Doctors have improved cardiac ablation outcomes for patients with life-threatening arrhythmias by leveraging a “digital twin” of a patient’s heart.
In the first clinical trial of cardiac digital twin technology, researchers at Johns Hopkins University created a digital replica of a patient’s heart and tested the procedure on the twins before performing it on the real thing. Using a digital twin resulted in a faster and significantly more accurate procedure and reduced arrhythmia recurrence in patients compared to traditional methods.
This research today New England Medical Journal, demonstrated the safety, feasibility, and promising results of this approach.
“For patients, digital twins can be life-changing and life-saving,” said lead author Jonathan Crispin, a cardiologist who specializes in treating arrhythmia. “We show that by targeting only important parts of the heart, their procedure can be made safer, shorter and more effective.”
A medical digital twin is a computer model of an organ that mimics its behavior and has predictive capabilities. The heart model was developed at Johns Hopkins University.
Digital twins can help doctors not only diagnose and treat problems, but also predict potential complications based on a patient’s genetics and heart structure.
Each of the 10 participants in the Food and Drug Administration-approved TWIN-VT trial had experienced a heart attack and suffered from ventricular tachycardia, a potentially life-threatening abnormal heartbeat. Arrhythmias are usually treated with a procedure called ablation, which destroys the tissue that causes the arrhythmia, but it can be difficult for doctors to identify the right areas to ablate. The procedure is also very long and the success rate is low. Arrhythmias often return after ablation and may need to be repeated several times before it becomes effective, causing further scarring and damage to the heart.
The team created a personalized digital twin of the heart for each trial participant based on 3D images from clinical contrast-enhanced MRI. Through a digital twin, the researchers studied how each heart processes electricity to predict which parts of the heart are causing arrhythmias, how best to treat each patient, and whether arrhythmias will return after ablation.
“A patient digital twin allows us to test different treatment scenarios before treating the actual patient and provide the treating physician with the optimal scenario, minimizing damage to the heart and increasing the likelihood of treatment success,” said senior author Natalia Trayanova, Murray B. Sachs Professor of Biomedical Engineering and member of the team that developed the digital twin technology used in the clinical trial. “Digital twins allow us to address all potential causes of arrhythmias that may not be apparent during clinical interrogation. We cover all possibilities.”
The predicted targets of these digital twins were imported into a system that navigates the ablation catheter in the operating room. Crispin and his team performed rational ablations based on predictions from the patient’s digital twin.
After the ablation, doctors were unable to induce arrhythmia in any of the subjects, indicating that the surgery was successful. Two patients experienced brief episodes during healing. More than a year later, all 10 patients were arrhythmia-free. Traditional ablation treatments have a long-term success rate of only 60%. Here it was 100%.
Additionally, eight patients completely discontinued antiarrhythmic drugs, and the remaining two had their doses reduced.
“We have shown that this technology is not only possible, but also yields excellent results,” Trayanova said. “This represents the best work in this technology that can move us further toward larger clinical trials.”
The team plans to further test the cardiac digital twin in a larger trial.
They’re also working on making the technology accessible on the desktop, so doctors can get the information in minutes. They also plan to expand this technology to apply to other heart diseases.
Authors include co-lead authors Adityo Prakosa, Eugene Kholmovski, Aravidan Kolandaivelu, Konstantinos N. Aronis MD, PhD, Ronald D. Berger, Hugh Calkins (all of Johns Hopkins University), and Amanda Barcelon of Johnson & Johnson MedTech.
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DOI: 10.1056/NEJMc2517822
