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\n\n \n \n 2025\n \n \n (2)\n \n \n
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\n \n \n\n \n \n \n \n \n Quantification of atrial cardiomyopathy disease severity by electroanatomic voltage mapping and cardiac magnetic resonance imaging.\n \n \n \n\n\n \n Sim, I.; Lemus, J. A. S.; O'Shea, C.; Razeghi, O.; Whitaker, J.; Mukherjee, R.; O'Hare, D.; Fitzpatrick, N.; Harrison, J.; Gharaviri, A.; O'Neill, L.; Kotadia, I.; Roney, C. H.; Grubb, N.; Newby, D. E.; Dweck, M. R.; Masci, P. G.; Wright, M.; Chiribiri, A.; Niederer, S.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n J Cardiovasc Electrophysiol, 36(2): 467-479. 2025.\n \n\n\n\n
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\n\n\n@article{RN591,\n author = {Sim, I. and Lemus, J. A. S. and O'Shea, C. and Razeghi, O. and Whitaker, J. and Mukherjee, R. and O'Hare, D. and Fitzpatrick, N. and Harrison, J. and Gharaviri, A. and O'Neill, L. and Kotadia, I. and Roney, C. H. and Grubb, N. and Newby, D. E. and Dweck, M. R. and Masci, P. G. and Wright, M. and Chiribiri, A. and Niederer, S. and O'Neill, M. and Williams, S. E.},\n title = {Quantification of atrial cardiomyopathy disease severity by electroanatomic voltage mapping and cardiac magnetic resonance imaging},\n journal = {J Cardiovasc Electrophysiol},\n volume = {36},\n number = {2},\n pages = {467-479},\n abstract = {INTRODUCTION: Atrial late gadolinium enhancement (Atrial-LGE) and electroanatomic voltage mapping (Atrial-EAVM) quantify the anatomical and functional extent of atrial cardiomyopathy. We aimed to explore the relationships between, and outcomes from, these modalities in patients with atrial fibrillation undergoing ablation. METHODS: Patients undergoing first-time ablation had disease severities quantified using both Atrial-LGE and Atrial-EAVM. Correlations between modalities and their relationships with clinical features and arrhythmia recurrence were assessed. RESULTS: In 123 atrial fibrillation patients (60 +/- 10 years), Atrial-EAVM was moderately correlated with Atrial-LGE (r = .34, p < .001), with a mean fibrosis burden of 47.2% +/- 14.91%. Agreement was strongest in the highest tertile of fibrosis burden (mean of differences 16.8% (95% CI = -24.4% to 57.9%, p = .433). Fibrosis burden was greater for Atrial-LGE than Atrial-EAVM (50.7% +/- 10.7% vs. 13.7% +/- 7.13%, p < .005) for patients in the lowest tertile who were younger, had smaller atria and a greater frequency of paroxysmal atrial fibrillation. Both Atrial EAVM and Atrial LGE were associated with recurrence of arrhythmia following ablation (Atrial-LGE HR = 1.02 (95% CI = 1.01-1.04), p = .047; Atrial-EAVM HR = 1.02 (95% CI = 1.005-1.03), p = .007). A low fibrosis burden (<15%) by Atrial-EAVM identified patients with very low arrhythmia recurrence. In contrast, a much higher fibrosis burden (>66%) by Atrial-LGE identified patients failing to respond to ablation. CONCLUSIONS: We demonstrate for the first time that the level of agreement between Atrial-EAVM and Atrial-LGE is dependent on the level of atrial cardiomyopathy disease severity. The functional consequences of atrial cardiomyopathy are most evident in patients with the highest anatomical extent of disease.},\n DOI = {10.1111/jce.16462},\n year = {2025},\n type = {Journal Article}\n}\n\n\n\n INTRODUCTION: Atrial late gadolinium enhancement (Atrial-LGE) and electroanatomic voltage mapping (Atrial-EAVM) quantify the anatomical and functional extent of atrial cardiomyopathy. We aimed to explore the relationships between, and outcomes from, these modalities in patients with atrial fibrillation undergoing ablation. METHODS: Patients undergoing first-time ablation had disease severities quantified using both Atrial-LGE and Atrial-EAVM. Correlations between modalities and their relationships with clinical features and arrhythmia recurrence were assessed. RESULTS: In 123 atrial fibrillation patients (60 +/- 10 years), Atrial-EAVM was moderately correlated with Atrial-LGE (r = .34, p < .001), with a mean fibrosis burden of 47.2% +/- 14.91%. Agreement was strongest in the highest tertile of fibrosis burden (mean of differences 16.8% (95% CI = -24.4% to 57.9%, p = .433). Fibrosis burden was greater for Atrial-LGE than Atrial-EAVM (50.7% +/- 10.7% vs. 13.7% +/- 7.13%, p < .005) for patients in the lowest tertile who were younger, had smaller atria and a greater frequency of paroxysmal atrial fibrillation. Both Atrial EAVM and Atrial LGE were associated with recurrence of arrhythmia following ablation (Atrial-LGE HR = 1.02 (95% CI = 1.01-1.04), p = .047; Atrial-EAVM HR = 1.02 (95% CI = 1.005-1.03), p = .007). A low fibrosis burden (<15%) by Atrial-EAVM identified patients with very low arrhythmia recurrence. In contrast, a much higher fibrosis burden (>66%) by Atrial-LGE identified patients failing to respond to ablation. CONCLUSIONS: We demonstrate for the first time that the level of agreement between Atrial-EAVM and Atrial-LGE is dependent on the level of atrial cardiomyopathy disease severity. The functional consequences of atrial cardiomyopathy are most evident in patients with the highest anatomical extent of disease.\n
\n\n\n\n \n\n \n \n \n \n \n Performance of atrial conduction velocity algorithms with error-prone clinical measurements for the identification of atrial fibrosis.\n \n \n \n\n\n \n Gharaviri, A.; Vigneswaran, V.; Vickneson, K.; Roney, C.; Corrado, C.; Coveney, S.; Maciunas, K.; Bodagh, N.; Klis, M.; Kotadia, I.; Sim, I.; Whitaker, J.; Bishop, M.; Niederer, S.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n Comput Biol Med, 191: 110119. 2025.\n \n\n\n\n
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\n\n\n@article{RN694,\n author = {Gharaviri, A. and Vigneswaran, V. and Vickneson, K. and Roney, C. and Corrado, C. and Coveney, S. and Maciunas, K. and Bodagh, N. and Klis, M. and Kotadia, I. and Sim, I. and Whitaker, J. and Bishop, M. and Niederer, S. and O'Neill, M. and Williams, S. E.},\n title = {Performance of atrial conduction velocity algorithms with error-prone clinical measurements for the identification of atrial fibrosis},\n journal = {Comput Biol Med},\n volume = {191},\n pages = {110119},\n abstract = {INTRODUCTION: Measuring conduction velocity, as a direct consequence of fibrosis, may provide a better method to localise fibrotic regions. This study aims to assess established cardiac conduction velocity calculation methods (Triangulation, Polynomial Surface Fitting, and Radial Basis Function) in identifying areas of conduction slowing caused by fibrosis, considering realistic measurement errors. METHOD: Using a human left atrium computational model, atrial activation was simulated. Each conduction velocity calculation method's performance was evaluated under uncertainties in mapping point density, local activation time assignment and electrode locations by comparing calculated conduction velocity to ground truth conduction velocity derived from high-resolution simulated atrial activation. RESULTS: All methods agreed well with ground truth conduction velocity maps in noise-free, high-density sampling conditions. However, Triangulation and Polynomial Surface Fitting methods showed susceptibility to noise, exhibiting significant errors under moderate to high noise levels. Radial Basis Function method demonstrated greater robustness to noise and reduced sampling density. Fibrotic region identification accuracy was high under ideal conditions for all methods but declined with increasing noise, with the Radial Basis Function method maintaining superior performance. CONCLUSION: While all methods accurately estimate conduction velocity under ideal conditions, the Radial Basis Function method shows robustness to a realistic clinical noise, hence making it the most suitable to identify fibrotic regions.},\n DOI = {10.1016/j.compbiomed.2025.110119},\n year = {2025},\n type = {Journal Article}\n}\n\n\n\n INTRODUCTION: Measuring conduction velocity, as a direct consequence of fibrosis, may provide a better method to localise fibrotic regions. This study aims to assess established cardiac conduction velocity calculation methods (Triangulation, Polynomial Surface Fitting, and Radial Basis Function) in identifying areas of conduction slowing caused by fibrosis, considering realistic measurement errors. METHOD: Using a human left atrium computational model, atrial activation was simulated. Each conduction velocity calculation method's performance was evaluated under uncertainties in mapping point density, local activation time assignment and electrode locations by comparing calculated conduction velocity to ground truth conduction velocity derived from high-resolution simulated atrial activation. RESULTS: All methods agreed well with ground truth conduction velocity maps in noise-free, high-density sampling conditions. However, Triangulation and Polynomial Surface Fitting methods showed susceptibility to noise, exhibiting significant errors under moderate to high noise levels. Radial Basis Function method demonstrated greater robustness to noise and reduced sampling density. Fibrotic region identification accuracy was high under ideal conditions for all methods but declined with increasing noise, with the Radial Basis Function method maintaining superior performance. CONCLUSION: While all methods accurately estimate conduction velocity under ideal conditions, the Radial Basis Function method shows robustness to a realistic clinical noise, hence making it the most suitable to identify fibrotic regions.\n
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\n\n \n \n 2024\n \n \n (10)\n \n \n
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\n \n \n\n \n \n \n \n \n Enhancing OpenEP: atrial conduction velocity and conduction velocity heterogeneity quantification through EP Workbench.\n \n \n \n\n\n \n Vigneswaran, V; Gharaviri, A G; Klis, M K; Bodagh, N B; Sim, I S; Roney, C R; Kotadia, I K; Niederer, S N; O Neill, M O; and Williams, S W\n\n\n \n\n\n\n EP Europace, 26(Supplement_1). 2024.\n \n\n\n\n
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\n\n\n@article{RN598,\n author = {Vigneswaran, V and Gharaviri, A G and Klis, M K and Bodagh, N B and Sim, I S and Roney, C R and Kotadia, I K and Niederer, S N and O Neill, M O and Williams, S W},\n title = {Enhancing OpenEP: atrial conduction velocity and conduction velocity heterogeneity quantification through EP Workbench},\n journal = {EP Europace},\n volume = {26},\n number = {Supplement_1},\n abstract = {Alterations in atrial conduction velocity have been implicated in the pathogenesis of atrial arrhythmias, with conduction velocity thought to be slower and more heterogeneous in areas promoting atrial fibrillation. However, clinical studies of conduction velocity are challenging since there are no available platforms that provide researchers and clinicians with the ability to quantify conduction velocity and analyse conduction heterogeneity from electroanatomic mapping data.In this paper we sought to address these limitations by enhancing the OpenEP Python library by (1) automating calculations using previously-published conduction velocity estimation algorithms; (2) providing an interactive graphical representing of conduction velocity and conduction heterogeneity and (3) implementing a histogram analysis tool to quantify conduction velocity and enable the identification of slow conduction regions.Two well-known and previously published methods for calculating cardiac conduction velocity, Triangulation and polynomial surface fitting, were implemented. Conduction velocity estimation was improved further by excluding regions with wave collisions and focal discharges. These regions were identified using the divergence of conduction velocity vector fields, with positive divergence representing focal sources and negative divergence representing areas of collision. Finally, calculated conduction velocities using electrogram data were interpolated over the surface mesh via Radial Basis Function (RBF) interpolation method. All algorithms were implemented in the OpenEP Python library (openep-py), with visualisation tools provided in the complementary graphical interface, EP Workbench.An extensible "Analysis" feature was created in openep-py, to provide conduction velocity and divergence calculations which are fully customisable through the graphical interface based on user preferences. The EP Workbench desktop application displays resulting cardiac surface maps to depict calculated conduction velocity and divergence fields, together with conventional activation and voltage maps (Figure 1).The histogram analysis tool can be used to identify regions of slow conduction velocity from electroanatomic mapping data. Using a threshold of <0.3m/s, two slow conducting channels are visualised on the posterior wall of a test case (Figure 2). The tool is not specific to conduction velocity and may also be used to quantify other data types in EP Workbench.Finally, computed conduction velocity and divergence values and surface maps are stored in the standard OpenEP data structure, allowing further analysis following data export.We have extended OpenEP to provide a comprehensive set of tools for conduction velocity calculation and analysis which will facilitate investigation of the structural and functional basis of conduction velocity alterations in disease states.},\n DOI = {10.1093/europace/euae102.626},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n Alterations in atrial conduction velocity have been implicated in the pathogenesis of atrial arrhythmias, with conduction velocity thought to be slower and more heterogeneous in areas promoting atrial fibrillation. However, clinical studies of conduction velocity are challenging since there are no available platforms that provide researchers and clinicians with the ability to quantify conduction velocity and analyse conduction heterogeneity from electroanatomic mapping data.In this paper we sought to address these limitations by enhancing the OpenEP Python library by (1) automating calculations using previously-published conduction velocity estimation algorithms; (2) providing an interactive graphical representing of conduction velocity and conduction heterogeneity and (3) implementing a histogram analysis tool to quantify conduction velocity and enable the identification of slow conduction regions.Two well-known and previously published methods for calculating cardiac conduction velocity, Triangulation and polynomial surface fitting, were implemented. Conduction velocity estimation was improved further by excluding regions with wave collisions and focal discharges. These regions were identified using the divergence of conduction velocity vector fields, with positive divergence representing focal sources and negative divergence representing areas of collision. Finally, calculated conduction velocities using electrogram data were interpolated over the surface mesh via Radial Basis Function (RBF) interpolation method. All algorithms were implemented in the OpenEP Python library (openep-py), with visualisation tools provided in the complementary graphical interface, EP Workbench.An extensible \"Analysis\" feature was created in openep-py, to provide conduction velocity and divergence calculations which are fully customisable through the graphical interface based on user preferences. The EP Workbench desktop application displays resulting cardiac surface maps to depict calculated conduction velocity and divergence fields, together with conventional activation and voltage maps (Figure 1).The histogram analysis tool can be used to identify regions of slow conduction velocity from electroanatomic mapping data. Using a threshold of <0.3m/s, two slow conducting channels are visualised on the posterior wall of a test case (Figure 2). The tool is not specific to conduction velocity and may also be used to quantify other data types in EP Workbench.Finally, computed conduction velocity and divergence values and surface maps are stored in the standard OpenEP data structure, allowing further analysis following data export.We have extended OpenEP to provide a comprehensive set of tools for conduction velocity calculation and analysis which will facilitate investigation of the structural and functional basis of conduction velocity alterations in disease states.\n
