In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation
Catheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which us...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2020-09-01
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| Series: | Frontiers in Physiology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fphys.2020.572874/full |
| _version_ | 1819092054958931968 |
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| author | Caroline H. Roney Marianne L. Beach Arihant M. Mehta Iain Sim Cesare Corrado Rokas Bendikas Jose A. Solis-Lemus Orod Razeghi John Whitaker Louisa O’Neill Gernot Plank Edward Vigmond Steven E. Williams Mark D. O’Neill Steven A. Niederer |
| author_facet | Caroline H. Roney Marianne L. Beach Arihant M. Mehta Iain Sim Cesare Corrado Rokas Bendikas Jose A. Solis-Lemus Orod Razeghi John Whitaker Louisa O’Neill Gernot Plank Edward Vigmond Steven E. Williams Mark D. O’Neill Steven A. Niederer |
| author_sort | Caroline H. Roney |
| collection | DOAJ |
| description | Catheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which use imaging data together with electroanatomic mapping data, depending on data availability. The aim of this study was to compare ablation techniques across a virtual cohort of AF patients. We constructed 20 paroxysmal and 30 persistent AF patient-specific left atrial (LA) bilayer models incorporating fibrotic remodeling from late-gadolinium enhancement (LGE) MRI scans. AF was simulated and post-processed using phase mapping to determine electrical driver locations over 15 s. Six different ablation approaches were tested: (i) PVI alone, modeled as wide-area encirclement of the pulmonary veins; PVI together with: (ii) roof and inferior lines to model posterior wall box isolation; (iii) isolating the largest fibrotic area (identified by LGE-MRI); (iv) isolating all fibrotic areas; (v) isolating the largest driver hotspot region [identified as high simulated phase singularity (PS) density]; and (vi) isolating all driver hotspot regions. Ablation efficacy was assessed to predict optimal ablation therapies for individual patients. We subsequently trained a random forest classifier to predict ablation response using (a) imaging metrics alone, (b) imaging and electrical metrics, or (c) imaging, electrical, and ablation lesion metrics. The optimal ablation approach resulting in termination, or if not possible atrial tachycardia (AT), varied among the virtual patient cohort: (i) 20% PVI alone, (ii) 6% box ablation, (iii) 2% largest fibrosis area, (iv) 4% all fibrosis areas, (v) 2% largest driver hotspot, and (vi) 46% all driver hotspots. Around 20% of cases remained in AF for all ablation strategies. The addition of patient-specific and ablation pattern specific lesion metrics to the trained random forest classifier improved predictive capability from an accuracy of 0.73 to 0.83. The trained classifier results demonstrate that the surface areas of pre-ablation driver regions and of fibrotic tissue not isolated by the proposed ablation strategy are both important for predicting ablation outcome. Overall, our study demonstrates the need to select the optimal ablation strategy for each patient. It suggests that both patient-specific fibrosis properties and driver locations are important for planning ablation approaches, and the distribution of lesions is important for predicting an acute response. |
| first_indexed | 2024-12-21T22:49:31Z |
| format | Article |
| id | doaj.art-941b1b8d71e04ff8ba239d740e028b15 |
| institution | Directory Open Access Journal |
| issn | 1664-042X |
| language | English |
| last_indexed | 2024-12-21T22:49:31Z |
| publishDate | 2020-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj.art-941b1b8d71e04ff8ba239d740e028b152022-12-21T18:47:36ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2020-09-011110.3389/fphys.2020.572874572874In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial FibrillationCaroline H. Roney0Marianne L. Beach1Arihant M. Mehta2Iain Sim3Cesare Corrado4Rokas Bendikas5Jose A. Solis-Lemus6Orod Razeghi7John Whitaker8Louisa O’Neill9Gernot Plank10Edward Vigmond11Steven E. Williams12Mark D. O’Neill13Steven A. Niederer14School of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomDepartment of Biophysics, Medical University of Graz, Graz, AustriaIHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, FranceSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomSchool of Biomedical Engineering and Imaging Sciences, King’s College London, London, United KingdomCatheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which use imaging data together with electroanatomic mapping data, depending on data availability. The aim of this study was to compare ablation techniques across a virtual cohort of AF patients. We constructed 20 paroxysmal and 30 persistent AF patient-specific left atrial (LA) bilayer models incorporating fibrotic remodeling from late-gadolinium enhancement (LGE) MRI scans. AF was simulated and post-processed using phase mapping to determine electrical driver locations over 15 s. Six different ablation approaches were tested: (i) PVI alone, modeled as wide-area encirclement of the pulmonary veins; PVI together with: (ii) roof and inferior lines to model posterior wall box isolation; (iii) isolating the largest fibrotic area (identified by LGE-MRI); (iv) isolating all fibrotic areas; (v) isolating the largest driver hotspot region [identified as high simulated phase singularity (PS) density]; and (vi) isolating all driver hotspot regions. Ablation efficacy was assessed to predict optimal ablation therapies for individual patients. We subsequently trained a random forest classifier to predict ablation response using (a) imaging metrics alone, (b) imaging and electrical metrics, or (c) imaging, electrical, and ablation lesion metrics. The optimal ablation approach resulting in termination, or if not possible atrial tachycardia (AT), varied among the virtual patient cohort: (i) 20% PVI alone, (ii) 6% box ablation, (iii) 2% largest fibrosis area, (iv) 4% all fibrosis areas, (v) 2% largest driver hotspot, and (vi) 46% all driver hotspots. Around 20% of cases remained in AF for all ablation strategies. The addition of patient-specific and ablation pattern specific lesion metrics to the trained random forest classifier improved predictive capability from an accuracy of 0.73 to 0.83. The trained classifier results demonstrate that the surface areas of pre-ablation driver regions and of fibrotic tissue not isolated by the proposed ablation strategy are both important for predicting ablation outcome. Overall, our study demonstrates the need to select the optimal ablation strategy for each patient. It suggests that both patient-specific fibrosis properties and driver locations are important for planning ablation approaches, and the distribution of lesions is important for predicting an acute response.https://www.frontiersin.org/article/10.3389/fphys.2020.572874/fullatrial fibrillationvirtual cohortcatheter ablationatrial fibrosisphase singularity mapping |
| spellingShingle | Caroline H. Roney Marianne L. Beach Arihant M. Mehta Iain Sim Cesare Corrado Rokas Bendikas Jose A. Solis-Lemus Orod Razeghi John Whitaker Louisa O’Neill Gernot Plank Edward Vigmond Steven E. Williams Mark D. O’Neill Steven A. Niederer In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation Frontiers in Physiology atrial fibrillation virtual cohort catheter ablation atrial fibrosis phase singularity mapping |
| title | In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation |
| title_full | In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation |
| title_fullStr | In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation |
| title_full_unstemmed | In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation |
| title_short | In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation |
| title_sort | in silico comparison of left atrial ablation techniques that target the anatomical structural and electrical substrates of atrial fibrillation |
| topic | atrial fibrillation virtual cohort catheter ablation atrial fibrosis phase singularity mapping |
| url | https://www.frontiersin.org/article/10.3389/fphys.2020.572874/full |
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