Computationally guided personalized targeted ablation of persistent atrial fibrillation

Patrick M. Boyle; Tarek Zghaib; Sohail Zahid; Rheeda L. Ali; Dongdong Deng; William H. Franceschi; Joe B. Hakim; Michael J. Murphy; Adityo Prakosa; Stefan L. Zimmerman; Hiroshi Ashikaga; Joseph E. Marine; Aravindan Kolandaivelu; Saman Nazarian; David D. Spragg; Hugh Calkins; Natalia A. Trayanova

doi:10.1038/s41551-019-0437-9

Computationally guided personalized targeted ablation of persistent atrial fibrillation

Patrick M. Boyle, Tarek Zghaib, Sohail Zahid, Rheeda L. Ali, Dongdong Deng, William H. Franceschi, Joe B. Hakim, Michael J. Murphy, Adityo Prakosa, Stefan L. Zimmerman, Hiroshi Ashikaga, Joseph E. Marine, Aravindan Kolandaivelu, Saman Nazarian, David D. Spragg, Hugh Calkins, Natalia A. Trayanova

Research output: Contribution to journal › Article › peer-review

47 Scopus citations

Abstract

Atrial fibrillation (AF)—the most common arrhythmia—significantly increases the risk of stroke and heart failure. Although catheter ablation can restore normal heart rhythms, patients with persistent AF who develop atrial fibrosis often undergo multiple failed ablations, and thus increased procedural risks. Here, we present personalized computational modelling for the reliable predetermination of ablation targets, which are then used to guide the ablation procedure in patients with persistent AF and atrial fibrosis. First, we show that a computational model of the atria of patients identifies fibrotic tissue that, if ablated, will not sustain AF. Then, we report the results of integrating the target ablation sites in a clinical mapping system and testing its feasibility in ten patients with persistent AF. The computational prediction of ablation targets avoids lengthy electrical mapping and could improve the accuracy and efficacy of targeted AF ablation in patients while eliminating the need for repeat procedures.

Original language	English (US)
Pages (from-to)	870-879
Number of pages	10
Journal	Nature biomedical engineering
Volume	3
Issue number	11
DOIs	https://doi.org/10.1038/s41551-019-0437-9
State	Published - Nov 1 2019

ASJC Scopus subject areas

Biotechnology
Bioengineering
Medicine (miscellaneous)
Biomedical Engineering
Computer Science Applications

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10.1038/s41551-019-0437-9

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Boyle, P. M., Zghaib, T., Zahid, S., Ali, R. L., Deng, D., Franceschi, W. H., Hakim, J. B., Murphy, M. J., Prakosa, A., Zimmerman, S. L., Ashikaga, H., Marine, J. E., Kolandaivelu, A., Nazarian, S., Spragg, D. D., Calkins, H., & Trayanova, N. A. (2019). Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nature biomedical engineering, 3(11), 870-879. https://doi.org/10.1038/s41551-019-0437-9

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 2019, 'Computationally guided personalized targeted ablation of persistent atrial fibrillation', Nature biomedical engineering, vol. 3, no. 11, pp. 870-879. https://doi.org/10.1038/s41551-019-0437-9

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abstract = "Atrial fibrillation (AF)—the most common arrhythmia—significantly increases the risk of stroke and heart failure. Although catheter ablation can restore normal heart rhythms, patients with persistent AF who develop atrial fibrosis often undergo multiple failed ablations, and thus increased procedural risks. Here, we present personalized computational modelling for the reliable predetermination of ablation targets, which are then used to guide the ablation procedure in patients with persistent AF and atrial fibrosis. First, we show that a computational model of the atria of patients identifies fibrotic tissue that, if ablated, will not sustain AF. Then, we report the results of integrating the target ablation sites in a clinical mapping system and testing its feasibility in ten patients with persistent AF. The computational prediction of ablation targets avoids lengthy electrical mapping and could improve the accuracy and efficacy of targeted AF ablation in patients while eliminating the need for repeat procedures.",

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AU - Zimmerman, Stefan L.

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Computationally guided personalized targeted ablation of persistent atrial fibrillation

Abstract

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