\n\n\n\n \n\n \n \n \n \n \n Spatial relationship between left peri-atrial fat with underlying arrhythmogenic substrate in patients with atrial fibrillation - innocent bystander or culprit?.\n \n \n \n\n\n \n Vickneson, K; Tonko, J; Gharaviri, A; Williams, M C; Dweck, M; Vigneswaran, V; Baptiste, T; Alonso Solis Lemus, J; Corrado, C; Niederer, S; O'neill, M; Whitaker, J; and Williams, S E\n\n\n \n\n\n\n EP Europace, 26(Supplement_1). 2024.\n \n\n\n\n
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\n\n\n@article{RN597,\n author = {Vickneson, K and Tonko, J and Gharaviri, A and Williams, M C and Dweck, M and Vigneswaran, V and Baptiste, T and Alonso Solis Lemus, J and Corrado, C and Niederer, S and O'neill, M and Whitaker, J and Williams, S E},\n title = {Spatial relationship between left peri-atrial fat with underlying arrhythmogenic substrate in patients with atrial fibrillation - innocent bystander or culprit?},\n journal = {EP Europace},\n volume = {26},\n number = {Supplement_1},\n abstract = {Peri-atrial fat may have pro-inflammatory and pro-fibrotic effects on atrial myocardium and increase the risk of developing atrial fibrillation. Consistent with prior evidence, we have previously shown that peri-atrial fat volume, but not fat attenuation (an imaging biomarker of inflammation), was associated with prevalent atrial fibrillation. Whether this association is secondary to "outside-to-inside" tissue cross talk with local adverse electrical remodelling of adjacent atrial cardiomyocytes is not known.In this novel proof of concept study, we aimed to identify a spatial relationship between left peri-atrial fat and potentially arrhythmogenic substrate (low voltage areas and slow conduction pathways) in the left atrial myocardium.Contrast-enhanced cardiac computed tomography (CT) imaging and electroanatomic mapping, during coronary sinus pacing, were performed pre-ablation in atrial fibrillation patients (n=27). The left atrium and surrounding peri-atrial fat were segmented and quantified using seg3D2 and CEMRGapp. We performed registration of the electroanatomic map onto the CT-derived left atrial mesh using OpenEP and EP Workbench. Mean fat volume and attenuation around each electroanatomic point (within a 3mm radial region of interest) were correlated with local voltage and conduction velocity.A total of 24,839 electroanatomic points were registered onto the 3D CT-derived left atrial mesh, with a median of 935 points (range 673 to 1150) per patient. The average bipolar voltage and conduction velocity in the left atrium was 1.92±1.91 mV and 0.66±0.47 ms-1 respectively. In comparison to areas with little/no surrounding periatrial fat (Quartile 1, ≤0.93 mm3 fat per electroanatomic point), areas with the highest burden of fat (Quartile 4, ≥17.6 mm3) had significantly lower bipolar voltage (1.69±1.7 vs 2.09±2.0 mV, p<0.001) and conduction velocity (0.627±0.43 vs 0.700±0.47 ms-1, p<0.001). However, the overall correlation between periatrial fat volume with bipolar voltage (r=-0.06, p<0.001) and conduction velocity (r=-0.07, p<0.001) was weak. High, not low, fat attenuation (Quartile 4; >-46.5HU vs Quartile 1; ≤-75.5HU) was associated with increased bipolar voltage (2.23±2.1 vs 1.63±1.6 mV) and conduction velocity (0.703±0.42 vs 0.623±0.39 ms-1 respectively).There is a weak correlation between periatrial fat and localized adverse electrical remodelling. The relationship between fat attenuation and electrophysiological properties was inverted. These findings confirm an association between peri-atrial fat burden but call question as to whether peri-atrial fat inflammation is implicated in atrial fibrillation pathogenesis.},\n DOI = {10.1093/europace/euae102.636},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n Peri-atrial fat may have pro-inflammatory and pro-fibrotic effects on atrial myocardium and increase the risk of developing atrial fibrillation. Consistent with prior evidence, we have previously shown that peri-atrial fat volume, but not fat attenuation (an imaging biomarker of inflammation), was associated with prevalent atrial fibrillation. Whether this association is secondary to \"outside-to-inside\" tissue cross talk with local adverse electrical remodelling of adjacent atrial cardiomyocytes is not known.In this novel proof of concept study, we aimed to identify a spatial relationship between left peri-atrial fat and potentially arrhythmogenic substrate (low voltage areas and slow conduction pathways) in the left atrial myocardium.Contrast-enhanced cardiac computed tomography (CT) imaging and electroanatomic mapping, during coronary sinus pacing, were performed pre-ablation in atrial fibrillation patients (n=27). The left atrium and surrounding peri-atrial fat were segmented and quantified using seg3D2 and CEMRGapp. We performed registration of the electroanatomic map onto the CT-derived left atrial mesh using OpenEP and EP Workbench. Mean fat volume and attenuation around each electroanatomic point (within a 3mm radial region of interest) were correlated with local voltage and conduction velocity.A total of 24,839 electroanatomic points were registered onto the 3D CT-derived left atrial mesh, with a median of 935 points (range 673 to 1150) per patient. The average bipolar voltage and conduction velocity in the left atrium was 1.92±1.91 mV and 0.66±0.47 ms-1 respectively. In comparison to areas with little/no surrounding periatrial fat (Quartile 1, ≤0.93 mm3 fat per electroanatomic point), areas with the highest burden of fat (Quartile 4, ≥17.6 mm3) had significantly lower bipolar voltage (1.69±1.7 vs 2.09±2.0 mV, p<0.001) and conduction velocity (0.627±0.43 vs 0.700±0.47 ms-1, p<0.001). However, the overall correlation between periatrial fat volume with bipolar voltage (r=-0.06, p<0.001) and conduction velocity (r=-0.07, p<0.001) was weak. High, not low, fat attenuation (Quartile 4; >-46.5HU vs Quartile 1; ≤-75.5HU) was associated with increased bipolar voltage (2.23±2.1 vs 1.63±1.6 mV) and conduction velocity (0.703±0.42 vs 0.623±0.39 ms-1 respectively).There is a weak correlation between periatrial fat and localized adverse electrical remodelling. The relationship between fat attenuation and electrophysiological properties was inverted. These findings confirm an association between peri-atrial fat burden but call question as to whether peri-atrial fat inflammation is implicated in atrial fibrillation pathogenesis.\n
\n\n\n\n \n\n \n \n \n \n \n OP6 Non-invasive quantification of peri-atrial fat volume and inflammation in atrial fibrillation: the promise of peri-atrial fat as a marker of adverse electrophysiological remodelling.\n \n \n \n\n\n \n Vickneson, K.; Gharaviri, A.; Vigneswaran, V.; Tonko, J.; Bodagh, N.; Klis, M.; Kotadia, I.; Wright, M.; Newby, D. E; Dweck, M. R; Williams, M. C; O’Neill, M.; Whitaker, J.; and Williams, S. E\n\n\n \n\n\n\n Heart, 110(Suppl 4): A2-A3. 2024.\n \n\n\n\n
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\n\n\n@article{RN594,\n author = {Vickneson, Keeran and Gharaviri, Ali and Vigneswaran, Vinush and Tonko, Johanna and Bodagh, Neil and Klis, Magda and Kotadia, Irum and Wright, Matthew and Newby, David E and Dweck, Marc R and Williams, Michelle C and O’Neill, Mark and Whitaker, John and Williams, Steven E},\n title = {OP6 Non-invasive quantification of peri-atrial fat volume and inflammation in atrial fibrillation: the promise of peri-atrial fat as a marker of adverse electrophysiological remodelling},\n journal = {Heart},\n volume = {110},\n number = {Suppl 4},\n pages = {A2-A3},\n abstract = {Background Peri-atrial fat may have pro-inflammatory and pro-fibrotic effects on atrial myocardium and increase the risk of developing atrial fibrillation. Peri-atrial fat attenuation, detected by cardiac computed tomography angiography (CCTA), potentially captures chronic inflammation by mapping spatial changes of fat density.Whether this association is secondary to ‘outside-to-inside’ tissue cross talk with local adverse electrical remodelling of adjacent atrial cardiomyocytes is not known.Methods CCTA was performed pre-ablation in 37 controls and 44 patients with atrial fibrillation. The left atrium and surrounding peri-atrial fat was segmented and quantified using seg3D2 and CEMRG. A multivariable logistic regression analysis was performed to assess the association between peri-atrial fat with atrial fibrillation.Electroanatomic maps (bipolar voltage and conduction velocity) were spatially co-registered with CCTA- derived peri-atrial fat segmentations. We assessed the bystander effect of peri-atrial fat burden and attenuation on local bipolar voltage and conduction velocity.Results Patients with atrial fibrillation had greater peri-atrial fat burden (20.96.6 ml vs 14.27.0 cm 3 , adjusted odds ratio (AOR) per cm 3 1.12; 95% CI 1.01–1.25), after adjusting for age, sex, body mass index, cardiovascular risk factors, left atrial volume and mass. Areas of myocardium with the highest burden of peri-atrial fat exhibited lower voltage (1.751.72mV versus 2.112.02mV, p<0.001) and conduction velocity (0.6270.55 versus0.6830.48 ms -1 , p<0.001). Peri-atrial fat attenuation was similar in both groups. Areas with low fat attenuation was associated with reduced bipolar voltage (1.691.68mV versus 2.162.07mV, p<0.001) and conduction velocity (0.6150.47 versus 0.6840.43 ms-1, P=0.001).Conclusions Peri-atrial fat volume is associated with atrial fibrillation. Increased peri-atrial fat burden and reduced attenuation were spatially associated with adverse electrophysiological remodelling in patients with AF.},\n DOI = {10.1136/heartjnl-2024-BSCI.6},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n Background Peri-atrial fat may have pro-inflammatory and pro-fibrotic effects on atrial myocardium and increase the risk of developing atrial fibrillation. Peri-atrial fat attenuation, detected by cardiac computed tomography angiography (CCTA), potentially captures chronic inflammation by mapping spatial changes of fat density.Whether this association is secondary to ‘outside-to-inside’ tissue cross talk with local adverse electrical remodelling of adjacent atrial cardiomyocytes is not known.Methods CCTA was performed pre-ablation in 37 controls and 44 patients with atrial fibrillation. The left atrium and surrounding peri-atrial fat was segmented and quantified using seg3D2 and CEMRG. A multivariable logistic regression analysis was performed to assess the association between peri-atrial fat with atrial fibrillation.Electroanatomic maps (bipolar voltage and conduction velocity) were spatially co-registered with CCTA- derived peri-atrial fat segmentations. We assessed the bystander effect of peri-atrial fat burden and attenuation on local bipolar voltage and conduction velocity.Results Patients with atrial fibrillation had greater peri-atrial fat burden (20.96.6 ml vs 14.27.0 cm 3 , adjusted odds ratio (AOR) per cm 3 1.12; 95% CI 1.01–1.25), after adjusting for age, sex, body mass index, cardiovascular risk factors, left atrial volume and mass. Areas of myocardium with the highest burden of peri-atrial fat exhibited lower voltage (1.751.72mV versus 2.112.02mV, p<0.001) and conduction velocity (0.6270.55 versus0.6830.48 ms -1 , p<0.001). Peri-atrial fat attenuation was similar in both groups. Areas with low fat attenuation was associated with reduced bipolar voltage (1.691.68mV versus 2.162.07mV, p<0.001) and conduction velocity (0.6150.47 versus 0.6840.43 ms-1, P=0.001).Conclusions Peri-atrial fat volume is associated with atrial fibrillation. Increased peri-atrial fat burden and reduced attenuation were spatially associated with adverse electrophysiological remodelling in patients with AF.\n
\n\n\n\n \n\n \n \n \n \n \n Peri-atrial adipose tissue inflammation in atrial fibrillation: quantification of electrophysiological effects using electroanatomic mapping.\n \n \n \n\n\n \n Vickneson, K.; Gharaviri, A.; Vigneswaran, V.; Tonko, J.; Bodagh, N.; Klis, M.; Kotadia, I.; Wright, M.; Newby, D. E.; Dweck, M.; Williams, M. C.; O’Neill, M.; Whitaker, J.; and Williams, S. E.\n\n\n \n\n\n\n JACC Clin Electrophysiol, Under revision. 2024.\n \n\n\n\n
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\n\n\n\n@article{RN592,\n author = {Vickneson, K. and Gharaviri, A. and Vigneswaran, V. and Tonko, J. and Bodagh, N. and Klis, M. and Kotadia, I. and Wright, M. and Newby, D. E. and Dweck, M. and Williams, M. C. and O’Neill, Mark and Whitaker, J. and Williams, Steven E.},\n title = {Peri-atrial adipose tissue inflammation in atrial fibrillation: quantification of electrophysiological effects using electroanatomic mapping},\n journal = {JACC Clin Electrophysiol},\n volume = {Under revision},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n \n\n \n \n \n \n \n High Prevalence of New Clinically Significant Findings in Patients With Embolic Stroke of Unknown Source Evaluated by Cardiac Magnetic Resonance Imaging.\n \n \n \n\n\n \n Kotadia, I. D.; O'Dowling, R.; Aboagye, A.; Crawley, R. J.; Bodagh, N.; Gharaviri, A.; O'Hare, D.; Solis‐Lemus, J. A.; Roney, C. H.; Sim, I.; Ramsey, D.; Newby, D.; Chiribiri, A.; Plein, S.; Sztriha, L.; Scott, P.; Masci, P.; Harrison, J.; Williams, M. C.; Birns, J.; Somerville, P.; Bhalla, A.; Niederer, S.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n Journal of the American Heart Association, 13(3). 2024.\n \n\n\n\n
