Unipolar voltage electroanatomical mapping detects structural atrial remodeling identified by LGE-MRI.

Atrial cardiomyopathy Atrial fibrillation Atrial fibrosis Atrial substrate Electroanatomical Mapping LGE-MRI Low-Voltage-Areas Neural-networks Unipolar voltage

Journal

Heart rhythm
ISSN: 1556-3871
Titre abrégé: Heart Rhythm
Pays: United States
ID NLM: 101200317

Informations de publication

Date de publication:
11 Oct 2024
Historique:
received: 20 05 2024
revised: 05 10 2024
accepted: 08 10 2024
medline: 14 10 2024
pubmed: 14 10 2024
entrez: 13 10 2024
Statut: aheadofprint

Résumé

In atrial fibrillation (AF) management, understanding left atrial (LA) substrate is crucial. While both electroanatomical mapping (EAM) and late gadolinium enhancement MRI (LGE-MRI) are accepted methods for assessing the atrial substrate and are associated with ablation outcome, recent findings have highlighted discrepancies between low voltage areas (LVAs) in EAM and LGE-areas. Explore the relationship between LGE regions and unipolar and bipolar-LVAs utilizing multipolar high-density (HD) mapping. 20 patients scheduled for AF ablation underwent pre-ablation LGE-MRI. LA segmentation was conducted using a deep learning approach, which subsequently generated a 3D mesh integrating the LGE data. HD-EAM was performed in sinus rhythm for each patient. The EAM map and LGE-MRI mesh were co-registered. LVAs were defined using voltage cut-offs of 0.5mV for bipolar and 2.5mV for unipolar. Correspondence between LGE-areas and LVAs in the LA was analyzed using confusion matrices and performance metrics. A considerable 87.3% of LGE regions overlapped with unipolar-LVAs, compared to only 16.2% overlap observed with bipolar-LVAs. Across all performance metrics, unipolar-LVAs outperformed bipolar-LVAs in identifying LGE-areas [precision (78.6% vs. 61.1%); sensitivity (87.3% vs. 16.2%); F1 score (81.3% vs. 26.0%); accuracy (74.0% vs. 35.3%)]. Our findings demonstrate that unipolar-LVAs highly correlate with LGE regions. These findings support the integration of unipolar mapping alongside bipolar mapping into clinical practice. This would offer a nuanced approach to diagnose and manage atrial fibrillation by revealing critical insights into the complex architecture of the atrial substrate.

Sections du résumé

BACKGROUND BACKGROUND
In atrial fibrillation (AF) management, understanding left atrial (LA) substrate is crucial. While both electroanatomical mapping (EAM) and late gadolinium enhancement MRI (LGE-MRI) are accepted methods for assessing the atrial substrate and are associated with ablation outcome, recent findings have highlighted discrepancies between low voltage areas (LVAs) in EAM and LGE-areas.
OBJECTIVE OBJECTIVE
Explore the relationship between LGE regions and unipolar and bipolar-LVAs utilizing multipolar high-density (HD) mapping.
METHODS METHODS
20 patients scheduled for AF ablation underwent pre-ablation LGE-MRI. LA segmentation was conducted using a deep learning approach, which subsequently generated a 3D mesh integrating the LGE data. HD-EAM was performed in sinus rhythm for each patient. The EAM map and LGE-MRI mesh were co-registered. LVAs were defined using voltage cut-offs of 0.5mV for bipolar and 2.5mV for unipolar. Correspondence between LGE-areas and LVAs in the LA was analyzed using confusion matrices and performance metrics.
RESULTS RESULTS
A considerable 87.3% of LGE regions overlapped with unipolar-LVAs, compared to only 16.2% overlap observed with bipolar-LVAs. Across all performance metrics, unipolar-LVAs outperformed bipolar-LVAs in identifying LGE-areas [precision (78.6% vs. 61.1%); sensitivity (87.3% vs. 16.2%); F1 score (81.3% vs. 26.0%); accuracy (74.0% vs. 35.3%)].
CONCLUSION CONCLUSIONS
Our findings demonstrate that unipolar-LVAs highly correlate with LGE regions. These findings support the integration of unipolar mapping alongside bipolar mapping into clinical practice. This would offer a nuanced approach to diagnose and manage atrial fibrillation by revealing critical insights into the complex architecture of the atrial substrate.

Identifiants

DOI: 10.1016/j.hrthm.2024.10.015 PMID: 39396602
pubmed: 39396602
pii: S1547-5271(24)03430-1
doi: 10.1016/j.hrthm.2024.10.015
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024. Published by Elsevier Inc.

Auteurs

Syed Yusuf Ali (SY)

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

Yazan Mohsen (Y)

Department of Biomedical Engineering and Medicine, Johns Hopkins University, Baltimore, MD, USA; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA; Department of Cardiology, Faculty of Health, School of Medicine, University Witten/Herdecke, Witten, Germany.

Yuncong Mao (Y)

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

Kensuke Sakata (K)

Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.

Eugene G Kholmovski (EG)

Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.

Adityo Prakosa (A)

Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.

Carolyna Yamamoto (C)

Department of Biomedical Engineering and Medicine, Johns Hopkins University, Baltimore, MD, USA; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.

Shane Loeffler (S)

Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA.

Marianna Elia (M)

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

Ghazal Zandieh (G)

Department of Radiology, Johns Hopkins Hospital, Baltimore, MD, USA.

Florian Stöckigt (F)

Department of Cardiology, University Hospital Bonn, Bonn, Germany.

Marc Horlitz (M)

Department of Cardiology, University Hospital Bonn, Bonn, Germany.

Sunil Kumar Sinha (SK)

Department of Cardiology, Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, MD, USA.

Joseph Marine (J)

Department of Cardiology, Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, MD, USA.

Hugh Calkins (H)

Department of Cardiology, Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, MD, USA.

Philipp Sommer (P)

Clinic for Electrophysiology, Herz- und Diabeteszentrum NRW, Ruhr- Universität Bochum, Bad Oeynhausen, Germany.

Vanessa Sciacca (V)

Clinic for Electrophysiology, Herz- und Diabeteszentrum NRW, Ruhr- Universität Bochum, Bad Oeynhausen, Germany.

Thomas Fink (T)

Clinic for Electrophysiology, Herz- und Diabeteszentrum NRW, Ruhr- Universität Bochum, Bad Oeynhausen, Germany.

Christian Sohns (C)

Clinic for Electrophysiology, Herz- und Diabeteszentrum NRW, Ruhr- Universität Bochum, Bad Oeynhausen, Germany.

David Spragg (D)

Department of Cardiology, Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, MD, USA.

Natalia Trayanova (N)

Department of Biomedical Engineering and Medicine, Johns Hopkins University, Baltimore, MD, USA; Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, MD, USA. Electronic address: ntrayanova@jhu.edu.

Classifications MeSH