Electrocardiographic Imaging for Atrial Fibrillation A Perspective From Computer Models and Animal Experiments to Clinical Value_fphys-12-653013.pdf (3.31 MB)
Electrocardiographic Imaging for Atrial Fibrillation: A Perspective From Computer Models and Animal Experiments to Clinical Value
journal contributionposted on 2022-06-23, 10:02 authored by João Salinet, Rubén Molero, Fernando S Schlindwein, Joël Karel, Miguel Rodrigo, José Luis Rojo-Álvarez, Omer Berenfeld, Andreu M Climent, Brian Zenger, Frederique Vanheusden, Jimena Gabriela Siles Paredes, Rob MacLeod, Felipe Atienza, María S Guillem, Matthijs Cluitmans, Pietro Bonizzi
Electrocardiographic imaging (ECGI) is a technique to reconstruct non-invasively the electrical activity on the heart surface from body-surface potential recordings and geometric information of the torso and the heart. ECGI has shown scientific and clinical value when used to characterize and treat both atrial and ventricular arrhythmias. Regarding atrial fibrillation (AF), the characterization of the electrical propagation and the underlying substrate favoring AF is inherently more challenging than for ventricular arrhythmias, due to the progressive and heterogeneous nature of the disease and its manifestation, the small volume and wall thickness of the atria, and the relatively large role of microstructural abnormalities in AF. At the same time, ECGI has the advantage over other mapping technologies of allowing a global characterization of atrial electrical activity at every atrial beat and non-invasively. However, since ECGI is time-consuming and costly and the use of electrical mapping to guide AF ablation is still not fully established, the clinical value of ECGI for AF is still under assessment. Nonetheless, AF is known to be the manifestation of a complex interaction between electrical and structural abnormalities and therefore, true electro-anatomical-structural imaging may elucidate important key factors of AF development, progression, and treatment. Therefore, it is paramount to identify which clinical questions could be successfully addressed by ECGI when it comes to AF characterization and treatment, and which questions may be beyond its technical limitations. In this manuscript we review the questions that researchers have tried to address on the use of ECGI for AF characterization and treatment guidance (for example, localization of AF triggers and sustaining mechanisms), and we discuss the technological requirements and validation. We address experimental and clinical results, limitations, and future challenges for fruitful application of ECGI for AF understanding and management. We pay attention to existing techniques and clinical application, to computer models and (animal or human) experiments, to challenges of methodological and clinical validation. The overall objective of the study is to provide a consensus on valuable directions that ECGI research may take to provide future improvements in AF characterization and treatment guidance.
JS was supported by Grants #2020/12841-3, #2020/13017-2, and #2018/25606-2, São Paulo Research Foundation (FAPESP). FS was supported by research grants from the Medical Research Council UK (MR/S037306/1), and from the British Heart Foundation (BHF Project Grant No. PG/18/33/33780). JR-Á was supported by the projects (PID2019-106623RB-C41) and meHeart (PID2019-104356RB-C42 and PID2019-104356RB-C43) from the Spanish Government. JP was funded by Program of Alliances for Education and Training (Scholarship Brazil—PAEC OEA-GCUB-2017). RM, AC, and MG were supported by the Instituto de Salud Carlos III, and Ministerio de Ciencia, Innovación y Universidades (supported by the FEDER Fondo Europeo de Desarrollo Regional PI17/01106 and RYC2018-024346B-750), EIT Health (Activity code 19600, EIT Health was supported by the EIT, a body of the European Union), received funding from the European Union’s Horizon research and Innovation programme under the Marie Skłodowska-Curie grant agreement no. 860974 and the Generalitat Valenciana Grants (ACIF/2020/265). BZ and RM were supported by the NIH NHLBI Grant No. 1F30HL149327; NIH NIGMS Center for Integrative Biomedical Computing (www.sci.utah.edu/cibc), NIH NIGMS Grants P41GM103545 and R24 GM136986; and the Nora Eccles Treadwell Foundation for Cardiovascular Research. OB was supported in part by research grants from the National Institutes of Health (R01-HL118304 and R21-HL153694), Abbott, Medtronic, and CoreMap. MC was supported by a Veni Grant from the Netherlands Organization for Scientific Research (TTW 16772). FA was supported by the Instituto de Salud Carlos III (PI17/010559 and PI20/01618) and the Consorcio de Investigación Biomédica en Red de Enfermedades Cardiovasculares (CIBERCV), the Ministerio de Ciencia e Innovación (cofound by FEDER: Fondo Europeo de Desarrollo Regional), EIT Health (AFFINE 19600, EIT Health was supported by EIT, a body of the European Union).
CitationSalinet, João, et al. "Electrocardiographic imaging for atrial fibrillation: a perspective from computer models and animal experiments to clinical value." Frontiers in physiology 12 (2021): 653013.
Author affiliationSchool of Engineering
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