Non-invasive prediction of atrial cardiomyopathy characterized by multipolar high-density contact mapping
Among patients qualifying for PVI, machine learning analysis of high-resolution LA maps allowed to identify subgroups with mild and severe AC avoiding the use of arbitrary LVA thresholds. The subgroups were predicted non-invasively with good accuracy using a machine learning approach that incorporated a set of echocardiographic markers. This data could advance the clinical triage of patients with AF.
Authors
- Moritz Till Huttelmaier (ORCID: https://orcid.org/0000-0002-9001-5649)
- Jonas Herting (ORCID: https://orcid.org/0000-0002-3854-1078)
- Alexander Gabel (ORCID: https://orcid.org/0000-0002-8064-3289)
- Stefan Frantz (ORCID: https://orcid.org/0000-0002-0301-6185)
- Stefan Störk (ORCID: https://orcid.org/0000-0002-1771-7249)
- Caroline Morbach (ORCID: https://orcid.org/0000-0002-0920-6094)
- Thomas H. Fischer (ORCID: https://orcid.org/0000-0002-7324-2575)
- M Vogel
Institutions
- Goethe University Frankfurt (DE)
- Universitäts-Kinderklinik Würzburg (DE)
- Universitätsklinikum Würzburg (DE)
Publication Details
- Journal
- Journal of Interventional Cardiac Electrophysiology
- Published
- 2025-02-03
- DOI
- https://doi.org/10.1007/s10840-025-02001-2
- Primary Topic
- Atrial Fibrillation Management and Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Deutsche Forschungsgemeinschaft
- Deutsche Stiftung für Herzforschung
- Bayerische Staatsministerium für Wirtschaft, Landesentwicklung und Energie