Pretransplant Model to Predict Posttransplant Survival in Liver Transplant Patients

Abstract

ObjectiveTo develop a prognostic model that determines patient survival outcomes after orthotopic liver transplantation (OLT) using readily available pretransplant variables. Summary Background DataThe current liver organ allocation system strongly favors organ distribution to critically ill recipients who exhibit poor survival outcomes following OLT. A severely limited organ resource, increasing waiting list deaths, and rising numbers of critically ill recipients mandate an organ allocation system that balances disease severity with survival outcomes. Such goals can be realized only through the development of prognostic models that predict survival following OLT. MethodsVariables that may affect patient survival following OLT were analyzed in hepatitis C (HCV) recipients at the authors' center, since HCV is the most common indication for OLT. The resulting patient survival model was examined and refined in HCV and non-HCV patients in the United Network for Organ Sharing (UNOS) database. Kaplan-Meier methods, univariate comparisons, and multivariate Cox proportional hazard regression were employed for analyses. ResultsVariables identified by multivariate analysis as independent predictors for patient survival following primary transplantation of adult HCV recipients in the last 10 years at the authors' center were entered into a prognostic survival model to predict patient survival. Accordingly, mortality was predicted by 0.0293 (recipient age) ϩ 1.085 (log 10 recipient creatinine) ϩ 0.289 (donor female gender) ϩ 0.675 urgent UNOS -1.612 (log 10 recipient creatinine times urgent UNOS). The above variables, in addition to donor age, total bilirubin, prothrombin time (PT), retransplantation, and warm and cold ischemia times, were applied to the UNOS database. Of the 46,942 patients transplanted over the last 10 years, 25,772 patients had complete data sets. An eight-factor model that accurately predicted survival was derived. Accordingly, the mortality index posttransplantation ϭ 0.0084 donor age ϩ 0.019 recipient age ϩ 0.816 log creatinine ϩ 0.0044 warm ischemia (in minutes) ϩ 0.659 (if second transplant) ϩ 0.10 log bilirubin ϩ 0.0087 PT ϩ 0.01 cold ischemia (in hours). Thus, this model is applicable to first or second liver transplants. Patient survival rates based on model-predicted risk scores for death and observed posttransplant survival rates were similar. Additionally, the model accurately predicted survival outcomes for HCV and non-HCV patients. ConclusionsPosttransplant patient survival can be accurately predicted based on eight straightforward factors. The balanced application of a model for liver transplant survival estimate, in addition to disease severity, as estimated by the model for end-stage liver disease, would markedly improve survival outcomes and maximize patients' benefits following OLT.

Retransplantation of liver: Primary graft nonfunction and hepatitis C virus are associated with worse outcome

“…In the absence of firm guidelines, this is likely to be interpreted as discriminatory by the lay public or outside observers. Many models and methods of risk assessment have been described to identify patients most likely to benefit from re-LT. 13,19,20 These models have shown that patient preoperative ventilatory status, serum creatinine level, and bilirubin level are among the major determinants of postoperative survival. A model described by Rosen et al 19 identified age, bilirubin level, creatinine level, UNOS status, and cause of graft failure as independent predictors of post-re-LT survival.…”

“…Another model identified donor and recipient age, creatinine level, bilirubin level, and warm and cold ischemia times as independent variables affecting survival. 20 Azoulay et al 2 found urgency of transplantation, age, and creatinine level to be independent variables affecting survival after re-LT. We perhaps could add HCV infection and transplantation for PGNF to the list of poor predictors of survival after re-LT.…”

Poor survival after liver retransplantation: Is hepatitis C to blame?

“…Prognostic models have been developed recently, but these studies predate the Model for End-Stage Liver Disease (MELD) scoring system, which incorporates many of these factors. [24][25][26] In the era of MELD scoring, one can speculate that patients undergoing OLT are sicker and have more risk factors for poor outcome than those who underwent OLT under the UNOS status scoring system.Considerable concern and debate surround the topic of re-OLT in general and specifically in HCV-infected patients. Aims of the study are to determine the current prevalence of HCV-related liver disease in the re-OLT population, survival after re-OLT for patients with HCV versus other diseases, and whether pre-OLT MELD scores are predictive of survival outcome.…”

“…Prognostic models have been developed recently, but these studies predate the Model for End-Stage Liver Disease (MELD) scoring system, which incorporates many of these factors. [24][25][26] In the era of MELD scoring, one can speculate that patients undergoing OLT are sicker and have more risk factors for poor outcome than those who underwent OLT under the UNOS status scoring system.…”

The Year in Review – ATC 2002

“…Two groups, UCLA (24) and University of Pennsylvania (25), presented data which challenged the potential utility of this system, and suggested that the MELD score may actually predict postoperative mortality. The merits of this MELD and PELD will undoubtedly be the focus of future analysis pending the accumulation of data in the first few years of this new allocation scheme.…”