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\n\n\n\n@article{RN441,\n author = {Kotadia, Irum D. and O'Dowling, Robert and Aboagye, Akosua and Crawley, Richard J. and Bodagh, Neil and Gharaviri, Ali and O'Hare, Daniel and Solis‐Lemus, Jose Alonso and Roney, Caroline H. and Sim, Iain and Ramsey, Deborah and Newby, David and Chiribiri, Amedeo and Plein, Sven and Sztriha, Laszlo and Scott, Paul and Masci, Pier‐Giorgio and Harrison, James and Williams, Michelle C. and Birns, Jonathan and Somerville, Peter and Bhalla, Ajay and Niederer, Steven and O'Neill, Mark and Williams, Steven E.},\n title = {High Prevalence of New Clinically Significant Findings in Patients With Embolic Stroke of Unknown Source Evaluated by Cardiac Magnetic Resonance Imaging},\n journal = {Journal of the American Heart Association},\n volume = {13},\n number = {3},\n DOI = {10.1161/jaha.123.031489},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n \n\n \n \n \n \n \n Predicting atrial fibrillation risk following embolic stroke of unknown source using cardiac magnetic resonance imaging.\n \n \n \n\n\n \n Kotadia, I; O'dowling, R; Aboagye, A; Crawley, R; Plein, S; Sztriha, L; Scott, P; Masci, P G; Harrison, J; Birns, J; Sommerville, P; Bhalla, A; Niederer, S; O'neill, M; and Williams, S E\n\n\n \n\n\n\n EP Europace, 26(Supplement_1). 2024.\n \n\n\n\n
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\n\n\n@article{RN601,\n author = {Kotadia, I and O'dowling, R and Aboagye, A and Crawley, R and Plein, S and Sztriha, L and Scott, P and Masci, P G and Harrison, J and Birns, J and Sommerville, P and Bhalla, A and Niederer, S and O'neill, M and Williams, S E},\n title = {Predicting atrial fibrillation risk following embolic stroke of unknown source using cardiac magnetic resonance imaging},\n journal = {EP Europace},\n volume = {26},\n number = {Supplement_1},\n abstract = {Approximately 25% of embolic strokes of unknown source (ESUS) are attributed to undiagnosed atrial fibrillation (AF).1 The European Stroke Organisation (ESO) recommends implantable loop recorder (ILR) monitoring to identify ESUS patients with AF, permit the initiation of anticoagulation and reduce risk of recurrent stroke.2 However, this approach delays treatment and carries significant cost and resource implications. An alternative approach, where early anticoagulation is initiated in the absence of AF has failed to reduce recurrent stroke in four randomised controlled trials (NAVIGATE ESUS, RE-SPECT ESUS, ATTICUS and ARCADIA).3-6 An urgent need exists for new strategies to identify ESUS patients with undiagnosed AF that are at high risk of future thromboembolism.The primary objective of this study was to produce a substrate-based predictive model for AF in ESUS patients using atrial cardiac magnetic resonance imaging (CMRi).The Atrial CARdiac Magnetic resonance imaging in patients with embolic stroke of unknown source without documented AF (CARM-AF) Study is a prospective, multi-centre, observational study. All patients underwent CMRi and ILR insertion for AF detection within 3 months of ESUS. The main inclusion criteria were expected survival >12 months, CHA2DS2VASc≥3 and eGFR >30ml/min. Patients were allocated to the study group if AF >30 seconds was detected by ILR during the first year of follow-up and control group if no AF detected (Figure 1). Univariable analysis was used to identify clinical, echocardiographic and CMR parameters associated with AF diagnosis. Parameters with p<0.2 and no co-linearity were used to develop multivariable logistic regression models for AF prediction.From September 2020 to September 2022, 102 patients were enrolled and 91 were included in the final analysis. AF was detected in 17 patients during the first year of follow-up (18.6%). In univariable analysis, patients with AF were more likely to be female (p=0.02), but there were no significant differences in age, race, body mass index (BMI) or CHA2DS2VASc score (p=0.06, 0.272, 0.195, 0.211) between groups. Increased left atrial (LA) volume, surface area and reduced LA ejection fraction were associated with increased risk of AF detection at 1 year (p=0.006, 0.044, 0.008). LA fibrosis and sphericity were not associated with AF (p=0.84, 0.98).Logistic regression models for AF risk prediction were developed using patient, echocardiographic and CMR parameters. A combined model of patient and CMR parameters outperformed assessment of patient characteristics alone, achieving an AUC of 0.85 for predicting AF occurrence (p<0.005). Pseudo R2 of 0.30 (McFadden) indicated excellent model fit (Figure 2).CARM-AF is the first study to demonstrate feasibility and utility of CMR imaging to predict AF risk following ESUS. A randomised controlled trial initiating anticoagulation using the developed model is recommended.Study DesignModel Comparison (ROC Curves)},\n DOI = {10.1093/europace/euae102.252},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n Approximately 25% of embolic strokes of unknown source (ESUS) are attributed to undiagnosed atrial fibrillation (AF).1 The European Stroke Organisation (ESO) recommends implantable loop recorder (ILR) monitoring to identify ESUS patients with AF, permit the initiation of anticoagulation and reduce risk of recurrent stroke.2 However, this approach delays treatment and carries significant cost and resource implications. An alternative approach, where early anticoagulation is initiated in the absence of AF has failed to reduce recurrent stroke in four randomised controlled trials (NAVIGATE ESUS, RE-SPECT ESUS, ATTICUS and ARCADIA).3-6 An urgent need exists for new strategies to identify ESUS patients with undiagnosed AF that are at high risk of future thromboembolism.The primary objective of this study was to produce a substrate-based predictive model for AF in ESUS patients using atrial cardiac magnetic resonance imaging (CMRi).The Atrial CARdiac Magnetic resonance imaging in patients with embolic stroke of unknown source without documented AF (CARM-AF) Study is a prospective, multi-centre, observational study. All patients underwent CMRi and ILR insertion for AF detection within 3 months of ESUS. The main inclusion criteria were expected survival >12 months, CHA2DS2VASc≥3 and eGFR >30ml/min. Patients were allocated to the study group if AF >30 seconds was detected by ILR during the first year of follow-up and control group if no AF detected (Figure 1). Univariable analysis was used to identify clinical, echocardiographic and CMR parameters associated with AF diagnosis. Parameters with p<0.2 and no co-linearity were used to develop multivariable logistic regression models for AF prediction.From September 2020 to September 2022, 102 patients were enrolled and 91 were included in the final analysis. AF was detected in 17 patients during the first year of follow-up (18.6%). In univariable analysis, patients with AF were more likely to be female (p=0.02), but there were no significant differences in age, race, body mass index (BMI) or CHA2DS2VASc score (p=0.06, 0.272, 0.195, 0.211) between groups. Increased left atrial (LA) volume, surface area and reduced LA ejection fraction were associated with increased risk of AF detection at 1 year (p=0.006, 0.044, 0.008). LA fibrosis and sphericity were not associated with AF (p=0.84, 0.98).Logistic regression models for AF risk prediction were developed using patient, echocardiographic and CMR parameters. A combined model of patient and CMR parameters outperformed assessment of patient characteristics alone, achieving an AUC of 0.85 for predicting AF occurrence (p<0.005). Pseudo R2 of 0.30 (McFadden) indicated excellent model fit (Figure 2).CARM-AF is the first study to demonstrate feasibility and utility of CMR imaging to predict AF risk following ESUS. A randomised controlled trial initiating anticoagulation using the developed model is recommended.Study DesignModel Comparison (ROC Curves)\n
\n\n\n\n \n\n \n \n \n \n \n OpenEP and EP Workbench for electrophysiology data analysis.\n \n \n \n\n\n \n Bodagh, N.; and Williams, S. E.\n\n\n \n\n\n\n Nat Rev Cardiol. 2024.\n \n\n\n\n
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\n\n\n\n@article{RN568,\n author = {Bodagh, N. and Williams, S. E.},\n title = {OpenEP and EP Workbench for electrophysiology data analysis},\n journal = {Nat Rev Cardiol},\n DOI = {10.1038/s41569-024-01092-0},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n \n\n \n \n \n \n \n GenECG: A synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development.\n \n \n \n\n\n \n Bodagh, N.; Tun, K. S.; Barton, A.; Javidi, M.; Rashid, D.; Burns, R.; Kotadia, I.; Klis, M.; Gharaviri, A.; Vigneswaran, V.; Niederer, S.; O’Neill, M.; Bernabeu, M. O; and Williams, S. E\n\n\n \n\n\n\n medRxiv,2023.12.27.23300581. 2024.\n \n\n\n\n
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\n\n\n@article{RN600,\n author = {Bodagh, Neil and Tun, Kyaw Soe and Barton, Adam and Javidi, Malihe and Rashid, Darwon and Burns, Rachel and Kotadia, Irum and Klis, Magda and Gharaviri, Ali and Vigneswaran, Vinush and Niederer, Steven and O’Neill, Mark and Bernabeu, Miguel O and Williams, Steven E},\n title = {GenECG: A synthetic image-based ECG dataset to augment artificial intelligence-enhanced algorithm development},\n journal = {medRxiv},\n pages = {2023.12.27.23300581},\n abstract = {Background Artificial intelligence-enhanced electrocardiogram (AI-ECG) algorithms have primarily been developed using digitised signal data, due to a relative absence of image-based datasets. An image-based ECG dataset incorporating artefacts common to paper-based ECGs, which are typically scanned or photographed into electronic health records, could facilitate clinically useful AI-ECG algorithm development.This study aimed to create a high-fidelity, synthetic image-based ECG dataset to enable image-based AI-ECG algorithm development.Methods ECG images were re-created from the PTB-XL database, a publicly available signal-based dataset, and image manipulation techniques were applied to mimic artefacts associated with ECGs in real-world settings. To evaluate the fidelity of the synthetic images, iterative clinical Turing tests were conducted. The ability of currently available algorithms to analyse synthetic ECG images containing artefacts was assessed.Results Synthetic images were created from all PTB-XL ECGs leading to the creation of GenECG, an image-based dataset containing 21,799 ECGs with artefacts encountered in routine clinical care paired with artefact-free images. Clinical Turing tests confirmed the realism of the images: expert observer accuracy of discrimination between real-world and synthetic ECGs fell from 63.9% (95% CI 58.0%-69.8%) to 53.3% (95% CI: 48.6%-58.1%) over three rounds of testing, indicating that observers could not distinguish between synthetic and real ECGs. The performance of pre-existing image-based algorithms on synthetic (AUC 0.592, 95% CI 0.421-0.763) and real-world (AUC 0.647, 95% CI 0.520-0.774) ECG images containing artefact was limited. Algorithm fine-tuning with GenECG data led to an improvement in classification accuracy on real-world ECG images (AUC 0.821, 95% CI 0.730-0.913) demonstrating the potential for synthetic data to augment image-based AI-ECG algorithm development.Conclusions GenECG is the first synthetic image-based ECG dataset to pass a clinical Turing test. The dataset will enable image-based AI-ECG algorithm development, ensuring utility in low resource areas, pre-hospital settings and hospital environments where signal data are unavailable.What is already known on the subject?Artificial intelligence-enhanced ECG (AI-ECG) analysis presents a significant opportunity to improve the care of patients with cardiovascular disease.Most AI-ECG algorithms have been developed using ECG signal data, limiting their ability to analyse paper-based ECGs which are still prevalent in various hospital and non-hospital settings.What this study addsThis study presents GenECG, a high-fidelity, synthetic dataset comprising 21,799 ECG images paired with artefact-free images and ECG signal data.GenECG is the first publicly available synthetic, image-based ECG dataset to pass a clinical Turing test.The performance of image-based AI-ECG algorithms improved through fine-tuning with GenECG data demonstrating the potential for synthetic data to augment AI-ECG research.How this study might affect research, practice or policyGenECG will facilitate the development of image-based AI-ECG algorithms, promising to expand the application of AI-ECG to traditional hospital settings, reliant on paper-based ECGs, and non-hospital environments such as remote healthcare areas or pre-hospital settings.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThe study was funded by a University of Edinburgh Wellcome Trust iTPA award. The authors acknowledge the support of the British Heart Foundation Centre for Research Excellence Award III (RE/18/5/34216). The authors acknowledge the support of the British Heart Foundation (RG/20/4/34803). SEW is supported by the British Heart Foundation (FS/20/26/34952). IK is supported by the British Heart Foundation (FS/CRTF/21/24166).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Ethics committee/IRB of King's College London gave ethical approval for the clinical Turing tests performed as part of this study (LRS-22/23-38259).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThe ECG images described in the study were created from the PTB-XL database.