Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies
Summary: Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
|
| Series: | EBioMedicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396422004923 |
| _version_ | 1828198558732386304 |
|---|---|
| author | Carolyna Yamamoto Natalia A. Trayanova |
| author_facet | Carolyna Yamamoto Natalia A. Trayanova |
| author_sort | Carolyna Yamamoto |
| collection | DOAJ |
| description | Summary: Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice. |
| first_indexed | 2024-04-12T10:40:10Z |
| format | Article |
| id | doaj.art-418f7506c3914192af8a84983a5d2902 |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-04-12T10:40:10Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-418f7506c3914192af8a84983a5d29022022-12-22T03:36:37ZengElsevierEBioMedicine2352-39642022-11-0185104310Atrial fibrillation: Insights from animal models, computational modeling, and clinical studiesCarolyna Yamamoto0Natalia A. Trayanova1Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USADepartment of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Alliance for Cardiovascular Diagnostic and Treatment Innovation (ADVANCE), Johns Hopkins University, Baltimore, MD, USA; Corresponding author. Johns Hopkins, Johns Hopkins University, United States.Summary: Atrial fibrillation (AF) is the most common human arrhythmia, affecting millions of patients worldwide. A combination of risk factors and comorbidities results in complex atrial remodeling, which increases AF vulnerability and persistence. Insights from animal models, clinical studies, and computational modeling have advanced the understanding of the mechanisms and pathophysiology of AF. Areas of heterogeneous pathological remodeling, as well as altered electrophysiological properties, serve as a substrate for AF drivers and spontaneous activations. The complex and individualized presentation of this arrhythmia suggests that mechanisms-based personalized approaches will likely be needed to overcome current challenges in AF management. In this paper, we review the insights on the mechanisms of AF obtained from animal models and clinical studies and how computational models integrate this knowledge to advance AF clinical management. We also assess the challenges that need to be overcome to implement these mechanistic models in clinical practice.http://www.sciencedirect.com/science/article/pii/S2352396422004923Atrial fibrillationReentrant driversArrhythmia mechanismsPersonalized computational modelingPulmonary vein isolation |
| spellingShingle | Carolyna Yamamoto Natalia A. Trayanova Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies EBioMedicine Atrial fibrillation Reentrant drivers Arrhythmia mechanisms Personalized computational modeling Pulmonary vein isolation |
| title | Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies |
| title_full | Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies |
| title_fullStr | Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies |
| title_full_unstemmed | Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies |
| title_short | Atrial fibrillation: Insights from animal models, computational modeling, and clinical studies |
| title_sort | atrial fibrillation insights from animal models computational modeling and clinical studies |
| topic | Atrial fibrillation Reentrant drivers Arrhythmia mechanisms Personalized computational modeling Pulmonary vein isolation |
| url | http://www.sciencedirect.com/science/article/pii/S2352396422004923 |
| work_keys_str_mv | AT carolynayamamoto atrialfibrillationinsightsfromanimalmodelscomputationalmodelingandclinicalstudies AT nataliaatrayanova atrialfibrillationinsightsfromanimalmodelscomputationalmodelingandclinicalstudies |