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Sim I, Razeghi O, Solis Lemus JA, Mukherjee R, O’hare D, O’neill L, Kotadia I, Roney CH, Wright M, Chiribiri A, Niederer S, O’neill M, Williams SE. Atrial tissue characterisation using electroanatomic voltage mapping and cardiac magnetic resonance imaging. Europace 2022. [DOI: 10.1093/europace/euac053.177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: Foundation. Main funding source(s): British Heart Foundation
Background
Atrial voltage mapping and atrial cardiac magnetic resonance imaging are two contemporary methods for quantification of atrial fibrosis. However, the absence of a gold standard for measuring atrial fibrosis has precluded their direct comparison. Nevertheless, understanding the relative performance of voltage mapping and atrial late gadolinium enhancement for identification of atrial cardiomyopathy remains critical to correctly targeting clinical application of these techniques.
Purpose
To assess the relative performance of electroanatomic voltage mapping and atrial late gadolinium enhancement imaging using three surrogate markers chosen to distinguish pre-procedural utility (progression to recurrent atrial fibrillation following ablation) from potential utility for providing atrial fibrillation mechanistic insights (paroxysmal vs. persistent status of atrial fibrillation and relationship with co-morbidities associated with atrial fibrillation).
Methods
123 patients underwent atrial late gadolinium enhancement imaging and electroanatomic voltage mapping prior to atrial fibrillation ablation. Atrial late gadolinium enhancement imaging was assessed with CEMRG software and electroanatomic voltage mapping processed with OpenEP software using previously published thresholds. Low voltage tissue was defined at (1) <0.5mV, (2) <1.17mV, and (3) <1.3mV. Atrial fibrosis using late gadolinium enhancement was defined using four thresholds (1) signal intensity >3.3 standard deviations above the blood pool mean; (2) image intensity ratio (IIR) 1.2x blood pool mean; (3) IIR 1.32x blood pool mean; and (4) IIR 0.97x blood pool mean.
Results
Patients with persistent atrial fibrillation and those with CHA2DS2VaSc >2 had increased low voltage area for each of the thresholds tested, but there was no increase in atrial late gadolinium enhancement area at any of the imaging thresholds tested.
Increased atrial fibrosis using IIR>0.97 was independently associated with recurrence of atrial fibrillation (OR 1.05 (CI 1.01-1.09), p=0.009) in both univariate and multivariate analysis. Low voltage area <1.13mV and low voltage area <1.17mV were associated with increased risk of recurrence (OR 1.02 (CI 1.01-1.04), p=0.01, and OR 1.03 (CI 1.01-1.04), p=0.009) in univariate analysis but neither voltage threshold remained statistically significant in multivariate analysis controlling for clinical variables.
Conclusion
Increased fibrosis burden measured with atrial magnetic resonance imaging, but not with low voltage area, is independently associated with recurrence of atrial fibrillation following catheter ablation. However, increased low voltage area measured with electroanatomic mapping is associated with persistent atrial fibrillation status and CHADS2VaSc score. These findings support the use of magnetic resonance imaging for pre-procedure assessment and the use of electroanatomic mapping for intraprocedural mechanism-based assessment of atrial cardiomyopathy.
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Affiliation(s)
- I Sim
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - O Razeghi
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - JA Solis Lemus
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - R Mukherjee
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - D O’hare
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - L O’neill
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - I Kotadia
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - CH Roney
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - M Wright
- St Thomas’ Hospital, Cardiology, London, United Kingdom of Great Britain & Northern Ireland
| | - A Chiribiri
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - S Niederer
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - M O’neill
- Kings College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - SE Williams
- University of Edinburgh, Edinburgh, United Kingdom of Great Britain & Northern Ireland
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Williams S, Roney CH, Connolly A, Smith P, Bishop M, Niederer S, Whitaker J, Corrado C, Kotadia I, O’hare D, Fitzpatrick N, Sim I, O’neill M. Interpolation of electrophysiology parameters using OpenEP: technology development and clinical application. Europace 2022. [DOI: 10.1093/europace/euac053.049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Abstract
Funding Acknowledgements
Type of funding sources: Foundation. Main funding source(s): British Heart Foundation
Background
Interpolation of data is common during clinical electrophysiology procedures. Applications include local activation mapping, voltage mapping and novel techniques including Sparkle and Coherence mapping. Nevertheless, underlying interpolation algorithms are proprietary and therefore challenging to reproduce. Importantly, direct comparison of electroanatomic datasets between system vendors is therefore not possible.
Purpose
We sought to (1) develop an open-source architecture for interpolation within the Open Electrophysiology Framework for Research (OpenEP; https://openep.io); (2) to provide three interpolation methods within this architecture and (3) to evaluate their performance against clinical data.
Method
The software architecture is shown in Figure 1A. The currently available methods are Radial Basis [1], Scattered Interpolant [2] and Local Smoothing [3]. Default parameters for each method are shown in Figure 1B.
The performance of each method was assessed using clinical left atrial mapping data, using the default options for each scheme. Following interpolation, a sample of 1000 activation/voltage points per mesh was used for analysis. For each interpolation method, correlation with clinical data was assessed using the intra-class correlation coefficient, whilst agreement was assessed using Bland Altman limits of agreement.
Results
For activation mapping, radial basis interpolation resulted in a smoother field than local smoothing, whilst scattered interpolation required more filtering of extreme values. Correlations between interpolated and original activation times were excellent for all interpolation schemes (radial basis R=0.91, p<0.0001; local smoothing R=0.95, p<0.0001; scattered interpolant R=0.92, p<0.0001). Local smoothing resulted in the narrowest 95 percent limits of agreement (-19 to +20ms), compared to radial basis (-24 to +28ms) and scattered interpolation (-22 to +25ms).
For voltage mapping, the interpolation schemes resulted in similar appearances of low voltage areas, however correlations with clinical data were weaker than for activation mapping (radial basis R=0.84, p<0.0001; local smoothing R=0.82, p<0.0001; scattered interpolant R=0.79, p<0.0001). The 95 percent limits of agreement were wide as a proportion of the mean data values, ranging from 83% (-0.8 to +0.66mV) for local smoothing to 97% (-0.78 to +0.63mV) for radial basis interpolation.
Conclusion
An extensible architecture is provided for data interpolation in OpenEP together with three interpolation methods. The methods performed wellfor local activation time interpolation but variation compared to clinical data was greater for voltage mapping. This new architecture will permit the optimisation of interpolation methods against "gold standard" simulation or histological data and facilitate comparison of datasets between system vendors.
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Affiliation(s)
- S Williams
- University of Edinburgh, Edinburgh, United Kingdom of Great Britain & Northern Ireland
| | - CH Roney
- Queen Mary University of London, London, United Kingdom of Great Britain & Northern Ireland
| | - A Connolly
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - P Smith
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - M Bishop
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - S Niederer
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - J Whitaker
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - C Corrado
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - I Kotadia
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - D O’hare
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - N Fitzpatrick
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - I Sim
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
| | - M O’neill
- King’s College London, Biomedical Engineering and Imaging Sciences, London, United Kingdom of Great Britain & Northern Ireland
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