[9] The ECG images will be used for a British Heart Foundation Data Science Centre open challenge (https://bhfdatasciencecentre.org/areas-unstructured-data-imaging-open-challenge/). Following this challenge, Dataset A and B will be made publicly available via a Creative Commons license. https://bhfdatasciencecentre.org/areas-unstructured-data-imaging-open-challenge/},\n DOI = {10.1101/2023.12.27.23300581},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n Background Artificial intelligence-enhanced electrocardiogram (AI-ECG) algorithms have primarily been developed using digitised signal data, due to a relative absence of image-based datasets. An image-based ECG dataset incorporating artefacts common to paper-based ECGs, which are typically scanned or photographed into electronic health records, could facilitate clinically useful AI-ECG algorithm development.This study aimed to create a high-fidelity, synthetic image-based ECG dataset to enable image-based AI-ECG algorithm development.Methods ECG images were re-created from the PTB-XL database, a publicly available signal-based dataset, and image manipulation techniques were applied to mimic artefacts associated with ECGs in real-world settings. To evaluate the fidelity of the synthetic images, iterative clinical Turing tests were conducted. The ability of currently available algorithms to analyse synthetic ECG images containing artefacts was assessed.Results Synthetic images were created from all PTB-XL ECGs leading to the creation of GenECG, an image-based dataset containing 21,799 ECGs with artefacts encountered in routine clinical care paired with artefact-free images. Clinical Turing tests confirmed the realism of the images: expert observer accuracy of discrimination between real-world and synthetic ECGs fell from 63.9% (95% CI 58.0%-69.8%) to 53.3% (95% CI: 48.6%-58.1%) over three rounds of testing, indicating that observers could not distinguish between synthetic and real ECGs. The performance of pre-existing image-based algorithms on synthetic (AUC 0.592, 95% CI 0.421-0.763) and real-world (AUC 0.647, 95% CI 0.520-0.774) ECG images containing artefact was limited. Algorithm fine-tuning with GenECG data led to an improvement in classification accuracy on real-world ECG images (AUC 0.821, 95% CI 0.730-0.913) demonstrating the potential for synthetic data to augment image-based AI-ECG algorithm development.Conclusions GenECG is the first synthetic image-based ECG dataset to pass a clinical Turing test. The dataset will enable image-based AI-ECG algorithm development, ensuring utility in low resource areas, pre-hospital settings and hospital environments where signal data are unavailable.What is already known on the subject?Artificial intelligence-enhanced ECG (AI-ECG) analysis presents a significant opportunity to improve the care of patients with cardiovascular disease.Most AI-ECG algorithms have been developed using ECG signal data, limiting their ability to analyse paper-based ECGs which are still prevalent in various hospital and non-hospital settings.What this study addsThis study presents GenECG, a high-fidelity, synthetic dataset comprising 21,799 ECG images paired with artefact-free images and ECG signal data.GenECG is the first publicly available synthetic, image-based ECG dataset to pass a clinical Turing test.The performance of image-based AI-ECG algorithms improved through fine-tuning with GenECG data demonstrating the potential for synthetic data to augment AI-ECG research.How this study might affect research, practice or policyGenECG will facilitate the development of image-based AI-ECG algorithms, promising to expand the application of AI-ECG to traditional hospital settings, reliant on paper-based ECGs, and non-hospital environments such as remote healthcare areas or pre-hospital settings.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThe study was funded by a University of Edinburgh Wellcome Trust iTPA award. The authors acknowledge the support of the British Heart Foundation Centre for Research Excellence Award III (RE/18/5/34216). The authors acknowledge the support of the British Heart Foundation (RG/20/4/34803). SEW is supported by the British Heart Foundation (FS/20/26/34952). IK is supported by the British Heart Foundation (FS/CRTF/21/24166).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:The Ethics committee/IRB of King's College London gave ethical approval for the clinical Turing tests performed as part of this study (LRS-22/23-38259).I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.YesThe ECG images described in the study were created from the PTB-XL database.[9] The ECG images will be used for a British Heart Foundation Data Science Centre open challenge (https://bhfdatasciencecentre.org/areas-unstructured-data-imaging-open-challenge/). Following this challenge, Dataset A and B will be made publicly available via a Creative Commons license. https://bhfdatasciencecentre.org/areas-unstructured-data-imaging-open-challenge/\n
\n\n\n\n \n\n \n \n \n \n \n Clinical Turing tests with user certainty analysis to create and validate synthetic electrocardiogram images for artificial intelligence-enhanced algorithm development.\n \n \n \n\n\n \n Bodagh, N; Tun, K S; Barton, A; Javidi, M; Kotadia, I; Klis, M; Gharaviri, A; Vigneswaran, V; Niederer, S; O'neill, M; Bernabeu, M O; and Williams, S E\n\n\n \n\n\n\n EP Europace, 26(Supplement_1). 2024.\n \n\n\n\n
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\n\n\n@article{RN599,\n author = {Bodagh, N and Tun, K S and Barton, A and Javidi, M and Kotadia, I and Klis, M and Gharaviri, A and Vigneswaran, V and Niederer, S and O'neill, M and Bernabeu, M O and Williams, S E},\n title = {Clinical Turing tests with user certainty analysis to create and validate synthetic electrocardiogram images for artificial intelligence-enhanced algorithm development},\n journal = {EP Europace},\n volume = {26},\n number = {Supplement_1},\n abstract = {Artificial intelligence-enhanced electrocardiogram (AI-ECG) algorithms have primarily been created using digitised signal data, owing to a relative absence of publicly available image-based datasets. ECGs are often scanned or photographed into electronic health records. For maximum clinical utility, AI-ECG algorithms should be applicable to these data. Synthetic data could expedite the creation of extensive, fully anonymised image-based ECG datasets to permit training image-based AI algorithms, but it is essential that such datasets contain the artefacts encountered in clinical practice. We investigated whether iterative clinical Turing tests with user certainty analysis could be used to develop and validate synthetic ECG data.To create synthetic ECG images containing the artefacts typically encountered in clinical practice, and to validate the images through iterative Turing testing and user certainty analysis.Synthetic ECG images containing artefacts were created using the PTB-XL dataset (a publicly available signal-based dataset comprising 21799 ECGs) as source data. Iterative clinical Turing tests were conducted where healthcare professionals completed an online survey comprising 60 real and synthetic ECGs. Participants were asked to select whether they thought ECGs were real or synthetic. For user certainty analysis, participants were asked to rate their confidence in their answers using a five-point Likert scale (Figure 1). Likert scale responses were converted into a signed ordinal scale representing user certainty in the identification of real or synthetic data. This scale was used to perform Receiver Operating Characteristic (ROC) analysis. Following quantitative survey completion, qualitative feedback was sought and used to iteratively improve the realism of the synthetic images.A total of 26 healthcare professionals completed the clinical Turing tests over three rounds. Qualitative feedback was used to improve the fidelity of the synthetic ECG images between rounds (Table 1). During iterative testing, the proportion of synthetic ECGs correctly identified fell from 61.5% to 53.7%, and the proportion of real-world ECGs correctly identified fell from 66.3% to 53.0% (Figure 1). Following the final Turing test, ROC analysis revealed no discriminative ability for identifying synthetic data (C-statistic 0.480, 95% confidence interval 0.432-0.529).Iterative Turing testing with user certainty analysis and qualitative user feedback may be used to create synthetic ECG images containing the artefacts typically encountered in clinical practice. Iterative Turing testing improved the images’ realism confirming their potential to augment image-based AI algorithm development. The presented methodology establishes a framework to develop high fidelity, synthetic patient datasets presenting a significant opportunity to enhance the uptake of AI within electrophysiology, cardiology, and medicine.},\n DOI = {10.1093/europace/euae102.572},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n Artificial intelligence-enhanced electrocardiogram (AI-ECG) algorithms have primarily been created using digitised signal data, owing to a relative absence of publicly available image-based datasets. ECGs are often scanned or photographed into electronic health records. For maximum clinical utility, AI-ECG algorithms should be applicable to these data. Synthetic data could expedite the creation of extensive, fully anonymised image-based ECG datasets to permit training image-based AI algorithms, but it is essential that such datasets contain the artefacts encountered in clinical practice. We investigated whether iterative clinical Turing tests with user certainty analysis could be used to develop and validate synthetic ECG data.To create synthetic ECG images containing the artefacts typically encountered in clinical practice, and to validate the images through iterative Turing testing and user certainty analysis.Synthetic ECG images containing artefacts were created using the PTB-XL dataset (a publicly available signal-based dataset comprising 21799 ECGs) as source data. Iterative clinical Turing tests were conducted where healthcare professionals completed an online survey comprising 60 real and synthetic ECGs. Participants were asked to select whether they thought ECGs were real or synthetic. For user certainty analysis, participants were asked to rate their confidence in their answers using a five-point Likert scale (Figure 1). Likert scale responses were converted into a signed ordinal scale representing user certainty in the identification of real or synthetic data. This scale was used to perform Receiver Operating Characteristic (ROC) analysis. Following quantitative survey completion, qualitative feedback was sought and used to iteratively improve the realism of the synthetic images.A total of 26 healthcare professionals completed the clinical Turing tests over three rounds. Qualitative feedback was used to improve the fidelity of the synthetic ECG images between rounds (Table 1). During iterative testing, the proportion of synthetic ECGs correctly identified fell from 61.5% to 53.7%, and the proportion of real-world ECGs correctly identified fell from 66.3% to 53.0% (Figure 1). Following the final Turing test, ROC analysis revealed no discriminative ability for identifying synthetic data (C-statistic 0.480, 95% confidence interval 0.432-0.529).Iterative Turing testing with user certainty analysis and qualitative user feedback may be used to create synthetic ECG images containing the artefacts typically encountered in clinical practice. Iterative Turing testing improved the images’ realism confirming their potential to augment image-based AI algorithm development. The presented methodology establishes a framework to develop high fidelity, synthetic patient datasets presenting a significant opportunity to enhance the uptake of AI within electrophysiology, cardiology, and medicine.\n
\n\n\n\n \n\n \n \n \n \n \n Time to capitalise on artificial intelligence in cardiac electrophysiology.\n \n \n \n\n\n \n Bodagh, N.; Klis, M.; Gharaviri, A.; Vigneswaran, V.; Vickneson, K.; Williams, M. C.; Niederer, S.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n J Interv Card Electrophysiol. 2024.\n \n\n\n\n
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\n\n\n\n@article{RN418,\n author = {Bodagh, N. and Klis, M. and Gharaviri, A. and Vigneswaran, V. and Vickneson, K. and Williams, M. C. and Niederer, S. and O'Neill, M. and Williams, S. E.},\n title = {Time to capitalise on artificial intelligence in cardiac electrophysiology},\n journal = {J Interv Card Electrophysiol},\n DOI = {10.1007/s10840-024-01803-0},\n year = {2024},\n type = {Journal Article}\n}\n\n\n\n
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\n \n \n\n \n \n \n \n \n Volumetric quantification of peri-atrial fat inflammation in atrial fibrillation patients.\n \n \n \n\n\n \n Vickneson, K; Tonko, J; Williams, M C; Gharaviri, A; Dweck, M; Baptiste, T; Alonso Solis Lemus, J; Corrado, C; Niederer, S; O' Neill, M; Whitaker, J; and Williams, S E\n\n\n \n\n\n\n EP Europace, 25(Supplement_1). 2023.\n \n\n\n\n
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\n\n\n \n\n\n\n \n\n \n \n doi\n \n \n\n \n link\n \n \n\n bibtex\n \n\n \n \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n \n \n \n\n\n\n
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\n\n\n@article{RN595,\n author = {Vickneson, K and Tonko, J and Williams, M C and Gharaviri, A and Dweck, M and Baptiste, T and Alonso Solis Lemus, J and Corrado, C and Niederer, S and O' Neill, M and Whitaker, J and Williams, S E},\n title = {Volumetric quantification of peri-atrial fat inflammation in atrial fibrillation patients},\n journal = {EP Europace},\n volume = {25},\n number = {Supplement_1},\n abstract = {Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Centre for Research Excellence Award III (RE/18/5/34216).SEW is supported by the British Heart Foundation (FS/20/26/34952). MCW is supported by the British Heart Foundation (FS/ICRF/20/26002)Peri-atrial fat may have pro-inflammatory and pro-fibrotic effects on atrial myocardium and increase the risk of developing atrial fibrillation. Both peri-atrial fat volume and density have been proposed as markers for atrial fibrillation risk, but prior studies considered only single-plane analysis or peri-atrial fat attenuation limited to the posterior left atrium.We aimed to assess total peri-atrial fat volume and attenuation and to explore the relationship between both parameters and the occurrence of atrial fibrillation.Contrast-enhanced cardiac computed tomography (CT) imaging was performed pre-ablation in atrial fibrillation patients (n=32) and in control patients (n=37) without atrial fibrillation undergoing investigation for chest pain. We developed a volumetric method for peri-atrial fat segmentation and quantification using seg3D2 and CEMRGapp. Peri-atrial fat volume and attenuation (Hounsfield units, HU) were compared between patients with and without atrial fibrillation in univariable and multivariable analyses. A sensitivity analysis was performed to assess the impact of fat proximity to the left atrial wall.Atrial fibrillation participants were older (64±11 years vs 58±7 years, P=0.004) and more likely to have a history of coronary artery disease (50% vs 25%, P=0.03). Participants with atrial fibrillation had greater left atrial volume (88±28 ml vs 69±19 ml, P=0.002) and mass (29±6 g vs 14±0.9 g, P=0.018) compared to controls. Peri-atrial fat volume was greater in those with than without atrial fibrillation (18.5±6.6 ml vs 14.2±7.0 ml, adjusted odds ratio (AOR) per ml 1.21; 95% confidence interval [CI] 1.03-1.48), after adjusting for age, body mass index, comorbidities, left atrial dimensions and mass. However, total peri-atrial fat attenuation was not significantly different between groups (-74.8 vs -73.0 HU; AOR per HU 1.01; 95% CI 0.82-1.22). Sensitivity analysis did not identify an interaction between interrogation distance from the left atrial wall and peri-atrial fat attenuation on prevalent atrial fibrillation (p=0.872).Peri-atrial fat volume, but not attenuation, is associated with prevalent atrial fibrillation. Further mechanistic studies are needed to study the role of peri-atrial fat in atrial fibrillation pathogenesis.