Predictors of the cost of liver transplantation
Article first published online: 30 DEC 2003
Copyright © 1998 American Association for the Study of Liver Diseases
Liver Transplantation and Surgery
Volume 4, Issue 2, pages 170–176, March 1998
How to Cite
Brown, R. S., Lake, J. R., Ascher, N. L., Emond, J. C. and Roberts, J. P. (1998), Predictors of the cost of liver transplantation. Liver Transpl, 4: 170–176. doi: 10.1002/lt.500040211
- Issue published online: 30 DEC 2003
- Article first published online: 30 DEC 2003
- Glaxo Institute for Digestive Health Healthcare Advancement Award (RSB)
Background. Orthotopic liver transplantation (OLT) is a highly effective but costly therapy for end-stage liver disease. However, there are limited data on the demographic and clinical variables that affect cost. We undertook a preliminary study using multiple regression techniques to analyze factors that influence the cost of OLT.
Methods. Patient and demographic data, including laboratory values and charges for all liver transplantations performed between June 1992 and June 1993 were analyzed (n = 111). Linear regression with standard and log-transformed values was performed by using STATA software (Stata Corporation College Station, TX). Independent variables included in the analyses were age, sex, United Network for Organ Sharing (UNOS) status, primary versus retransplantation, liver-kidney transplantation, and laboratory parameters of both liver (aspartate aminotransferase, AST; alkaline phosphatase; bilirubin; albumin; and prothrombin time) and kidney (blood urea nitrogen, BUN; creatinine) function. An F-to-remove strategy was employed with a significance level set at P = .05.
Results. The full model with 12 variables explained 37% of the total variation in charges. When one excludes variables that did not have a significant impact on cost, the remaining significant variables were BUN and UNOS status 1. The final model was
Charges (US$) = 3,407 × BUN
+ 74,474 × status 1 + 102,662
This model accounted for 29% of the total variability with BUN accounting for the vast majority (26%).
Conclusions. Renal function is the most important predictor of cost of OLT (P < .001). UNOS status 1 further increases cost, but other hospitalized patients have similar costs when one controls for other clinical variables. The degree of liver impairment is less important in predicting cost.