Digital twin hearts yield 100% success in early arrhythmia trial

Johns Hopkins researchers used personalized cardiac digital twins to plan ablation for 10 patients with ventricular tachycardia; the FDA-approved TWIN‑VT trial reported all 10 were arrhythmia-free more than a year later and most reduced or stopped anti-arrhythmic medication.

Johns Hopkins researchers tested personalized digital models of patients' hearts to guide cardiac ablation in an early clinical trial. The trial involved people who had prior heart attacks and ventricular tachycardia. Teams built each cardiac model from contrast-enhanced 3D MRI and used simulations to identify likely sources of arrhythmia before the procedure. Predicted targets were imported into the catheter navigation system and clinicians performed streamlined ablations guided by those predictions. Key findings: - Trial design: The FDA-approved TWIN‑VT study enrolled 10 participants who had experienced heart attacks and ventricular tachycardia. - Modeling method: Personalized cardiac digital twins were created from contrast-enhanced MRI and used to simulate electrical behavior and predict arrhythmia sources and optimal ablation targets. - Procedure: Predicted targets were imported into the ablation navigation system and clinicians carried out the guided ablations. - Immediate result: After each ablation, arrhythmias could not be induced in any subject, and two patients had brief episodes while healing. - One-year outcome: More than a year after treatment, all 10 patients were reported free of arrhythmia; eight were off anti-arrhythmic medication and two had reduced doses. - Publication and support: The study was published in the New England Journal of Medicine and was supported by NIH grant R01HL174440 and the Leducq Foundation. Summary: The small, FDA-approved trial reported faster and more targeted ablations with high short- and long-term success in this cohort, compared with typical long-term success rates reported around 60% for standard treatment. The research team plans larger clinical testing and is working to make the technology more accessible, including desktop-based workflows for quicker clinical use.