},\n DOI = {10.1093/europace/euad122.012},\n year = {2023},\n type = {Journal Article}\n}\n\n\n\n Type of funding sources: Foundation. Main funding source(s): British Heart Foundation Centre for Research Excellence Award III (RE/18/5/34216).SEW is supported by the British Heart Foundation (FS/20/26/34952). MCW is supported by the British Heart Foundation (FS/ICRF/20/26002)Peri-atrial fat may have pro-inflammatory and pro-fibrotic effects on atrial myocardium and increase the risk of developing atrial fibrillation. Both peri-atrial fat volume and density have been proposed as markers for atrial fibrillation risk, but prior studies considered only single-plane analysis or peri-atrial fat attenuation limited to the posterior left atrium.We aimed to assess total peri-atrial fat volume and attenuation and to explore the relationship between both parameters and the occurrence of atrial fibrillation.Contrast-enhanced cardiac computed tomography (CT) imaging was performed pre-ablation in atrial fibrillation patients (n=32) and in control patients (n=37) without atrial fibrillation undergoing investigation for chest pain. We developed a volumetric method for peri-atrial fat segmentation and quantification using seg3D2 and CEMRGapp. Peri-atrial fat volume and attenuation (Hounsfield units, HU) were compared between patients with and without atrial fibrillation in univariable and multivariable analyses. A sensitivity analysis was performed to assess the impact of fat proximity to the left atrial wall.Atrial fibrillation participants were older (64±11 years vs 58±7 years, P=0.004) and more likely to have a history of coronary artery disease (50% vs 25%, P=0.03). Participants with atrial fibrillation had greater left atrial volume (88±28 ml vs 69±19 ml, P=0.002) and mass (29±6 g vs 14±0.9 g, P=0.018) compared to controls. Peri-atrial fat volume was greater in those with than without atrial fibrillation (18.5±6.6 ml vs 14.2±7.0 ml, adjusted odds ratio (AOR) per ml 1.21; 95% confidence interval [CI] 1.03-1.48), after adjusting for age, body mass index, comorbidities, left atrial dimensions and mass. However, total peri-atrial fat attenuation was not significantly different between groups (-74.8 vs -73.0 HU; AOR per HU 1.01; 95% CI 0.82-1.22). Sensitivity analysis did not identify an interaction between interrogation distance from the left atrial wall and peri-atrial fat attenuation on prevalent atrial fibrillation (p=0.872).Peri-atrial fat volume, but not attenuation, is associated with prevalent atrial fibrillation. Further mechanistic studies are needed to study the role of peri-atrial fat in atrial fibrillation pathogenesis.\n
\n\n\n\n \n\n \n \n \n \n \n AF and in-hospital mortality in COVID-19 patients.\n \n \n \n\n\n \n Kotadia, I. D.; Dias, M.; Roney, C.; Parker, R. A.; O'Dowling, R.; Bodagh, N.; Lemus-Solis, J. A.; O'Hare, D.; Sim, I.; Newby, D.; Niederer, S.; Birns, J.; Sommerville, P.; Bhalla, A.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n Heart Rhythm O2, 4(11): 700-707. 2023.\n \n\n\n\n
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\n\n\n@article{RN602,\n author = {Kotadia, I. D. and Dias, M. and Roney, C. and Parker, R. A. and O'Dowling, R. and Bodagh, N. and Lemus-Solis, J. A. and O'Hare, D. and Sim, I. and Newby, D. and Niederer, S. and Birns, J. and Sommerville, P. and Bhalla, A. and O'Neill, M. and Williams, S. E.},\n title = {AF and in-hospital mortality in COVID-19 patients},\n journal = {Heart Rhythm O2},\n volume = {4},\n number = {11},\n pages = {700-707},\n abstract = {BACKGROUND: There are conflicting data on whether new-onset atrial fibrillation (AF) is independently associated with poor outcomes in COVID-19 patients. This study represents the largest dataset curated by manual chart review comparing clinical outcomes between patients with sinus rhythm, pre-existing AF, and new-onset AF. OBJECTIVE: The primary aim of this study was to assess patient outcomes in COVID-19 patients with sinus rhythm, pre-existing AF, and new-onset AF. The secondary aim was to evaluate predictors of new-onset AF in patients with COVID-19 infection. METHODS: This was a single-center retrospective study of patients with a confirmed diagnosis of COVID-19 admitted between March and September 2020. Patient demographic data, medical history, and clinical outcome data were manually collected. Adjusted comparisons were performed following propensity score matching between those with pre-existing or new-onset AF and those without AF. RESULTS: The study population comprised of 1241 patients. A total of 94 (7.6%) patients had pre-existing AF and 42 (3.4%) patients developed new-onset AF. New-onset AF was associated with increased in-hospital mortality before (odds ratio [OR] 3.58, 95% confidence interval [CI] 1.78-7.06, P < .005) and after (OR 2.80, 95% CI 1.01-7.77, P < .005) propensity score matching compared with the no-AF group. However, pre-existing AF was not independently associated with in-hospital mortality compared with patients with no AF (postmatching OR: 1.13, 95% CI 0.57-2.21, P = .732). CONCLUSION: New-onset AF, but not pre-existing AF, was independently associated with elevated mortality in patients hospitalised with COVID-19. This observation highlights the need for careful monitoring of COVID-19 patients with new-onset AF. Further research is needed to explain the mechanistic relationship between new-onset AF and clinical outcomes in COVID-19 patients.},\n DOI = {10.1016/j.hroo.2023.10.004},\n year = {2023},\n type = {Journal Article}\n}\n\n\n\n BACKGROUND: There are conflicting data on whether new-onset atrial fibrillation (AF) is independently associated with poor outcomes in COVID-19 patients. This study represents the largest dataset curated by manual chart review comparing clinical outcomes between patients with sinus rhythm, pre-existing AF, and new-onset AF. OBJECTIVE: The primary aim of this study was to assess patient outcomes in COVID-19 patients with sinus rhythm, pre-existing AF, and new-onset AF. The secondary aim was to evaluate predictors of new-onset AF in patients with COVID-19 infection. METHODS: This was a single-center retrospective study of patients with a confirmed diagnosis of COVID-19 admitted between March and September 2020. Patient demographic data, medical history, and clinical outcome data were manually collected. Adjusted comparisons were performed following propensity score matching between those with pre-existing or new-onset AF and those without AF. RESULTS: The study population comprised of 1241 patients. A total of 94 (7.6%) patients had pre-existing AF and 42 (3.4%) patients developed new-onset AF. New-onset AF was associated with increased in-hospital mortality before (odds ratio [OR] 3.58, 95% confidence interval [CI] 1.78-7.06, P < .005) and after (OR 2.80, 95% CI 1.01-7.77, P < .005) propensity score matching compared with the no-AF group. However, pre-existing AF was not independently associated with in-hospital mortality compared with patients with no AF (postmatching OR: 1.13, 95% CI 0.57-2.21, P = .732). CONCLUSION: New-onset AF, but not pre-existing AF, was independently associated with elevated mortality in patients hospitalised with COVID-19. This observation highlights the need for careful monitoring of COVID-19 patients with new-onset AF. Further research is needed to explain the mechanistic relationship between new-onset AF and clinical outcomes in COVID-19 patients.\n
\n\n\n\n \n\n \n \n \n \n \n State of the art paper: Cardiac computed tomography of the left atrium in atrial fibrillation.\n \n \n \n\n\n \n Bodagh, N.; Williams, M. C.; Vickneson, K.; Gharaviri, A.; Niederer, S.; and Williams, S. E.\n\n\n \n\n\n\n J Cardiovasc Comput Tomogr, 17(3): 166-176. 2023.\n \n\n\n\n
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\n\n\n@article{RN593,\n author = {Bodagh, N. and Williams, M. C. and Vickneson, K. and Gharaviri, A. and Niederer, S. and Williams, S. E.},\n title = {State of the art paper: Cardiac computed tomography of the left atrium in atrial fibrillation},\n journal = {J Cardiovasc Comput Tomogr},\n volume = {17},\n number = {3},\n pages = {166-176},\n abstract = {The clinical spectrum of atrial fibrillation means that a patient-individualized approach is required to ensure optimal treatment. Cardiac computed tomography can accurately delineate atrial structure and function and could contribute to a personalized care pathway for atrial fibrillation patients. The imaging modality offers excellent spatial resolution and has been utilised in pre-, peri- and post-procedural care for patients with atrial fibrillation. Advances in temporal resolution, acquisition times and analysis techniques suggest potential expanding roles for cardiac computed tomography in the future management of patients with atrial fibrillation. The aim of the current review is to discuss the use of cardiac computed tomography in atrial fibrillation in pre-, peri- and post-procedural settings. Potential future applications of cardiac computed tomography including atrial wall thickness assessment and epicardial fat volume quantification are discussed together with emerging analysis techniques including computational modelling and machine learning with attention paid to how these developments may contribute to a personalized approach to atrial fibrillation management.},\n DOI = {10.1016/j.jcct.2023.03.002},\n year = {2023},\n type = {Journal Article}\n}\n\n\n\n The clinical spectrum of atrial fibrillation means that a patient-individualized approach is required to ensure optimal treatment. Cardiac computed tomography can accurately delineate atrial structure and function and could contribute to a personalized care pathway for atrial fibrillation patients. The imaging modality offers excellent spatial resolution and has been utilised in pre-, peri- and post-procedural care for patients with atrial fibrillation. Advances in temporal resolution, acquisition times and analysis techniques suggest potential expanding roles for cardiac computed tomography in the future management of patients with atrial fibrillation. The aim of the current review is to discuss the use of cardiac computed tomography in atrial fibrillation in pre-, peri- and post-procedural settings. Potential future applications of cardiac computed tomography including atrial wall thickness assessment and epicardial fat volume quantification are discussed together with emerging analysis techniques including computational modelling and machine learning with attention paid to how these developments may contribute to a personalized approach to atrial fibrillation management.\n
\n\n\n\n \n\n \n \n \n \n \n The Impact of Atrial Fibrillation Treatment Strategies on Cognitive Function.\n \n \n \n\n\n \n Bodagh, N.; Kotadia, I.; Gharaviri, A.; Zelaya, F.; Birns, J.; Bhalla, A.; Sommerville, P.; Niederer, S.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n J Clin Med, 12(9). 2023.\n \n\n\n\n
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\n\n\n@article{RN315,\n author = {Bodagh, N. and Kotadia, I. and Gharaviri, A. and Zelaya, F. and Birns, J. and Bhalla, A. and Sommerville, P. and Niederer, S. and O'Neill, M. and Williams, S. E.},\n title = {The Impact of Atrial Fibrillation Treatment Strategies on Cognitive Function},\n journal = {J Clin Med},\n volume = {12},\n number = {9},\n abstract = {There is increasing evidence to suggest that atrial fibrillation is associated with a heightened risk of dementia. The mechanism of interaction is unclear. Atrial fibrillation-induced cerebral infarcts, hypoperfusion, systemic inflammation, and anticoagulant therapy-induced cerebral microbleeds, have been proposed to explain the link between these conditions. An understanding of the pathogenesis of atrial fibrillation-associated cognitive decline may enable the development of treatment strategies targeted towards the prevention of dementia in atrial fibrillation patients. The aim of this review is to explore the impact that existing atrial fibrillation treatment strategies may have on cognition and the putative mechanisms linking the two conditions. This review examines how components of the 'Atrial Fibrillation Better Care pathway' (stroke risk reduction, rhythm control, rate control, and risk factor management) may influence the trajectory of atrial fibrillation-associated cognitive decline. The requirements for further prospective studies to understand the mechanistic link between atrial fibrillation and dementia and to develop treatment strategies targeted towards the prevention of atrial fibrillation-associated cognitive decline, are highlighted.},\n DOI = {10.3390/jcm12093050},\n year = {2023},\n type = {Journal Article}\n}\n\n\n\n There is increasing evidence to suggest that atrial fibrillation is associated with a heightened risk of dementia. The mechanism of interaction is unclear. Atrial fibrillation-induced cerebral infarcts, hypoperfusion, systemic inflammation, and anticoagulant therapy-induced cerebral microbleeds, have been proposed to explain the link between these conditions. An understanding of the pathogenesis of atrial fibrillation-associated cognitive decline may enable the development of treatment strategies targeted towards the prevention of dementia in atrial fibrillation patients. The aim of this review is to explore the impact that existing atrial fibrillation treatment strategies may have on cognition and the putative mechanisms linking the two conditions. This review examines how components of the 'Atrial Fibrillation Better Care pathway' (stroke risk reduction, rhythm control, rate control, and risk factor management) may influence the trajectory of atrial fibrillation-associated cognitive decline. The requirements for further prospective studies to understand the mechanistic link between atrial fibrillation and dementia and to develop treatment strategies targeted towards the prevention of atrial fibrillation-associated cognitive decline, are highlighted.\n
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\n \n \n\n \n \n \n \n \n Updates on OpenEP: The Open-Source Platform for Elec-trophysiological Data Analysis.\n \n \n \n\n\n \n Williams, S. E.; Smith, P.; Gharaviri, A.; O'Shea, C.; Connolly, A.; O'Neill, L.; Kotadia, I.; Sim, I.; Bodagh, N.; Grubb, N.; Whitaker, J.; Wright, M.; Niederer, S.; O'Neill, M.; and Linton, N.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
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\n\n\n\n@misc{RN271,\n author = {Williams, Steven E. and Smith, Paul and Gharaviri, Ali and O'Shea, Chris and Connolly, Adam and O'Neill, Louisa and Kotadia, Irum and Sim, Iain and Bodagh, Neil and Grubb, Neil and Whitaker, John and Wright, Matthew and Niederer, Steven and O'Neill, Mark and Linton, Nick},\n title = {Updates on OpenEP: The Open-Source Platform for Elec-trophysiological Data Analysis},\n DOI = {10.22489/CinC.2022.363},\n year = {2022},\n type = {Conference Paper}\n}\n\n\n\n \n\n \n \n \n \n \n CArdiac MagnEtic resonance assessment of bi-Atrial fibrosis in secundum atrial septal defects patients: CAMERA-ASD study.\n \n \n \n\n\n \n O'Neill, L.; Sim, I.; O'Hare, D.; Whitaker, J.; Mukherjee, R. K.; Razeghi, O.; Niederer, S.; Wright, M.; Chiribiri, A.; Frigiola, A.; O'Neill, M. D.; and Williams, S. E.\n\n\n \n\n\n\n Eur Heart J Cardiovasc Imaging, 23(9): 1231-1239. 2022.\n \n\n\n\n
