Renal Perfusion Model: Outcome Predictions

Abstract

The Banner University Medical Center's (BUMC) renal transplant program relies on the LifePort Kidney Transporter to optimize marginal kidney organs via hypothermic machine perfusion (HMP) prior to transplantation. Hemodynamic parameters produced by the device followed over the duration of support, combined with clinical experience, guide decisions in determining the acceptability of a donor kidney for implantation. Thus far, statistical evidence supporting ideal parameters remain undefined. The purpose of this study is to create a logistic model that will ascertain the post-implant sustainability of LifePort® supported kidneys and predict clinical outcomes. My hypothesis is that the statistical models constructed based on retrospective LifePort® parameters and clinical outcome data will successfully predict donor organ vascular health for transplantation and the optimal support duration. A successful model will contribute to increased efficiencies in the kidney transplant process as well as improved patient outcomes. An overview of the institution’s success was weighed using a survival analysis, with delayed graft function (DGF) as the endpoint. A logistic regression model and forecast model were built to predict the outcome for rejecting or accepting the organ for transplant, as well as to predict the hemodynamic parameters hours after the start of infusion. Results concluded a flow greater than 80 mL/min had a 90% probability of transplantation. The forecast model was capable of predicting flow for up to five hours. The calculated flow was in a 10 mL/min range of the actual flow, when up to one hour parameters were entered into the model. The study concluded practicality in the clinical setting, in kidney transplantation.