Unmet Need
· Atrial fibrillation affects millions of patients worldwide, with the number expected to grow 2.5-fold in the next 40 years. (Boyle et al., 2016) Ablation of cardiac tissue around the pulmonary veins is a standard treatment, but fails 20-40% of the time in patients with a type of atrial fibrillation called persistent atrial fibrillation. (Kirchhof & Calkins, 2017) Attempts to reduce the atrial fibrillation recurrence rate have been met with a host of problems including ineffectiveness, lack of personalization, and inability to account for the effects of both the presence of fibrosis throughout the atria and the effects of other ablation lesions that were delivered previously or during the given ablation procedure. Therefore, there is a need to develop a, personalized, intelligent, non-invasive method for identifying ablation targets to guide the ablation procedure that accounts for the patient-specific fibrotic remodeling in the atria.
Value Proposition:
· Non-invasive and cost-effective method of identifying ablation targets
· Easily incorporated into the current clinical/ablation system workflow
· Predicts the response of the patient’s digital twin to each ablation step and incorporates it into ablation target selections
· Can be expanded to cardiac conditions beyond persistent atrial fibrillation
Technology Description
· Researchers at Johns Hopkins have developed OPTIMA, a method that builds a model of a patient’s heart ( a “digital twin”) from a cardiac MRI, uses it to conduct electrical simulations to identify possible arrhythmias, and then determines the smallest set of ablation lesions that would eliminate all arrhythmias. This method is non-invasive, cost effective, and can be easily incorporated into the current clinical/ablation system workflow. It can also be extended to apply to other cardiac conditions beyond persistent atrial fibrillation.
Stage of Development
· The technology has been developed and its efficacy is currently being tested in a FDA-approved randomized clinical trial.
Data Availability
N/A
Publication
Boyle PM, Zghaib T, Zahid S, Ali RL, Deng D, Franceschi WH, Hakim JB, Murphy MJ, Prakosa A, Zimmerman SL, Ashikaga H, Marine JE, Kolandaivelu A, Nazarian S, Spragg DD, Calkins H, Trayanova NA. Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nat Biomed Eng. 2019 Nov;3(11):870-879. doi: 10.1038/s41551-019-0437-9. Epub 2019 Aug 19. PMID: 31427780; PMCID: PMC6842421.