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\n\n\n@article{RN329,\n author = {O'Neill, L. and Sim, I. and O'Hare, D. and Whitaker, J. and Mukherjee, R. K. and Razeghi, O. and Niederer, S. and Wright, M. and Chiribiri, A. and Frigiola, A. and O'Neill, M. D. and Williams, S. E.},\n title = {CArdiac MagnEtic resonance assessment of bi-Atrial fibrosis in secundum atrial septal defects patients: CAMERA-ASD study},\n journal = {Eur Heart J Cardiovasc Imaging},\n volume = {23},\n number = {9},\n pages = {1231-1239},\n abstract = {AIMS: Atrial septal defects (ASD) are associated with atrial arrhythmias, but the arrhythmia substrate in these patients is poorly defined. We hypothesized that bi-atrial fibrosis is present and that right atrial fibrosis is associated with atrial arrhythmias in ASD patients. We aimed to evaluate the extent of bi-atrial fibrosis in ASD patients and to investigate the relationships between bi-atrial fibrosis, atrial arrhythmias, shunt fraction, and age. METHODS AND RESULTS: Patients with uncorrected secundum ASDs (n = 36; 50.4 +/- 13.6 years) underwent cardiac magnetic resonance imaging with atrial late gadolinium enhancement. Comparison was made to non-congenital heart disease patients (n = 36; 60.3 +/- 10.5 years) with paroxysmal atrial fibrillation (AF). Cardiac magnetic resonance parameters associated with atrial arrhythmias were identified and the relationship between bi-atrial structure, age, and shunt fraction studied. Bi-atrial fibrosis burden was greater in ASD patients than paroxysmal AF patients (20.7 +/- 14% vs. 10.1 +/- 8.6% and 14.8 +/- 8.5% vs. 8.6 +/- 6.1% for right and left atria respectively, P = 0.001 for both). In ASD patients, right atrial fibrosis burden was greater in those with than without atrial arrhythmias (33.4 +/- 18.7% vs. 16.8 +/- 10.3%, P = 0.034). On receiver operating characteristic analysis, a right atrial fibrosis burden of 32% had a 92% specificity and 71% sensitivity for predicting the presence of atrial arrhythmias. Neither age nor shunt fraction was associated with bi-atrial fibrosis burden. CONCLUSION: Bi-atrial fibrosis burden is greater in ASD patients than non-congenital heart disease patients with paroxysmal AF. Right atrial fibrosis is associated with the presence of atrial arrhythmias in ASD patients. These findings highlight the importance of right atrial fibrosis to atrial arrhythmogenesis in ASD patients.},\n DOI = {10.1093/ehjci/jeab188},\n year = {2022},\n type = {Journal Article}\n}\n\n\n\n AIMS: Atrial septal defects (ASD) are associated with atrial arrhythmias, but the arrhythmia substrate in these patients is poorly defined. We hypothesized that bi-atrial fibrosis is present and that right atrial fibrosis is associated with atrial arrhythmias in ASD patients. We aimed to evaluate the extent of bi-atrial fibrosis in ASD patients and to investigate the relationships between bi-atrial fibrosis, atrial arrhythmias, shunt fraction, and age. METHODS AND RESULTS: Patients with uncorrected secundum ASDs (n = 36; 50.4 +/- 13.6 years) underwent cardiac magnetic resonance imaging with atrial late gadolinium enhancement. Comparison was made to non-congenital heart disease patients (n = 36; 60.3 +/- 10.5 years) with paroxysmal atrial fibrillation (AF). Cardiac magnetic resonance parameters associated with atrial arrhythmias were identified and the relationship between bi-atrial structure, age, and shunt fraction studied. Bi-atrial fibrosis burden was greater in ASD patients than paroxysmal AF patients (20.7 +/- 14% vs. 10.1 +/- 8.6% and 14.8 +/- 8.5% vs. 8.6 +/- 6.1% for right and left atria respectively, P = 0.001 for both). In ASD patients, right atrial fibrosis burden was greater in those with than without atrial arrhythmias (33.4 +/- 18.7% vs. 16.8 +/- 10.3%, P = 0.034). On receiver operating characteristic analysis, a right atrial fibrosis burden of 32% had a 92% specificity and 71% sensitivity for predicting the presence of atrial arrhythmias. Neither age nor shunt fraction was associated with bi-atrial fibrosis burden. CONCLUSION: Bi-atrial fibrosis burden is greater in ASD patients than non-congenital heart disease patients with paroxysmal AF. Right atrial fibrosis is associated with the presence of atrial arrhythmias in ASD patients. These findings highlight the importance of right atrial fibrosis to atrial arrhythmogenesis in ASD patients.\n
\n\n\n\n \n\n \n \n \n \n \n Provocation and localization of atrial ectopy in patients with atrial septal defects.\n \n \n \n\n\n \n O'Neill, L.; Sim, I.; O'Hare, D.; Whitaker, J.; Mukherjee, R. K.; Niederer, S.; Wright, M.; Ezzat, V.; Rosenthal, E.; Jones, M. I.; Frigiola, A.; O'Neill, M. D.; and Williams, S. E.\n\n\n \n\n\n\n J Interv Card Electrophysiol, 65(1): 227-237. 2022.\n \n\n\n\n
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\n\n\n@article{RN658,\n author = {O'Neill, L. and Sim, I. and O'Hare, D. and Whitaker, J. and Mukherjee, R. K. and Niederer, S. and Wright, M. and Ezzat, V. and Rosenthal, E. and Jones, M. I. and Frigiola, A. and O'Neill, M. D. and Williams, S. E.},\n title = {Provocation and localization of atrial ectopy in patients with atrial septal defects},\n journal = {J Interv Card Electrophysiol},\n volume = {65},\n number = {1},\n pages = {227-237},\n abstract = {BACKGROUND: Atrial fibrillation (AF) is associated with atrial septal defects (ASDs), but the mechanism of arrhythmia in these patients is poorly understood. We hypothesised that right-sided atrial ectopy may predominate in this cohort. Here, we aimed to localise the origin of spontaneous and provoked atrial ectopy in ASD patients. METHODS: Following invasive calibration of P-wave axes, 24-h Holter monitoring was used to determine the chamber of origin of spontaneous atrial ectopy. Simultaneous electrogram recording from multiple intra-cardiac catheters was used to determine the chamber of origin of isoprenaline-provoked ectopy. Comparison was made to a group of non-congenital heart disease AF patients. RESULTS: Amongst ASD patients, a right-sided origin for spontaneous atrial ectopy was significantly more prevalent than a left-sided origin (24/30 patients with right-sided ectopy vs. 14/30 with left-sided ectopy, P = 0.015). Amongst AF patients, there was no difference in the prevalence of spontaneous right vs. left-sided ectopy. For isoprenaline-provoked ectopy, there was no significant difference in the proportions of patients with right-sided or left-sided ectopy in either group. CONCLUSIONS: When spontaneous atrial ectopy occurs in ASD patients, it is significantly more prevalent from a right-sided than left-sided origin. Isoprenaline infusion did not reveal the predilection for right-sided ectopy during electrophysiology study.},\n DOI = {10.1007/s10840-022-01273-2},\n year = {2022},\n type = {Journal Article}\n}\n\n\n\n BACKGROUND: Atrial fibrillation (AF) is associated with atrial septal defects (ASDs), but the mechanism of arrhythmia in these patients is poorly understood. We hypothesised that right-sided atrial ectopy may predominate in this cohort. Here, we aimed to localise the origin of spontaneous and provoked atrial ectopy in ASD patients. METHODS: Following invasive calibration of P-wave axes, 24-h Holter monitoring was used to determine the chamber of origin of spontaneous atrial ectopy. Simultaneous electrogram recording from multiple intra-cardiac catheters was used to determine the chamber of origin of isoprenaline-provoked ectopy. Comparison was made to a group of non-congenital heart disease AF patients. RESULTS: Amongst ASD patients, a right-sided origin for spontaneous atrial ectopy was significantly more prevalent than a left-sided origin (24/30 patients with right-sided ectopy vs. 14/30 with left-sided ectopy, P = 0.015). Amongst AF patients, there was no difference in the prevalence of spontaneous right vs. left-sided ectopy. For isoprenaline-provoked ectopy, there was no significant difference in the proportions of patients with right-sided or left-sided ectopy in either group. CONCLUSIONS: When spontaneous atrial ectopy occurs in ASD patients, it is significantly more prevalent from a right-sided than left-sided origin. Isoprenaline infusion did not reveal the predilection for right-sided ectopy during electrophysiology study.\n
\n\n\n\n \n\n \n \n \n \n \n Atrial CARdiac Magnetic resonance imaging in patients with embolic stroke of unknown source without documented Atrial Fibrillation (CARM-AF): Study design and clinical protocol.\n \n \n \n\n\n \n Kotadia, I. D.; O'Dowling, R.; Aboagye, A.; Sim, I.; O'Hare, D.; Lemus-Solis, J. A.; Roney, C. H.; Dweck, M.; Chiribiri, A.; Plein, S.; Sztriha, L.; Scott, P.; Harrison, J.; Ramsay, D.; Birns, J.; Somerville, P.; Bhalla, A.; Niederer, S.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n Heart Rhythm O2, 3(2): 196-203. 2022.\n \n\n\n\n
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\n\n\n@article{RN246,\n author = {Kotadia, I. D. and O'Dowling, R. and Aboagye, A. and Sim, I. and O'Hare, D. and Lemus-Solis, J. A. and Roney, C. H. and Dweck, M. and Chiribiri, A. and Plein, S. and Sztriha, L. and Scott, P. and Harrison, J. and Ramsay, D. and Birns, J. and Somerville, P. and Bhalla, A. and Niederer, S. and O'Neill, M. and Williams, S. E.},\n title = {Atrial CARdiac Magnetic resonance imaging in patients with embolic stroke of unknown source without documented Atrial Fibrillation (CARM-AF): Study design and clinical protocol},\n journal = {Heart Rhythm O2},\n volume = {3},\n number = {2},\n pages = {196-203},\n abstract = {BACKGROUND: Initiation of anticoagulation therapy in ischemic stroke patients is contingent on a clinical diagnosis of atrial fibrillation (AF). Results from previous studies suggest thromboembolic risk may predate clinical manifestations of AF. Early identification of this cohort of patients may allow early initiation of anticoagulation and reduce the risk of secondary stroke. OBJECTIVE: This study aims to produce a substrate-based predictive model using cardiac magnetic resonance imaging (CMR) and baseline noninvasive electrocardiographic investigations to improve the identification of patients at risk of future thromboembolism. METHODS: CARM-AF is a prospective, multicenter, observational cohort study. Ninety-two patients will be recruited following an embolic stroke of unknown source (ESUS) and undergo atrial CMR followed by insertion of an implantable loop recorder (ILR) as per routine clinical care within 3 months of index stroke. Remote ILR follow-up will be used to allocate patients to a study or control group determined by the presence or absence of AF as defined by ILR monitoring. RESULTS: Baseline data collection, noninvasive electrocardiographic data analysis, and imaging postprocessing will be performed at the time of enrollment. Primary analysis will be performed following 12 months of continuous ILR monitoring, with interim and delayed analyses performed at 6 months and 2 and 3 years, respectively. CONCLUSION: The CARM-AF Study will use atrial structural and electrocardiographic metrics to identify patients with AF, or at high risk of developing AF, who may benefit from early initiation of anticoagulation.},\n DOI = {10.1016/j.hroo.2022.01.005},\n year = {2022},\n type = {Journal Article}\n}\n\n\n\n BACKGROUND: Initiation of anticoagulation therapy in ischemic stroke patients is contingent on a clinical diagnosis of atrial fibrillation (AF). Results from previous studies suggest thromboembolic risk may predate clinical manifestations of AF. Early identification of this cohort of patients may allow early initiation of anticoagulation and reduce the risk of secondary stroke. OBJECTIVE: This study aims to produce a substrate-based predictive model using cardiac magnetic resonance imaging (CMR) and baseline noninvasive electrocardiographic investigations to improve the identification of patients at risk of future thromboembolism. METHODS: CARM-AF is a prospective, multicenter, observational cohort study. Ninety-two patients will be recruited following an embolic stroke of unknown source (ESUS) and undergo atrial CMR followed by insertion of an implantable loop recorder (ILR) as per routine clinical care within 3 months of index stroke. Remote ILR follow-up will be used to allocate patients to a study or control group determined by the presence or absence of AF as defined by ILR monitoring. RESULTS: Baseline data collection, noninvasive electrocardiographic data analysis, and imaging postprocessing will be performed at the time of enrollment. Primary analysis will be performed following 12 months of continuous ILR monitoring, with interim and delayed analyses performed at 6 months and 2 and 3 years, respectively. CONCLUSION: The CARM-AF Study will use atrial structural and electrocardiographic metrics to identify patients with AF, or at high risk of developing AF, who may benefit from early initiation of anticoagulation.\n
\n\n\n\n \n\n \n \n \n \n \n In Vivo Analysis of Conduction Pattern Dynamics: System Development and Application Using OpenEP.\n \n \n \n\n\n \n Gharaviri, A.; O'Neill, L.; Smith, P.; Roney, C.; Grub, N.; Wright, M.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n 2022.\n \n\n\n\n
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\n\n\n\n@misc{RN272,\n author = {Gharaviri, Ali and O'Neill, Louisa and Smith, Paul and Roney, Caroline and Grub, Neil and Wright, Matthew and O'Neill, Mark and Williams, Steven E.},\n title = {In Vivo Analysis of Conduction Pattern Dynamics: System Development and Application Using OpenEP},\n DOI = {10.22489/CinC.2022.347},\n year = {2022},\n type = {Conference Paper}\n}\n\n\n\n \n\n \n \n \n \n \n Impact of catheter ablation versus medical therapy on cognitive function in atrial fibrillation: a systematic review.\n \n \n \n\n\n \n Bodagh, N.; Yap, R.; Kotadia, I.; Sim, I.; Bhalla, A.; Somerville, P.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n J Interv Card Electrophysiol, 65(1): 271-286. 2022.\n \n\n\n\n
