Precision Medicine in the Management of Heart Transplant Recipients

Overview

The goal of this clinical trial is to demonstrate that a multiparametric approach, based on the integration of biomolecular, histological, imaging, and clinical information, along with the use of machine learning methods, can identify among heart transplant patients those at higher risk of rejection, infectious events, and chronic graft dysfunction. Patients have been and will be treated according to clinical practice, in accordance with the physician's judgment and the information provided in the Technical Data Sheet of each individual product used in concomitant therapies, if administered according to clinical practice. The diagnostic-therapeutic pathway of the patients will not be in any way influenced by the results of tissue tests performed for the purposes of the study

The main focus of the study, and the primary outcome measure, is the diagnosis of rejection based on the histopathological examination of myocardial biopsies. At the time of each biopsy procedure, or within the 5 days immediately preceding, enrolled patients will undergo specific non-invasive investigations, along with baseline clinical and laboratory evaluations. Additional analyses will be performed on myocardial tissue samples obtained during biopsies conducted for clinical practice, including the analysis of the intramyocardial gene expression profile (nanostring and microarray), to identify molecular profiles specific to cellular or antibody-mediated rejection. The results of these investigations will be correlated with the findings from histopathological and molecular analyses of the biopsies using artificial intelligence methods, in order to develop a non-invasive investigation algorithm that can predict the risk of rejection. Similarly, these diagnostic investigations will be longitudinally related to the incidence of infection events, as defined above, and to the development of myocardial fibrosis diagnosed by cardiac magnetic resonance imaging. In a subsequent phase, biopsies will be reclassified through the examination of histopathological morphology using machine learning techniques. The result of the re-evaluation of the histological samples will be correlated with the diagnosis based on the intramyocardial gene expression and the predictiveness of the previously developed algorithm.