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Computationally guided personalized targeted ablation of persistent atrial fibrillation

Nature Biomedical Engineering volume 3, pages 870–879 (2019)Cite this article

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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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Fig. 1: OPTIMA approach flowchart.
Fig. 2: Schematic summarizing the process of importing OPTIMA ablation targets into CARTO.
Fig. 3: Examples of the process to determine the OPTIMA ablation targets for three patients.
Fig. 4: Data from the OPTIMA-driven ablation procedures in the three patients from Fig. 2.

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Code availability

The image-processing software ITK-SNAP is freely available from http://www.itksnap.org/. Computational meshes were generated using the commercial software Simpleware ScanIP (Synopsys). Source code for the human atrial ionic model is freely available from the repository CellML (https://models.physiomeproject.org/exposure/0e03bbe01606be5811691f9d5de10b65). All simulations were conducted using the software package CARP, a free version of which can be downloaded for academic use via https://carp.medunigraz.at/carputils/. Simulation results were visualized using either Meshalyzer (which can be downloaded via https://github.com/cardiosolv/meshalyzer) or ParaView (Kitware) (which can be downloaded via https://www.paraview.org/download/). Data from clinical procedures were visualized using the commercial software CARTOMERGE (Biosense Webster).

Data availability

Relevant data, including patient MRI scans, are available from the authors on approval from the Johns Hopkins Institutional Review Board.

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Acknowledgements

This project was supported by grants from the NIH (DP1-HL123271 to N.A.T. and U01-HL141074 to N.A.T. and P.M.B.), the AHA (16-SDG-30440006 to P.M.B.), Biosense Webster (to S.N.) and the NSF (graduate fellowship to S.Z.). This project has received funding from the Leducq Foundation (Research Grant number 16 CVD 02). This project was also supported by the Roz and Marvin H. Weiner and Family Foundation, the Dr Francis P. Chiaramonte Private Foundation, M. Poindexter, C. Poindexter and the Norbert and Louise Grunwald Cardiac Arrhythmia Research Fund.

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  1. These authors contributed equally: Patrick M. Boyle, Tarek Zghaib.

Authors and Affiliations

  1. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA

    Patrick M. Boyle, Sohail Zahid, William H. Franceschi, Michael J. Murphy & Natalia A. Trayanova

  2. Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA

    Patrick M. Boyle, Rheeda L. Ali, Dongdong Deng, Joe B. Hakim, Adityo Prakosa & Natalia A. Trayanova

  3. Department of Bioengineering, University of Washington, Seattle, WA, USA

    Patrick M. Boyle

  4. Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, WA, USA

    Patrick M. Boyle

  5. Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Tarek Zghaib, Hiroshi Ashikaga, Joseph E. Marine, Aravindan Kolandaivelu, David D. Spragg & Hugh Calkins

  6. Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Stefan L. Zimmerman

  7. Division of Cardiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Saman Nazarian

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Contributions

T.Z., P.M.B., S.Z., A.P., S.N., H.C. and N.A.T. designed the study. P.M.B., S.Z., R.L.A., D.D., W.H.F., J.B.H. and M.J.M. conducted the simulations and analysed the simulation results. T.Z., P.M.B., S.Z., R.L.A., J.B.H., A.P., S.L.Z., H.A., J.E.M., A.K., S.N., D.D.S., H.C. and N.A.T. interpreted the imaging and simulation data. H.A., J.E.M. and H.C. performed the catheter ablation procedures. T.Z., P.M.B. and N.A.T. wrote the paper. H.A., J.E.M., D.D.S. and H.C. edited the manuscript.

Corresponding author

Correspondence to Natalia A. Trayanova.

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Competing interests

N.A.T. has filed a patent application (US patent application number US0161100A1; World Intellectual Property Organization application number WO2015/084876A1; European Patent Office application number EP3076869A4; Japan application number JP2016540570A; Israel application number IL245988D0; entitled ‘Systems and methods for atrial fibrillation treatment and risk assessment’) that is currently under review.

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Supplementary information

Supplementary Information

Supplementary figures, tables and video captions.

Reporting Summary

Supplementary Video 1

Dynamic illustration of all of the steps in the OPTIMA approach.

Supplementary Video 2

Dynamic illustration of the results shown in the top two rows of Fig. 2 (patient 5).

Supplementary Video 3

Same as Supplementary Video 2, but for the results shown in the third and fourth rows of Fig. 2 (patient 7).

Supplementary Video 4

Same as Supplementary Video 2, but for the results shown in the bottom two rows of Fig. 2 (patient 9).

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Boyle, P.M., Zghaib, T., Zahid, S. et al. Computationally guided personalized targeted ablation of persistent atrial fibrillation. Nat Biomed Eng 3, 870–879 (2019). https://doi.org/10.1038/s41551-019-0437-9

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