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\n\n\n@article{RN412,\n author = {Bodagh, N. and Yap, R. and Kotadia, I. and Sim, I. and Bhalla, A. and Somerville, P. and O'Neill, M. and Williams, S. E.},\n title = {Impact of catheter ablation versus medical therapy on cognitive function in atrial fibrillation: a systematic review},\n journal = {J Interv Card Electrophysiol},\n volume = {65},\n number = {1},\n pages = {271-286},\n abstract = {PURPOSE: Atrial fibrillation is associated with an increased risk of cognitive impairment. It is unclear whether the restoration of sinus rhythm with catheter ablation may modify this risk. We conducted a systematic review of studies comparing cognitive outcomes following catheter ablation with medical therapy (rate and/or rhythm control) in atrial fibrillation. METHODS: Searches were performed on the following databases from their inception to 17 October 2021: PubMed, OVID Medline, Embase and Cochrane Library. The inclusion criteria comprised studies comparing catheter ablation against medical therapy (rate and/or rhythm control in conjunction with anticoagulation where appropriate) which included cognitive assessment and/or a diagnosis of dementia as an outcome. RESULTS: A total of 599 records were screened. Ten studies including 15,886 patients treated with catheter ablation and 42,684 patients treated with medical therapy were included. Studies which compared the impact of catheter ablation versus medical therapy on quantitative assessments of cognitive function yielded conflicting results. In studies, examining new onset dementia during follow-up, catheter ablation was associated with a lower risk of subsequent dementia diagnosis compared to medical therapy (hazard ratio: 0.60 (95% confidence interval 0.42-0.88, p < 0.05)). CONCLUSION: The accumulating evidence linking atrial fibrillation with cognitive impairment warrants the design of atrial fibrillation treatment strategies aimed at minimising cognitive decline. However, the impact of catheter ablation and atrial fibrillation medical therapy on cognitive decline is currently uncertain. Future studies investigating atrial fibrillation treatment strategies should include cognitive outcomes as important clinical endpoints.},\n DOI = {10.1007/s10840-022-01196-y},\n year = {2022},\n type = {Journal Article}\n}\n\n\n\n PURPOSE: Atrial fibrillation is associated with an increased risk of cognitive impairment. It is unclear whether the restoration of sinus rhythm with catheter ablation may modify this risk. We conducted a systematic review of studies comparing cognitive outcomes following catheter ablation with medical therapy (rate and/or rhythm control) in atrial fibrillation. METHODS: Searches were performed on the following databases from their inception to 17 October 2021: PubMed, OVID Medline, Embase and Cochrane Library. The inclusion criteria comprised studies comparing catheter ablation against medical therapy (rate and/or rhythm control in conjunction with anticoagulation where appropriate) which included cognitive assessment and/or a diagnosis of dementia as an outcome. RESULTS: A total of 599 records were screened. Ten studies including 15,886 patients treated with catheter ablation and 42,684 patients treated with medical therapy were included. Studies which compared the impact of catheter ablation versus medical therapy on quantitative assessments of cognitive function yielded conflicting results. In studies, examining new onset dementia during follow-up, catheter ablation was associated with a lower risk of subsequent dementia diagnosis compared to medical therapy (hazard ratio: 0.60 (95% confidence interval 0.42-0.88, p < 0.05)). CONCLUSION: The accumulating evidence linking atrial fibrillation with cognitive impairment warrants the design of atrial fibrillation treatment strategies aimed at minimising cognitive decline. However, the impact of catheter ablation and atrial fibrillation medical therapy on cognitive decline is currently uncertain. Future studies investigating atrial fibrillation treatment strategies should include cognitive outcomes as important clinical endpoints.\n
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\n \n \n\n \n \n \n \n \n OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research.\n \n \n \n\n\n \n Williams, S. E.; Roney, C. H.; Connolly, A.; Sim, I.; Whitaker, J.; O'Hare, D.; Kotadia, I.; O'Neill, L.; Corrado, C.; Bishop, M.; Niederer, S. A.; Wright, M.; O'Neill, M.; and Linton, N. W. F.\n\n\n \n\n\n\n Front Physiol, 12: 646023. 2021.\n \n\n\n\n
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\n\n\n@article{RN174,\n author = {Williams, S. E. and Roney, C. H. and Connolly, A. and Sim, I. and Whitaker, J. and O'Hare, D. and Kotadia, I. and O'Neill, L. and Corrado, C. and Bishop, M. and Niederer, S. A. and Wright, M. and O'Neill, M. and Linton, N. W. F.},\n title = {OpenEP: A Cross-Platform Electroanatomic Mapping Data Format and Analysis Platform for Electrophysiology Research},\n journal = {Front Physiol},\n volume = {12},\n pages = {646023},\n abstract = {BACKGROUND: Electroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner. METHODS: A data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research. RESULTS: The average time to parse clinical datasets was 400 +/- 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 +/- 8.7 Mb, vs clinical: 1.46 +/- 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: R (2) = 0.7726, P < 0.0001; Volume: R (2) = 0.5179, P < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage R (2) = 0.8708, P < 0.001; local activation time R (2) = 0.8892, P < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies. CONCLUSIONS: We present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development.},\n DOI = {10.3389/fphys.2021.646023},\n year = {2021},\n type = {Journal Article}\n}\n\n\n\n BACKGROUND: Electroanatomic mapping systems are used to support electrophysiology research. Data exported from these systems is stored in proprietary formats which are challenging to access and storage-space inefficient. No previous work has made available an open-source platform for parsing and interrogating this data in a standardized format. We therefore sought to develop a standardized, open-source data structure and associated computer code to store electroanatomic mapping data in a space-efficient and easily accessible manner. METHODS: A data structure was defined capturing the available anatomic and electrical data. OpenEP, implemented in MATLAB, was developed to parse and interrogate this data. Functions are provided for analysis of chamber geometry, activation mapping, conduction velocity mapping, voltage mapping, ablation sites, and electrograms as well as visualization and input/output functions. Performance benchmarking for data import and storage was performed. Data import and analysis validation was performed for chamber geometry, activation mapping, voltage mapping and ablation representation. Finally, systematic analysis of electrophysiology literature was performed to determine the suitability of OpenEP for contemporary electrophysiology research. RESULTS: The average time to parse clinical datasets was 400 +/- 162 s per patient. OpenEP data was two orders of magnitude smaller than compressed clinical data (OpenEP: 20.5 +/- 8.7 Mb, vs clinical: 1.46 +/- 0.77 Gb). OpenEP-derived geometry metrics were correlated with the same clinical metrics (Area: R (2) = 0.7726, P < 0.0001; Volume: R (2) = 0.5179, P < 0.0001). Investigating the cause of systematic bias in these correlations revealed OpenEP to outperform the clinical platform in recovering accurate values. Both activation and voltage mapping data created with OpenEP were correlated with clinical values (mean voltage R (2) = 0.8708, P < 0.001; local activation time R (2) = 0.8892, P < 0.0001). OpenEP provides the processing necessary for 87 of 92 qualitatively assessed analysis techniques (95%) and 119 of 136 quantitatively assessed analysis techniques (88%) in a contemporary cohort of mapping studies. CONCLUSIONS: We present the OpenEP framework for evaluating electroanatomic mapping data. OpenEP provides the core functionality necessary to conduct electroanatomic mapping research. We demonstrate that OpenEP is both space-efficient and accurately representative of the original data. We show that OpenEP captures the majority of data required for contemporary electroanatomic mapping-based electrophysiology research and propose a roadmap for future development.\n
\n\n\n\n \n\n \n \n \n \n \n Secondary Stroke Prevention Following Embolic Stroke of Unknown Source in the Absence of Documented Atrial Fibrillation: A Clinical Review.\n \n \n \n\n\n \n Kotadia, I. D.; Sim, I.; Mukherjee, R.; O'Hare, D.; Chiribiri, A.; Birns, J.; Bhalla, A.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n J Am Heart Assoc, 10(13): e021045. 2021.\n \n\n\n\n
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\n\n\n@article{RN710,\n author = {Kotadia, I. D. and Sim, I. and Mukherjee, R. and O'Hare, D. and Chiribiri, A. and Birns, J. and Bhalla, A. and O'Neill, M. and Williams, S. E.},\n title = {Secondary Stroke Prevention Following Embolic Stroke of Unknown Source in the Absence of Documented Atrial Fibrillation: A Clinical Review},\n journal = {J Am Heart Assoc},\n volume = {10},\n number = {13},\n pages = {e021045},\n abstract = {Approximately one-third of ischemic strokes are classified as cryptogenic strokes. The risk of stroke recurrence in these patients is significantly elevated with up to one-third of patients with cryptogenic stroke experiencing a further stroke within 10 years. While anticoagulation is the mainstay of treatment for secondary stroke prevention in the context of documented atrial fibrillation (AF), it is estimated that up to 25% of patients with cryptogenic stroke have undiagnosed AF. Furthermore, the historical acceptance of a causal relationship between AF and stroke has recently come under scrutiny, with evidence to suggest that embolic stroke risk may be elevated even in the absence of documented atrial fibrillation attributable to the presence of electrical and structural changes constituting an atrial cardiomyopathy. More recently, the term embolic stroke of unknown source has garnered increasing interest as a subset of patients with cryptogenic stroke in whom a minimum set of diagnostic investigations has been performed, and a nonlacunar infarct highly suspicious of embolic etiology is suspected but in the absence of an identifiable secondary cause of stroke. The ongoing ARCADIA (Atrial Cardiopathy and Antithrombotic Drugs in Prevention After Cryptogenic Stroke) randomized trial and ATTICUS (Apixiban for Treatment of Embolic Stroke of Undetermined Source) study seek to further define this novel term. This review summarizes the relationship between AF, embolic stroke, and atrial cardiomyopathy and provides an overview of the clinical relevance of cardiac imaging, electrocardiographic, and serum biomarkers in the assessment of AF and secondary stroke risk. The implications of these findings on therapeutic considerations is considered and gaps in the literature identified as areas for future study in risk stratifying this cohort of patients.},\n DOI = {10.1161/JAHA.121.021045},\n year = {2021},\n type = {Journal Article}\n}\n\n\n\n Approximately one-third of ischemic strokes are classified as cryptogenic strokes. The risk of stroke recurrence in these patients is significantly elevated with up to one-third of patients with cryptogenic stroke experiencing a further stroke within 10 years. While anticoagulation is the mainstay of treatment for secondary stroke prevention in the context of documented atrial fibrillation (AF), it is estimated that up to 25% of patients with cryptogenic stroke have undiagnosed AF. Furthermore, the historical acceptance of a causal relationship between AF and stroke has recently come under scrutiny, with evidence to suggest that embolic stroke risk may be elevated even in the absence of documented atrial fibrillation attributable to the presence of electrical and structural changes constituting an atrial cardiomyopathy. More recently, the term embolic stroke of unknown source has garnered increasing interest as a subset of patients with cryptogenic stroke in whom a minimum set of diagnostic investigations has been performed, and a nonlacunar infarct highly suspicious of embolic etiology is suspected but in the absence of an identifiable secondary cause of stroke. The ongoing ARCADIA (Atrial Cardiopathy and Antithrombotic Drugs in Prevention After Cryptogenic Stroke) randomized trial and ATTICUS (Apixiban for Treatment of Embolic Stroke of Undetermined Source) study seek to further define this novel term. This review summarizes the relationship between AF, embolic stroke, and atrial cardiomyopathy and provides an overview of the clinical relevance of cardiac imaging, electrocardiographic, and serum biomarkers in the assessment of AF and secondary stroke risk. The implications of these findings on therapeutic considerations is considered and gaps in the literature identified as areas for future study in risk stratifying this cohort of patients.\n
\n\n\n\n \n\n \n \n \n \n \n Dielectric Imaging Accurately Measures Regional Cardiac Chamber Wall Thickness - An In Vivo Study.\n \n \n \n\n\n \n Kotadia, I.; Michelle Williams, I. S.; Roney, C. H.; Solis-Lemus, J.; Razeghi, O.; Daniel, C.; Eddie Clutton, S. G.; Lynn Grant, R. G.; Chris Proudfoot, J. N.; Reisner, Y.; Harks, E.; Art, P.; Stephen Welsh, A. K.; Whitaker, J.; James Wright, M.; Niederer, S. A.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n Heart Rhythm, 18(8): S227-S228. 2021.\n \n\n\n\n
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\n\n\n\n@article{RN603,\n author = {Kotadia, Irum and Michelle Williams, Iain Sim and Roney, Caroline H. and Solis-Lemus, Jose and Razeghi, Orod and Daniel, Carola and Eddie Clutton, Stephen Greenhalgh and Lynn Grant, Rachael Gregson and Chris Proudfoot, James Nixon and Reisner, Yotam and Harks, Erik and Art, Pilmeyer and Stephen Welsh, Alisa Komleva and Whitaker, John and James Wright, Matthew and Niederer, Steven A. and O'Neill, Mark and Williams, Steven Edwin},\n title = {Dielectric Imaging Accurately Measures Regional Cardiac Chamber Wall Thickness - An In Vivo Study},\n journal = {Heart Rhythm},\n volume = {18},\n number = {8},\n pages = {S227-S228},\n DOI = {10.1016/j.hrthm.2021.06.570},\n year = {2021},\n type = {Journal Article}\n}\n\n\n\n
\n\n \n \n 2020\n \n \n (3)\n \n \n
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\n \n \n\n \n \n \n \n \n Percutaneous secundum atrial septal defect closure for the treatment of atrial arrhythmia in the adult: A meta-analysis.\n \n \n \n\n\n \n O'Neill, L.; Floyd, C. N.; Sim, I.; Whitaker, J.; Mukherjee, R.; O'Hare, D.; Gatzoulis, M.; Frigiola, A.; O'Neill, M. D.; and Williams, S. E.\n\n\n \n\n\n\n Int J Cardiol, 321: 104-112. 2020.\n \n\n\n\n
