Advances and New Insights in Post-Transplant Care: From Sequencing to Imaging
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(2020) 22:32
Heart Failure (W Tang, Section Editor)
Advances and New Insights in Post-Transplant Care: From Sequencing to Imaging Carol E. Battikha, MD Ibrahim Selevany, MD Paul J. Kim, MD* Address * Division of Cardiovascular Medicine, Department of Medicine, University of California, San Diego, La Jolla, CA, USA Email: [email protected]
* Springer Science+Business Media, LLC, part of Springer Nature 2020
This article is part of the Topical Collection on Heart Failure Carol E. Battikha and Ibrahim Selevany contributed equally to this work. Keywords Heart-transplantation I Cardiovascular imaging I Cardiac magnetic resonance imaging I AlloMap I Donor-derived cell-free DNA I Single-cell RNA sequencing I Molecular microscope I Machine learning
Abstract Purpose of review Cardiac imaging and sequencing have greatly improved over the recent years. The goal of this review is to summarize these recent advances in cardiac imaging and sequencing, their application in heart-transplantation, and provide our perspective in how artificial intelligence provides a new paradigm for big data-driven analysis in hearttransplant research. Recent findings Cardiac imaging, particularly parametric mapping by cardiac MRI and global longitudinal strain by echocardiography, has improved our understanding of cardiac allograft rejection and prediction of adverse clinical outcomes. Independently, gene expression profiling and measurement of donor-derived cell-free DNA have greatly improved risk stratification for acute rejection. More recently, data-driven phenotypic clustering using novel machine learning algorithms has been used to identify a distinct macrophage subset, associated with acute rejection. Summary Developments in imaging and sequencing techniques in the application of hearttransplant research are improving rapidly and in parallel with improvements in analysis of these large datasets. The approach to heart-transplant research is in the transition of significant change as big data-driven analysis identifies new mechanistic patterns that can be combined with traditional hypothesis testing.
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Curr Treat Options Cardio Med
(2020) 22:32
Introduction The morbidity and mortality of patients with heart failure have improved considerably over recent years [1–3]. However, there remain patients who continue to progress in their heart failure and ultimately require hearttransplantation for increased survival and improved quality of life [4, 5]. Remarkably, heart-transplant volumes have continued to grow in the past decade despite the apparent limited supply of donor hearts [5]. Heart-transplant care has gradually improved with an increase in median cardiac allograft longevity from 11 to 12.5 years over the last two decades [5]. Immunemediated rejection however limits longer allograft survival and is defined as either acute or chronic rejection [6]. Acute rejection is currently diagnosed by endomyocardial biopsy, classified as either acute cellular rejection (ACR) or antibody mediated rejection (AMR), and typicall
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