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A model to predict survival following liver retransplantation
Author(s) -
Rosen Hugo R.,
Madden Joseph P.,
Martin Paul
Publication year - 1999
Publication title -
hepatology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.488
H-Index - 361
eISSN - 1527-3350
pISSN - 0270-9139
DOI - 10.1002/hep.510290221
Subject(s) - medicine , liver transplantation , proportional hazards model , cohort , creatinine , univariate , transplantation , economic shortage , multivariate analysis , multivariate statistics , confidence interval , surgery , statistics , linguistics , philosophy , mathematics , government (linguistics)
In the current era of critical‐organ shortage, one of the most controversial questions facing transplantation teams is whether hepatic retransplantation, which has historically been associated with increased resource utilization and diminished survival, should be offered to a patient whose first allograft is failing. Retransplantation effectively denies access to orthotopic liver transplantation (OLT) to another candidate and further depletes an already‐limited organ supply. The study group was comprised of 1,356 adults undergoing hepatic retransplantation in the United States between 1990 and 1996 as reported to the United Network for Organ Sharing (UNOS). We analyzed numerous donor and recipient variables and created Cox proportional‐hazards models on 900 randomly chosen patients, validating the results on the remaining cohort. Five variables consistently provided significant predictive power and made up the final model: age, bilirubin, creatinine, UNOS status, and cause of graft failure. Although both hepatitis C seropositivity and donor age were significant by univariate and multivariate analyses, neither contributed independently to the estimation of prognosis when added to the final model. The final model was highly predictive of survival (whole model χ 2 = 139.63). The risk scores for individual patients were calculated, and patients were assigned into low‐, medium‐, and high‐risk groups ( P < .00001). The low degree of uncertainty in the probability estimates as reflected by confidence intervals, even in our high‐risk patients, underscores the applicability of our model as an adjunct to clinical judgment. We have developed and validated a model that uses five readily accessible “bedside” variables to accurately predict survival in patients undergoing liver retransplantation.