Predicting Survival After Liver Transplant
Currently, nearly 18 thousand patients are awaiting liver transplants, but because organs are scarce, only about 6,000 are transplanted each year. There are no universally accepted criteria for liver donors. In addition, the importance of various recipient characteristics to post- transplant survival isn’t fully understood.
George Ioannou, M.D., M.S., of the Veterans Affairs Puget Sound Health Care System in Seattle sought to identify donor and recipient characteristics that are important predictors of graft survival following liver transplantation. He then used these predictors to develop and validate a survival model.
Using information provided by the United Network for Organ Sharing, Ioannou identified all patients who had a liver transplant between 1994 and 2003. For his study, he did not include patients who had donors under age 10 or over age 75, living donors, split-liver donors, non-heart-beating donors, or donors with serum sodium concentration greater than 170 mmoles/L. He also excluded patients with multiple organ transplants, previous liver transplants and incomplete information.
For the 20,301 patients who remained, including 6,477 with hepatitis C (HCV), he used statistical models to examine the relationship between donor and recipient characteristics and survival after transplant. He then created two models that predict survival after liver transplant – one for patients without HCV and one for those with HCV. He validated the models using data from patients not included in their derivation.
Ioannou found that the donor age, cold ischemia time, recipient MELD score, and cause of liver disease have the greatest impact on survival. However, the best model for patients without HCV included donor age, cold ischemia time, gender, race/ethnicity, recipient age, BMI, MELD score, status at time of transplantation, diabetes mellitus, cause of liver disease, and serum albumin. For patients with HCV, the best model included the same donor characteristics, and all recipient characteristics except cause of liver disease and serum albumin.
“Ultimately,” Ioannou writes, “risk scores and predicted
survivals determined from such models may be an objective
way to assess the risk of a given liver donor, recipient, or
donor/recipient combination.” Such models could improve the
fairness of organ distribution. For example, he suggests,
“if two donors are expected to be available at approximately
the same time, it would be more equitable for the recipient
with worse predicted post-transplant survival to receive the
donor with the better predicted survival and vice versa
since that would make the post transplant survival of the
two recipients more similar.”
Article: “Development and Validation of a Model Predicting
Graft Survival After Liver Transplantation” by George N.
Ioannou, Liver Transplantation; November 2006 (DOI:
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