Related Experiment Video
Updated: Dec 24, 2025
Technique of Porcine Liver Procurement and Orthotopic Transplantation using an Active Porto-Caval Shunt
Published on: May 7, 2015
Platelets level variability during the first year after liver transplantation in the risk prediction model for
Wojciech Jarmulski1, Alicja Wieczorkowska1, Mariusz Trzaska1
1Polish-Japanese Academy of Information Technology, 86 Koszykowa St, Warsaw, Poland.
Introduction And Objectives:
Many scoring systems in liver diseases use static values of liver function parameters. These parameters may change significantly in liver transplant (LTx) recipients over time due to various processes. The study was aimed at building a new model for survival prediction after LTx based on variability of selected parameters.
Materials And Methods:
The study included 450 LTx recipients who survived a minimum one year after transplantation. We analyzed liver enzymes and hematology parameters static values and their variability during the first year after transplantation. Modeling patients' survival was performed using Cox regression. Various sets of parameters (both static and variability and trends values) were tested to predict survival in our study group. Models' performance was measured using the concordance index.
Results:
The single predictors of the patients survival were the static values of AST with C-index 0.706 (0.5883-0.7494), ALT 0.6102 (0.4843-0.6857) and bilirubin 0.6224 (0.5537-0.6695). High prediction scores were observed for variability in creatinine 0.6023 (0.5409-0.6451), PLT 0.6350 (0.5491-0.7043), RBC 0.5689 (0.5065-0.6213) and WBC 0.6506 (0.5095-0.7124). Our best-fitted and proposed model for patients survival after LTx has C-index 0.8273 (IQR 0.7767-0.8649). The model uses the following indicators for mortality prediction: the static value of AST, variability measure of PLT and trend measures of WBC and PLT.
Conclusions:
Adding variability and trend measures increases predictive accuracy in modeling patients survival after LTx. We propose a high-accuracy survival model in which variability and trend of PLT measures in the first year after transplantation are strong predictors of long-term mortality.
