By analyzing heart rhythm recordings and blood tests, artificial intelligence may accurately flag when a transplant patient's body starts attacking a donated heart, a new study suggests.
The current gold standard for diagnosing heart transplant rejection is a biopsy, the surgical removal of a small piece of heart muscle, which experts inspect under the microscope for inflammation and other changes that may indicate that the body's immune system is rejecting an organ.
Led by NYU Langone Health researchers, the study explored an alternative method: whether combining electrocardiograms (EKGs)—which record the heart's electrical activity using sensors placed on the skin—with blood tests could help identify rejection without the need for an invasive biopsy in many cases.
The team trained AI models to recognize patterns in 5,300 EKG readings taken in 2,357 adult heart transplant recipients. One AI model was trained on EKG data alone, while another AI tool looked at EKG readings and the results of two blood tests commonly used to predict rejection risk. Biopsy records paired with EKG readings from the same patients served as a measure for the tools' prediction accuracy.
In a test group of an additional 38 male and female heart transplant recipients, the researchers found that the combined model performed better, correctly identifying 94 percent of patients who were not experiencing rejection. By contrast, the model based on blood tests incorrectly flagged 19 patients as potentially needing a biopsy. The combined model, the researchers said, would have spared the patients from the procedure.
"Our results highlight that electrocardiograms contain an abundance of physiological information that can be used to substantially improve the accuracy of detection and enable earlier diagnosis and treatment for patients with cardiac transplant rejection," said study senior author Lior Jankelson, MD, PhD, an associate professor in the Leon H. Charney Division of Cardiology in NYU Grossman School of Medicine's Department of Medicine and an associate professor of biomedical engineering at NYU Tandon School of Engineering.
Previous work has shown that the blood biomarkers, which measure gene activity linked to cellular rejection and fragments of donor DNA in the recipient's bloodstream, are effective at spotting rejection. However they frequently produce false-positive test results, which lead to unnecessary biopsies, the researchers said.
Published online Sept. 25 in the Journal of Heart and Lung Transplantation, the study is the first to combine these blood biomarkers with EKG readings in a single AI-enabled model and compare the analysis directly against biopsy results, the researchers said.
For the study, the researchers trained the combined AI model on EKG data along with the biomarkers from heart transplant recipients treated between 2018 and 2024, matching each EKG to a biopsy performed within the past month—a window supported by earlier research. Organ rejections were grouped into two categories: no or mild rejection versus moderate or severe rejection. Treatment changes, such as adjusting immune-suppressing medications, are typically made only for the more serious cases.
"Heart transplantation is a major, resource-intensive procedure, and rejection is common but treatable if caught in time," said Dr. Jankelson. "Based on our findings, combining the availability of EKGs with the support of AI may help identify rejection earlier and save more lives."
As a next step, the researchers plan to test their model in more patients at several transplant centers.
Study funding was provided by NYU Langone.
Other NYU Langone researchers involved in the study were Kevin Chen, MD; Robert Ronan, MS; Larry A. Chinitz, MD; and Randal I. Goldberg, MD.
