ISHLT Foundation Funds Research in AI + Biomarkers in Heart and Lung Transplantation, and ECP
During the 45th Annual Meeting & Scientific Sessions in Boston, ISHLT announced $250,000 USD in grants funded by the ISHLT Foundation to support research to advance heart and lung disease.
ISHLT Utilization of Non-Invasive Biomarkers in Combination with Artificial Intelligence/Machine Learning in the Care of Heart Transplant Candidates or Recipients Grant, supported by CareDx
Awarded to: Nicole Asemota, MRCS, MBBS, BMedSci (Hons), AFHEA
Royal Papworth Hospital, Cambridge, United Kingdom
Research Title: “Markers of Transplantability in Ex-SiTu Perfused Hearts: The TEST Study”
This project represents an extraordinary collaboration between doctors, nurses, engineers, veterinarians, biochemical analysts, bioinformaticians and clinical statisticians. The funding from this grant will support integrated analysis of multiple different potential markers, supported by high level statistical analysis and machine learning. Upon completion, this research will aim to improve the rates of donation after circulatory death (DCD) heart utilization, increasing the number of successful heart transplants and lives saved.
ISHLT Utilization of Non-Invasive Biomarkers in Combination with Artificial Intelligence/Machine Learning in the Care of Heart Transplant Candidates or Recipients Grant, supported by CareDx
Awarded to: Asif Padiyath, MBBS
Children’s Hospital of Philadelphia / Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA USA
Research Title: “Machine Learning-Driven Prediction of Hemodynamics in Pediatric Heart Transplant Recipients Using Non-Invasive Venous Waveform Analysis”
This study utilizes a new technology called Non-Invasive Venous waveform Analysis (NIVA) to explore a safer, non-invasive way to monitor heart function in children who have received a heart transplant. The main aim is to determine if NIVA can accurately predict the same heart pressures that are currently measured through cardiac catheterization. If successful, this research could provide heart transplant recipients with a safer, quicker, and less invasive way to monitor their heart function, improving care and potentially reducing the need for invasive procedures.
ISHLT Non-Invasive Biomarker Study Using Artificial Intelligence/Machine Learning Analysis in Lung Transplantation Grant, supported by CareDx
Awarded to: Eric Abston, MD, PhD
Massachusetts General Hospital, Boston, MA USA
Research Title: “AI-Enhanced Nutritional Profiling for Improved Lung Transplant Success: A Focus on Body Composition Metrics”
This project aims to overcome that challenge by developing artificial intelligence algorithms that extract meaningful data from routine CT scans to create image-based biomarkers of nutritional status. The research will validate these imaging biomarkers by evaluating how well they predict key clinical outcomes, such as post-transplant survival. With funding from this grant, the team will advance the development of an AI-powered tool that leverages existing medical imaging to give transplant teams enhanced, actionable information about a candidate's readiness and likely success following transplantation. Ultimately, this technology has the potential to help transplant teams make more informed decisions and improve long-term survival rates after lung transplantation.
ISHLT Non-Invasive Biomarker Study Using Artificial Intelligence/Machine Learning Analysis in Lung Transplantation Grant, supported by CareDx
Awarded to: Stephen Juvet, MD, PhD
University Health Network, Toronto, ON Canada
Research Title: “A Machine Learning Enhanced Lung-Specific Autoantibody Signature to Identify Patients At Risk for CLAD”
While the precise mechanisms behind chronic lung allograft dysfunction (CLAD), and why standard immunosuppressive therapies often fail to prevent it, remain unclear, recent research has identified autoantibodies as a key contributing factor. This study aims to validate these findings using blood samples from additional lung transplant recipients. The research will also leverage machine learning to identify the most predictive autoantibodies and determine the optimal timing for testing after transplantation. The ultimate goal is to develop a simple, clinically applicable blood test that enables earlier identification of high-risk patients and supports personalized treatment strategies—potentially improving outcomes and making lung transplantation a safer, more effective option for those with advanced lung disease.
ISHLT Extracorporeal Photopheresis Immunomodulation in Thoracic Transplantation Challenge Grant, supported by Therakos Healthcare Limited
Awarded to: Jérôme Le Pavec, MD, PhD
Hôpital Marie Lannelongue, Le Plessis Robinson, France
Research Title: "Biomarker Guided Extracorporeal Photopheresis for Chronic Lung Allograft Dysfunction: The BioCLAD-ECP Study"
This research project is a prospective, multicenter, observational pilot study investigating the use of extracorporeal photochemotherapy (ECP) in the treatment of CLAD following bilateral lung transplantation. The study focuses on evaluating two key biomarkers—donor-derived cell-free DNA (dd-cfDNA) and Torque Teno Virus (TTV) viral load—both before and after the initiation of ECP therapy. Ultimately, this study aims to enhance the clinical utility of dd-cfDNA and TTV as tools for identifying suitable ECP candidates, monitoring therapeutic response, and optimizing immunosuppressive management in patients with CLAD.
For more information about ISHLT and ISHLT Foundation Research Grants, visit https://www.ishlt.org/grants-and-awards/research-grants. The next grant cycle opens near the end of May 2025.