dc.contributor.advisor | 2023 Computing in Cardiology (CinC) | |
dc.contributor.author | Jaffery, OA | en_US |
dc.contributor.author | Horrach, CV | en_US |
dc.contributor.author | Lagalante, DJ | en_US |
dc.contributor.author | Thomas, G | en_US |
dc.contributor.author | Slabaugh, G | en_US |
dc.contributor.author | Melki, L | en_US |
dc.contributor.author | Good, WW | en_US |
dc.contributor.author | Roney, CH | en_US |
dc.date.accessioned | 2024-02-02T12:00:56Z | |
dc.date.issued | 2023-01-01 | en_US |
dc.identifier.isbn | 9798350382525 | en_US |
dc.identifier.issn | 2325-8861 | en_US |
dc.identifier.uri | https://qmro.qmul.ac.uk/xmlui/handle/123456789/94421 | |
dc.description.abstract | Improving patient outcomes with ablation of non-paroxysmal AF (PsAF) has proved challenging using a population-based treatment approach due to large interindividual variability in the underlying electroanatomical substrate. Ablation of pathologic conduction patterns outside of pulmonary vein isolation (PVI) has recently shown encouraging results in PsAF patients returning for their first or second retreatment (76% freedom from AF recorded in the RECOVER AF trial). However, the optimal targets and best sequence of ablation lesions are still unknown, and testing different sequences, types, and methods of ablation cannot be performed clinically on a single patient or patient cohort. Considering the predictive potential of computational modelling, a small exploratory subset of patients (N=4) enrolled in the ongoing DISCOVER trial was used to create patient-specific models of left atrial electrophysiology. The subject-specific models displayed a high correlation between simulated targets and clinical targets. AF complexity was highest in all patients prior to therapy. PVI caused a marginal decrease in complexity across the cohort whereas PVI+PCP showed an extensive decrease in the AF complexity across the patients and resulted in AF termination in all patients. | en_US |
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dc.title | Subject-Specific Ablation of Pathologic Conduction Patterns Beyond the Pulmonary Veins: A Personalised Modelling Approach | en_US |
dc.type | Conference Proceeding | |
dc.identifier.doi | 10.22489/CinC.2023.400 | en_US |
pubs.notes | Not known | en_US |
pubs.publication-status | Published | en_US |
rioxxterms.funder | Default funder | en_US |
rioxxterms.identifier.project | Default project | en_US |
qmul.funder | Mapping populations to patients: designing optimal ablation therapy for atrial fibrillation through simulation and deep learning of digital twins::UK Research and Innovation | en_US |