Precision Medicine in the Management of Heart Transplant Patients Using Advanced Molecular, Imaging, and Machine Learning Approaches

What this Trial Is About

Researchers are exploring a new precision medicine approach for people who have received heart transplants. This study aims to combine molecular, tissue, imaging, and clinical data using machine learning to identify patients at higher risk for heart rejection, infections, and long-term heart graft problems. The study focuses on improving how rejection is diagnosed and predicted in heart transplant recipients. Participants will follow their usual medical care, including heart biopsies as clinically indicated. At each biopsy or within five days before, patients will undergo non-invasive tests and standard clinical evaluations. Tissue samples from biopsies will be studied for gene expression patterns linked to rejection types. Machine learning will help analyze biopsy images and molecular data to develop a non-invasive method to forecast rejection risk. During the study, patients will be monitored over time with biopsies, imaging scans, and lab tests. The main outcome measured is the diagnosis of rejection that requires changing immunosuppressive treatment, tracked over five years. Researchers will also observe infection events and heart tissue scarring. The results aim to better predict rejection and improve heart transplant patient care without changing standard treatments during the study.

Precision Medicine in the Management of Heart Transplant Recipients