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Models Predict Liver Graft Survival


SEATTLE -- Four donor-specific characteristics and nine recipient characteristics can help predict the odds for the success of a liver transplant, according to a surgeon here.

SEATTLE, Nov. 6 -- Four donor-specific characteristics and nine recipient characteristics can help predict the odds for the success of a liver transplant, according to a surgeon here.

With certain variations, the approach can be used for recipients with or without hepatitis C virus infection, reported George N. Ioannou, M.D., M.S., of the University of Washington and Veterans Affairs Puget Sound Health Care System here in the November issue of Liver Transplantation.

The approach may allow for more accurate assessment of post-transplant survival than the MELD (Model End-Stage Liver Disease) score used by the United Network for Organ Sharing , and could help in allocation decisions, particularly in the case of marginal donors or recipients, Dr. Ioannou wrote.

"For instance, a 40-year-old non-diabetic woman with primary biliary cirrhosis and a MELD score of 30 would be expected to have a much better post-transplant survival than a 65-year-old diabetic man with hepatitis C and a MELD score of 30, even though both have a similar mortality without transplant, since they have the same MELD score" he wrote.

"If two donors are expected to be available at approximately the same time, it would be more equitable for the recipient with worse predicted posttransplant survival (as determined by the models presented here) to receive the donor with better predicted survival and vice versa, since that would make the posttransplant survival of the two recipients more similar," he continued.

Dr. Ioannou used data from UNOS on 20,301 people who underwent liver transplantation in the United States from 1994 to 2003 to create two models -- one for donor recipients without HCV infection, and one for recipients with hepatitis C.

He used proportional-hazards regression to identify the donor and recipient characteristics that best predicted survival, and incorporated those characteristics into a multivariate model.

He also validated the model with a data-splitting approach, in which survival as predicted by the model was compared with the observed survival among patients whose data were not used to create the model.

He found that in the multivariate analysis, four specific characteristics of the donor had a bearing on survival of the recipients who did not have HCV:

  • Donor age
  • Cold ischemia time of the donor graft
  • Donor gender
  • Race/ethnicity

For example, livers from African American and female donors were associated with significantly higher risk of graft failure, as were livers with cold ischemia time longer than 8.8 hours.

Nine characteristics of the recipients were also associated with graft survival in the multivariate model:

  • Age
  • Body mass index
  • MELD score
  • Status at time of transplantation
  • Gender
  • Race/ethnicity
  • Diabetes
  • Cause of liver disease
  • Serum albumin

Thus, older and heavier donors, men, African Americans, patients with diabetes, and patients at the top of the UNOS urgency list were more likely in the model to have graft failure.

For patients with concurrent HCV infection, the donor factors were the same, as were the recipient factors, except that serum albumin and cause of liver disease were not significant predictors, and MELD score was a categorical rather than continuous variable.

"The models?show that a large proportion of post-liver transplant mortality is determined before the liver transplantation has actually occurred by pretransplant characteristics of the donor and recipient," Dr. Ioannou wrote. "The models make explicit in a multivariate analysis which donor and recipient characteristics have the greatest impact on survival. Although all the variables included in the model are important, donor age, cold ischemia time, recipient MELD score, and cause of liver disease have the greatest impact on survival."

Dr. Ioanou noted that the predictive model for patients without HCV infection may be less effective at predicting surviving in some patients, such as those with hepatocellular carcinoma or unspecified "other" liver disease compared with those who have, for example, primary biliary cirrhosis or alcoholic liver disease.

"This problem can be overcome by developing survival models specific for each major cause of liver disease, just as has been done in this paper for HCV, although this approach was avoided in the current paper for the sake of simplicity," he wrote.

He concluded, "It is hoped that the analyses presented here will serve as a starting point for subsequent investigators to improve upon the presented models. Ultimately, 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."

In an accompanying editorial, Ignazio R. Marino, M.D., of Jefferson Medical College in Philadelphia discussed some of the ethical issues facing transplant surgeons as they begin to debate the use of models to match donors and recipients. He believes that it is unfair to leave these decisions up to single transplant centers and that criteria should be developed and applied uniformly.

He concluded that we might not be ready to match donors and recipients yet but that "a large prospective stratification of transplantation candidates at the time of surgery should be implemented. Comparing results across different groups will then help optimize allocation criteria and define when a prospective donor should not be used for a prospective recipient."

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