1 research outputs found
Quantitative Methods for Optimizing Patient Outcomes in Liver Transplantation
Liver transplantation continues to be the gold standard for treating patients
with end-stage liver diseases. However, despite the huge success of liver
transplantation in improving patient outcomes, long term graft survival
continues to be a major problem. The current clinical practice in the
management of liver transplant patients is centered around immunosuppressive
multidrug regimens. Current research has been focusing on phenotypic
personalized medicine as a novel approach in the optimization of
immunosuppression, a regressional math modeling focusing on individual patient
dose and response using specific markers like transaminases. A prospective area
of study includes the development of a mechanistic computational math modeling
for optimizing immunosuppression to improve patient outcomes and increase
long-term graft survival by exploring the intricate immune/drug interactions to
help us further our understanding and management of medical problems like
transplants, autoimmunity, and cancer therapy. Thus, by increasing long-term
graft survival, the need for redo transplants will decrease, which will free up
organs and potentially help with the organ shortage problem promoting equity
and equal opportunity for transplants, as well as decreasing the medical costs
associated with additional testing and hospital admissions. Although long-term
graft survival remains challenging, computational and quantitative methods have
led to significant improvements. In this article, we review recent advances and
remaining opportunities. We focus on the following topics: donor organ
availability and allocation with a focus on equity, monitoring of patient and
graft health, and optimization of immunosuppression dosing.Comment: 2 figures, including a graphical abstrac