Crippin, Robert Gish, Nathan Bass, Alan Bostrom, George Netto, Judy Alonzo, Richard Garcia-Kennedy, Jose Miguel Rayón, Teresa L. Wright

Research output: Contribution to journalArticle

Abstract

Post-transplantation recurrence is increasing in patients with HCV. Early antiviral therapy may be of benefit in this setting. Thus, accurate and early prediction of progression may help select candidates for treatment. We developed a model based on pre- and/or early post-transplantation variables, which may predict progression to severe disease. Clinical and histologic outcomes were assessed in 554 liver recipients. A total of 1,353 biopsy specimens obtained after 1 year (median of 2 biopsies per patient; range, 1-8) were scored. Two outcome measures were used: cumulative probability of developing severe disease (fibrosis 3 and 4) within 5 years and actual progression to severe disease in 2 years. We used Cox proportional hazard survival analysis for the whole cohort. Predictors analyzed included HCV genotype and recipient, donor, and transplant-related variables. The cumulative risk of progressing to fibrosis 3 and 4 was significantly greater in patients transplanted recently (P <.001) and was present in all centers. Factors increasing this risk were genotype, induction with mycophenolate, donor age, short course of azathioprine, and prednisone (<12 months). To create a model of prediction, 285 patients with 2-year follow-up were used to create a logistic regression analysis. The estimated probability of being at high risk for severe disease was calculated from a formula that included donor age and recipient therapy as critical variables. In conclusion, we have developed a model that uses early post-transplantation variables to predict severe HCV recurrence. Accuracy of the model is not perfect (c-statistic 0.80), probably reflecting the complexity of HCV in the liver transplant setting.

Original languageEnglish (US)
Pages (from-to)34-41
Number of pages8
JournalHepatology
Volume38
Issue number1
DOIs
StatePublished - Jul 1 2003
Externally publishedYes

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Liver Transplantation
Transplantation
Tissue Donors
Fibrosis
Genotype
Biopsy
Recurrence
Liver
Azathioprine
Survival Analysis
Secondary Prevention
Prednisone
Antiviral Agents
Logistic Models
Regression Analysis
Outcome Assessment (Health Care)
Transplants
Therapeutics

ASJC Scopus subject areas

  • Hepatology

Cite this

Berenguer, M., Crippin, J., Gish, R., Bass, N., Bostrom, A., Netto, G., ... Wright, T. L. (2003). A model to predict severe HCV-related disease following liver transplantation. Hepatology, 38(1), 34-41. https://doi.org/10.1053/jhep.2003.50278

A model to predict severe HCV-related disease following liver transplantation. / Berenguer, Marina; Crippin, Jeffrey; Gish, Robert; Bass, Nathan; Bostrom, Alan; Netto, George; Alonzo, Judy; Garcia-Kennedy, Richard; Rayón, Jose Miguel; Wright, Teresa L.

In: Hepatology, Vol. 38, No. 1, 01.07.2003, p. 34-41.

Research output: Contribution to journalArticle

Berenguer, M, Crippin, J, Gish, R, Bass, N, Bostrom, A, Netto, G, Alonzo, J, Garcia-Kennedy, R, Rayón, JM & Wright, TL 2003, 'A model to predict severe HCV-related disease following liver transplantation', Hepatology, vol. 38, no. 1, pp. 34-41. https://doi.org/10.1053/jhep.2003.50278
Berenguer M, Crippin J, Gish R, Bass N, Bostrom A, Netto G et al. A model to predict severe HCV-related disease following liver transplantation. Hepatology. 2003 Jul 1;38(1):34-41. https://doi.org/10.1053/jhep.2003.50278
Berenguer, Marina ; Crippin, Jeffrey ; Gish, Robert ; Bass, Nathan ; Bostrom, Alan ; Netto, George ; Alonzo, Judy ; Garcia-Kennedy, Richard ; Rayón, Jose Miguel ; Wright, Teresa L. / A model to predict severe HCV-related disease following liver transplantation. In: Hepatology. 2003 ; Vol. 38, No. 1. pp. 34-41.
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