Quantifying a spectrum of clinical response in atrial tachyarrhythmias using spatiotemporal synchronization of electrograms

Author:

Ganesan Prasanth1ORCID,Deb Brototo1ORCID,Feng Ruibin1ORCID,Rodrigo Miguel2ORCID,Ruiperez-Campillo Samuel23ORCID,Rogers Albert J1ORCID,Clopton Paul1ORCID,Wang Paul J1ORCID,Zeemering Stef4ORCID,Schotten Ulrich4ORCID,Rappel Wouter-Jan5ORCID,Narayan Sanjiv M1ORCID

Affiliation:

1. Division of Cardiovascular Medicine, Stanford Cardiovascular Institute, Stanford University , 453 Quarry Road, Palo Alto, CA 94304 , USA

2. Electronic Engineering Department, Universitat de Valencia , Av. de Blasco Ibáñez, 13, 46010 València , Spain

3. Department of Bioengineering, University of California , Berkeley, CA 94720 , USA

4. Department of Physiology, Maastricht University , 6211 LK Maastricht, 616 6200 , Netherlands

5. Department of Physics, University of California , 9500 Gilman Dr, La Jolla, CA 92093 , USA

Abstract

Abstract Aims There is a clinical spectrum for atrial tachyarrhythmias wherein most patients with atrial tachycardia (AT) and some with atrial fibrillation (AF) respond to ablation, while others do not. It is undefined if this clinical spectrum has pathophysiological signatures. This study aims to test the hypothesis that the size of spatial regions showing repetitive synchronized electrogram (EGM) shapes over time reveals a spectrum from AT, to AF patients who respond acutely to ablation, to AF patients without acute response. Methods and results We studied n = 160 patients (35% women, 65.0 ± 10.4 years) of whom (i) n = 75 had AF terminated by ablation propensity matched to (ii) n = 75 without AF termination and (iii) n = 10 with AT. All patients had mapping by 64-pole baskets to identify areas of repetitive activity (REACT) to correlate unipolar EGMs in shape over time. Synchronized regions (REACT) were largest in AT, smaller in AF termination, and smallest in non-termination cohorts (0.63 ± 0.15, 0.37 ± 0.22, and 0.22 ± 0.18, P < 0.001). Area under the curve for predicting AF termination in hold-out cohorts was 0.72 ± 0.03. Simulations showed that lower REACT represented greater variability in clinical EGM timing and shape. Unsupervised machine learning of REACT and extensive (50) clinical variables yielded four clusters of increasing risk for AF termination (P < 0.01, χ2), which were more predictive than clinical profiles alone (P < 0.001). Conclusion The area of synchronized EGMs within the atrium reveals a spectrum of clinical response in atrial tachyarrhythmias. These fundamental EGM properties, which do not reflect any predetermined mechanism or mapping technology, predict outcome and offer a platform to compare mapping tools and mechanisms between AF patient groups.

Funder

National Institutes of Health

Machine Learning in Atrial Fibrillation

Dynamics of Atrial Fibrillation

Publisher

Oxford University Press (OUP)

Subject

Physiology (medical),Cardiology and Cardiovascular Medicine

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