Dynamic Data-Driven Modeling for Ex Vivo Data Analysis: Insights into Liver Transplantation and Pathobiology

Abstract

Extracorporeal organ perfusion, in which organs are preserved in an isolated, ex vivo environment over an extended time-span, is a concept that has led to the development of numerous alternative preservation protocols designed to better maintain organ viability prior to transplantation. These protocols offer researchers a novel opportunity to obtain extensive sampling of isolated organs, free from systemic influences. Data-driven computational modeling is a primary means of integrating the extensive and multivariate data obtained in this fashion. In this review, we focus on the application of dynamic data-driven computational modeling to liver pathophysiology and transplantation based on data obtained from ex vivo organ perfusion.

Quantitative methods for optimizing patient outcomes in liver transplantation

“…Statistical modeling of liver pathophysiology and transplantation has also been applied to data acquired from ex vivo organ perfusion to offer insights into liver function. [28] Delving deeper into liver immunobiology, specifically in the development and progression of pediatric acute liver failure, researchers have used PCA to group patients with similar inflammatory signatures, yielding better connections between groups and outcomes using an unsupervised hierarchical clustering algorithm. PCA is a statistical technique used to simplify complex data by transforming it into a new set of uncorrelated variables, called principal components.…”