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Interpreting Activation Mapping of Atrial Fibrillation: A Hybrid Computational/Physiological Study

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Abstract

Atrial fibrillation is the most common rhythm disorder of the heart associated with a rapid and irregular beating of the upper chambers. Activation mapping remains the gold standard to diagnose and interpret atrial fibrillation. However, fibrillatory activation maps are highly sensitive to far-field effects, and often disagree with other optical mapping modalities. Here we show that computational modeling can identify spurious non-local components of atrial fibrillation electrograms and improve activation mapping. We motivate our approach with a cohort of patients with potential drivers of persistent atrial fibrillation. In a computational study using a monodomain Maleckar model, we demonstrate that in organized rhythms, electrograms successfully track local activation, whereas in atrial fibrillation, electrograms are sensitive to spiral wave distance and number, spiral tip trajectories, and effects of fibrosis. In a clinical study, we analyzed n = 15 patients with persistent atrial fibrillation that was terminated by limited ablation. In five cases, traditional activation maps revealed a spiral wave at sites of termination; in ten cases, electrogram timings were ambiguous and activation maps showed incomplete reentry. By adjusting electrogram timing through computational modeling, we found rotational activation, which was undetectable with conventional methods. Our results demonstrate that computational modeling can identify non-local deflections to improve activation mapping and explain how and where ablation can terminate persistent atrial fibrillation. Our hybrid computational/physiological approach has the potential to optimize map-guided ablation and improve ablation therapy in atrial fibrillation.

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Acknowledgments

Francisco Sahli Costabal is supported by a Stanford Cardiovascular Institute Seed Grant, by the Stanford School of Engineering Fellowship, by the Becas Chile-Fulbright Fellowship, and by the National Institute of Health Grant U01 HL119578. Junaid Zaman is supported by a Fulbright British Heart Foundation Scholarship. Ellen Kuhl is supported by the National Institute of Health Grant U01 HL119578. Sanjiv Narayan is supported by the National Institute of Health Grants R01 HL83359 and K24 HL8103800.

Conflict of interest

Sanjiv Narayan is co-author of intellectual property owned by the University of California Regents and licensed to Topera, Inc. He held equity in Topera and received honoraria from Medtronic and St. Jude Medical. The other authors declare no conflict of interest.

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  1. Stanford University, Stanford, CA, USA

    Francisco Sahli Costabal, Junaid A. B. Zaman, Ellen Kuhl & Sanjiv M. Narayan

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  1. Francisco Sahli Costabal
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Correspondence to Ellen Kuhl.

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Sahli Costabal, F., Zaman, J.A.B., Kuhl, E. et al. Interpreting Activation Mapping of Atrial Fibrillation: A Hybrid Computational/Physiological Study. Ann Biomed Eng 46, 257–269 (2018). https://doi.org/10.1007/s10439-017-1969-3

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