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Structural and electrophysiological determinants of atrial cardiomyopathy identify remodeling discrepancies between paroxysmal and persistent atrial fibrillation

Huang, Taiyuan; Nairn, Deborah ORCID iD icon 1; Chen, Juan; Mueller-Edenborn, Bjoern; Pilia, Nicolas; Mayer, Louisa; Eichenlaub, Martin; Moreno-Weidmann, Zoraida; Allgeier, Juergen; Trenk, Dietmar; Ahlgrim, Christoph; Westermann, Dirk; Arentz, Thomas; Loewe, Axel ORCID iD icon 1; Jadidi, Amir
1 Institut für Biomedizinische Technik (IBT), Karlsruher Institut für Technologie (KIT)

Abstract:

Background: Progressive atrial fibrotic remodeling has been reported to be associated with atrial cardiomyopathy (ACM) and the transition from paroxysmal to persistent atrial fibrillation (AF). We sought to identify the anatomical/structural and electrophysiological factors involved in atrial remodeling that promote AF persistency.
Methods: Consecutive patients with paroxysmal (n = 134) or persistent (n = 136) AF who presented for their first AF ablation procedure were included. Patients underwent left atrial (LA) high-definition mapping (1,835 ± 421 sites/map) during sinus rhythm (SR) and were randomized to training and validation sets for model development and evaluation. A total of 62 parameters from both electro-anatomical mapping and non-invasive baseline data were extracted encompassing four main categories: (1) LA size, (2) extent of low-voltage-substrate (LVS), (3) LA voltages and (4) bi-atrial conduction time as identified by the duration of amplified P-wave (APWD) in a digital 12-lead-ECG. Least absolute shrinkage and selection operator (LASSO) and logistic regression were performed to identify the factors that are most relevant to AF persistency in each category alone and all categories combined. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000155732
Veröffentlicht am 10.02.2023
Originalveröffentlichung
DOI: 10.3389/fcvm.2022.1101152
Scopus
Zitationen: 3
Web of Science
Zitationen: 3
Dimensions
Zitationen: 3
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Biomedizinische Technik (IBT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 2297-055X
KITopen-ID: 1000155732
Erschienen in Frontiers in Cardiovascular Medicine
Verlag Frontiers Media SA
Band 9
Seiten Art.-Nr.: 1101152
Vorab online veröffentlicht am 11.01.2023
Schlagwörter atrial fibrillation, atrial cardiomyopathy, mapping, ECG, machine learning
Nachgewiesen in Web of Science
Dimensions
Scopus
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