A Simulation Model to Investigate the Impact of Cardiovascular Risk in Renal Transplantation

Abstract

Premature cardiovascular (CV) disease is the leading cause of death following renal transplantation and, as a consequence of death with a functioning graft, it is a major cause of graft loss. Renal transplant recipients have a high prevalence of CV risk factors that influence both patient and graft survival. We used data on the relationship between CV risk factors and graft and patient survivals to develop a discrete event simulation model to study the possible impact of CV risk factor reduction on transplant outcome. The simulation was based on a renal unit in a population that has the risk factor profile of patients from the West of Scotland. We studied the dynamic between patient numbers on the waiting list compared to the transplanted list. After establishing results pertinent to the renal unit, we investigated in what way potential changes to transplant policy affected patient numbers. These peturbations included changing the number of transplants performed, changing the incidence of acute rejection, and interventional policies where patients on the waiting list were selectively transplanted taking into account their CV risk factor profiles. Overall, the model predicts that reducing CV risk in the population with end-stage renal failure awaiting kidney transplantation will have comparable benefits to foreseeable developments in immunosuppression or attainable increases in transplant numbers. Moreover, addressing CV risk has benefits for all patients regardless of whether or not they ultimately receive a kidney transplant.