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\n\n\n@article{RN657,\n author = {O'Neill, L. and Floyd, C. N. and Sim, I. and Whitaker, J. and Mukherjee, R. and O'Hare, D. and Gatzoulis, M. and Frigiola, A. and O'Neill, M. D. and Williams, S. E.},\n title = {Percutaneous secundum atrial septal defect closure for the treatment of atrial arrhythmia in the adult: A meta-analysis},\n journal = {Int J Cardiol},\n volume = {321},\n pages = {104-112},\n abstract = {BACKGROUND: Atrial arrhythmias are common in patients with atrial septal defects (ASD) but the effects of percutaneous closure on atrial arrhythmia prevalence is unclear. We investigated the effects of ASD device closure and the impact of age at time of closure on prevalent atrial arrythmia. METHODS: Meta-analysis of studies reporting atrial arrhythmia prevalence in adult patients before and after percutaneous closure was performed. Primary outcomes were prevalence of 'all atrial arrhythmia' and atrial fibrillation alone post closure. Sub-group analysis examined the effects of closure according to age in patients; <40 years, >/=40 and >/= 60 years. 25 studies were included. RESULTS: Meta-analysis of all studies demonstrated no reduction in all atrial arrhythmia or atrial fibrillation prevalence post-closure (OR 0.855, 95% CI 0.672 to 1.087, P = .201 and OR 0.818, 95% CI 0.645 to 1.038, P = .099, respectively). A weak reduction in all atrial arrhythmia and atrial fibrillation was seen in patients >/=40 years (OR 0.77, 95% CI 0.616 to 0.979, P = .032 and OR 0.760, 95% CI 0.6 to 0.964, P = .024, respectively) but not >/=60 years (OR 0.822, 95% CI 0.593 to 1.141, P = .242 and OR 0.83, 95% CI 0.598 to 1.152, P = .266, respectively). No data were available in patients <40 years. This, and other limitations, prevents conclusive assessment of the effect of age on arrhythmia prevalence. CONCLUSIONS: Overall, percutaneous ASD closure is not associated with a reduction in atrial arrhythmia prevalence in this meta-analysis. A weak benefit is seen in patients >/=40 years of age, not present in patients >/=60 years.},\n DOI = {10.1016/j.ijcard.2020.07.014},\n year = {2020},\n type = {Journal Article}\n}\n\n\n\n BACKGROUND: Atrial arrhythmias are common in patients with atrial septal defects (ASD) but the effects of percutaneous closure on atrial arrhythmia prevalence is unclear. We investigated the effects of ASD device closure and the impact of age at time of closure on prevalent atrial arrythmia. METHODS: Meta-analysis of studies reporting atrial arrhythmia prevalence in adult patients before and after percutaneous closure was performed. Primary outcomes were prevalence of 'all atrial arrhythmia' and atrial fibrillation alone post closure. Sub-group analysis examined the effects of closure according to age in patients; <40 years, >/=40 and >/= 60 years. 25 studies were included. RESULTS: Meta-analysis of all studies demonstrated no reduction in all atrial arrhythmia or atrial fibrillation prevalence post-closure (OR 0.855, 95% CI 0.672 to 1.087, P = .201 and OR 0.818, 95% CI 0.645 to 1.038, P = .099, respectively). A weak reduction in all atrial arrhythmia and atrial fibrillation was seen in patients >/=40 years (OR 0.77, 95% CI 0.616 to 0.979, P = .032 and OR 0.760, 95% CI 0.6 to 0.964, P = .024, respectively) but not >/=60 years (OR 0.822, 95% CI 0.593 to 1.141, P = .242 and OR 0.83, 95% CI 0.598 to 1.152, P = .266, respectively). No data were available in patients <40 years. This, and other limitations, prevents conclusive assessment of the effect of age on arrhythmia prevalence. CONCLUSIONS: Overall, percutaneous ASD closure is not associated with a reduction in atrial arrhythmia prevalence in this meta-analysis. A weak benefit is seen in patients >/=40 years of age, not present in patients >/=60 years.\n
\n\n\n\n \n\n \n \n \n \n \n High-power, Short-duration Radiofrequency Ablation for the Treatment of AF.\n \n \n \n\n\n \n Kotadia, I. D.; Williams, S. E.; and O'Neill, M.\n\n\n \n\n\n\n Arrhythm Electrophysiol Rev, 8(4): 265-272. 2020.\n \n\n\n\n
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\n\n\n@article{RN575,\n author = {Kotadia, I. D. and Williams, S. E. and O'Neill, M.},\n title = {High-power, Short-duration Radiofrequency Ablation for the Treatment of AF},\n journal = {Arrhythm Electrophysiol Rev},\n volume = {8},\n number = {4},\n pages = {265-272},\n abstract = {High-power, short-duration (HPSD) ablation for the treatment of AF is emerging as an alternative to ablation using conventional ablation generator settings characterised by lower power and longer duration. Although the reported potential advantages of HPSD ablation include less tissue oedema and collateral tissue damage, a reduction in procedural time and superior ablation lesion formation, clinical studies of HPSD ablation validating these observations are limited. One of the main challenges for HPSD ablation has been the inability to adequately assess temperature and lesion formation in real time. Novel catheter designs may improve the accuracy of intra-ablation temperature recording and correspondingly may improve the safety profile of HPSD ablation. Clinical studies of HPSD ablation are on-going and interpretation of the data from these and other studies will be required to ascertain the clinical value of HPSD ablation.},\n DOI = {10.15420/aer.2019.09},\n year = {2020},\n type = {Journal Article}\n}\n\n\n\n High-power, short-duration (HPSD) ablation for the treatment of AF is emerging as an alternative to ablation using conventional ablation generator settings characterised by lower power and longer duration. Although the reported potential advantages of HPSD ablation include less tissue oedema and collateral tissue damage, a reduction in procedural time and superior ablation lesion formation, clinical studies of HPSD ablation validating these observations are limited. One of the main challenges for HPSD ablation has been the inability to adequately assess temperature and lesion formation in real time. Novel catheter designs may improve the accuracy of intra-ablation temperature recording and correspondingly may improve the safety profile of HPSD ablation. Clinical studies of HPSD ablation are on-going and interpretation of the data from these and other studies will be required to ascertain the clinical value of HPSD ablation.\n
\n\n\n\n \n\n \n \n \n \n \n Supraventricular tachycardia: An overview of diagnosis and management.\n \n \n \n\n\n \n Kotadia, I. D.; Williams, S. E.; and O'Neill, M.\n\n\n \n\n\n\n Clin Med (Lond), 20(1): 43-47. 2020.\n \n\n\n\n
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\n\n\n@article{RN446,\n author = {Kotadia, I. D. and Williams, S. E. and O'Neill, M.},\n title = {Supraventricular tachycardia: An overview of diagnosis and management},\n journal = {Clin Med (Lond)},\n volume = {20},\n number = {1},\n pages = {43-47},\n abstract = {Supraventricular tachycardia (SVT) is a common cause of hospital admissions and can cause significant patient discomfort and distress. The most common SVTs include atrioventricular nodal re-entrant tachycardia, atrioventricular re-entrant tachycardia and atrial tachycardia. In many cases, the underlying mechanism can be deduced from electrocardiography during tachycardia, comparing it with sinus rhythm, and assessing the onset and offset of tachycardia. Recent European Society of Cardiology guidelines continue to advocate the use of vagal manoeuvres and adenosine as first-line therapies in the acute diagnosis and management of SVT. Alternative therapies include the use of beta-blockers and calcium channel blockers. All patients treated for SVT should be referred for a heart rhythm specialist opinion. Long-term treatment is dependent on several factors including frequency of symptoms, risk stratification, and patient preference. Management can range from conservative, if symptoms are rare and the patient is low risk, to catheter ablation which is curative in the majority of patients.},\n DOI = {10.7861/clinmed.cme.20.1.3},\n year = {2020},\n type = {Journal Article}\n}\n\n\n\n Supraventricular tachycardia (SVT) is a common cause of hospital admissions and can cause significant patient discomfort and distress. The most common SVTs include atrioventricular nodal re-entrant tachycardia, atrioventricular re-entrant tachycardia and atrial tachycardia. In many cases, the underlying mechanism can be deduced from electrocardiography during tachycardia, comparing it with sinus rhythm, and assessing the onset and offset of tachycardia. Recent European Society of Cardiology guidelines continue to advocate the use of vagal manoeuvres and adenosine as first-line therapies in the acute diagnosis and management of SVT. Alternative therapies include the use of beta-blockers and calcium channel blockers. All patients treated for SVT should be referred for a heart rhythm specialist opinion. Long-term treatment is dependent on several factors including frequency of symptoms, risk stratification, and patient preference. Management can range from conservative, if symptoms are rare and the patient is low risk, to catheter ablation which is curative in the majority of patients.\n
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\n\n \n \n 2019\n \n \n (3)\n \n \n
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\n \n \n\n \n \n \n \n \n Left atrial effective conducting size predicts atrial fibrillation vulnerability in persistent but not paroxysmal atrial fibrillation.\n \n \n \n\n\n \n Williams, S. E.; O'Neill, L.; Roney, C. H.; Julia, J.; Metzner, A.; Reissmann, B.; Mukherjee, R. K.; Sim, I.; Whitaker, J.; Wright, M.; Niederer, S.; Sohns, C.; and O'Neill, M.\n\n\n \n\n\n\n J Cardiovasc Electrophysiol, 30(9): 1416-1427. 2019.\n \n\n\n\n
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\n\n\n@article{RN171,\n author = {Williams, S. E. and O'Neill, L. and Roney, C. H. and Julia, J. and Metzner, A. and Reissmann, B. and Mukherjee, R. K. and Sim, I. and Whitaker, J. and Wright, M. and Niederer, S. and Sohns, C. and O'Neill, M.},\n title = {Left atrial effective conducting size predicts atrial fibrillation vulnerability in persistent but not paroxysmal atrial fibrillation},\n journal = {J Cardiovasc Electrophysiol},\n volume = {30},\n number = {9},\n pages = {1416-1427},\n abstract = {BACKGROUND: The multiple wavelets and functional re-entry hypotheses are mechanistic theories to explain atrial fibrillation (AF). If valid, a chamber's ability to support AF should depend upon the left atrial size, conduction velocity (CV), and refractoriness. Measurement of these parameters could provide a new therapeutic target for AF. We investigated the relationship between left atrial effective conducting size (LA(ECS) ), a function of area, CV and refractoriness, and AF vulnerability in patients undergoing AF ablation. METHODS AND RESULTS: Activation mapping was performed in patients with paroxysmal (n = 21) and persistent AF (n = 18) undergoing pulmonary vein isolation. Parameters used for calculating LA(ECS) were: (a) left atrial body area (A); (b) effective refractory period (ERP); and (c) total activation time (T). Global CV was estimated as radicalA/T . Effective atrial conducting size was calculated as LAECS = A/(CV x ERP) . Post ablation, AF inducibility testing was performed. The critical LA(ECS) required for multiple wavelet termination was determined from computational modeling. LA(ECS) was greater in patients with persistent vs paroxysmal AF (4.4 +/- 2.0 cm vs 3.2 +/- 1.4 cm; P = .049). AF was inducible in 14/39 patients. LA(ECS) was greater in AF-inducible patients (4.4 +/- 1.8 cm vs 3.3 +/- 1.7 cm; P = .035, respectively). The difference in LA(ECS) between inducible and noninducible patients was significant in patients with persistent (P = .0046) but not paroxysmal AF (P = .6359). Computational modeling confirmed that LA(ECS) > 4 cm was required for continuation of AF. CONCLUSIONS: LA(ECS) measured post ablation was associated with AF inducibility in patients with persistent, but not paroxysmal AF. These data support a role for this method in electrical substrate assessment in AF patients.},\n DOI = {10.1111/jce.13990},\n year = {2019},\n type = {Journal Article}\n}\n\n\n\n BACKGROUND: The multiple wavelets and functional re-entry hypotheses are mechanistic theories to explain atrial fibrillation (AF). If valid, a chamber's ability to support AF should depend upon the left atrial size, conduction velocity (CV), and refractoriness. Measurement of these parameters could provide a new therapeutic target for AF. We investigated the relationship between left atrial effective conducting size (LA(ECS) ), a function of area, CV and refractoriness, and AF vulnerability in patients undergoing AF ablation. METHODS AND RESULTS: Activation mapping was performed in patients with paroxysmal (n = 21) and persistent AF (n = 18) undergoing pulmonary vein isolation. Parameters used for calculating LA(ECS) were: (a) left atrial body area (A); (b) effective refractory period (ERP); and (c) total activation time (T). Global CV was estimated as radicalA/T . Effective atrial conducting size was calculated as LAECS = A/(CV x ERP) . Post ablation, AF inducibility testing was performed. The critical LA(ECS) required for multiple wavelet termination was determined from computational modeling. LA(ECS) was greater in patients with persistent vs paroxysmal AF (4.4 +/- 2.0 cm vs 3.2 +/- 1.4 cm; P = .049). AF was inducible in 14/39 patients. LA(ECS) was greater in AF-inducible patients (4.4 +/- 1.8 cm vs 3.3 +/- 1.7 cm; P = .035, respectively). The difference in LA(ECS) between inducible and noninducible patients was significant in patients with persistent (P = .0046) but not paroxysmal AF (P = .6359). Computational modeling confirmed that LA(ECS) > 4 cm was required for continuation of AF. CONCLUSIONS: LA(ECS) measured post ablation was associated with AF inducibility in patients with persistent, but not paroxysmal AF. These data support a role for this method in electrical substrate assessment in AF patients.\n
\n\n\n\n \n\n \n \n \n \n \n Reproducibility of Atrial Fibrosis Assessment Using CMR Imaging and an Open Source Platform.\n \n \n \n\n\n \n Sim, I.; Razeghi, O.; Karim, R.; Chubb, H.; Whitaker, J.; O'Neill, L.; Mukherjee, R. K.; Roney, C. H.; Razavi, R.; Wright, M.; O'Neill, M.; Niederer, S.; and Williams, S. E.\n\n\n \n\n\n\n JACC Cardiovasc Imaging, 12(10): 2076-2077. 2019.\n \n\n\n\n
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\n\n\n\n@article{RN357,\n author = {Sim, I. and Razeghi, O. and Karim, R. and Chubb, H. and Whitaker, J. and O'Neill, L. and Mukherjee, R. K. and Roney, C. H. and Razavi, R. and Wright, M. and O'Neill, M. and Niederer, S. and Williams, S. E.},\n title = {Reproducibility of Atrial Fibrosis Assessment Using CMR Imaging and an Open Source Platform},\n journal = {JACC Cardiovasc Imaging},\n volume = {12},\n number = {10},\n pages = {2076-2077},\n DOI = {10.1016/j.jcmg.2019.03.027},\n year = {2019},\n type = {Journal Article}\n}\n\n\n\n \n\n \n \n \n \n \n Left atrial voltage mapping: defining and targeting the atrial fibrillation substrate.\n \n \n \n\n\n \n Sim, I.; Bishop, M.; O'Neill, M.; and Williams, S. E.\n\n\n \n\n\n\n J Interv Card Electrophysiol, 56(3): 213-227. 2019.\n \n\n\n\n
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\n\n\n@article{RN3,\n author = {Sim, I. and Bishop, M. and O'Neill, M. and Williams, S. E.},\n title = {Left atrial voltage mapping: defining and targeting the atrial fibrillation substrate},\n journal = {J Interv Card Electrophysiol},\n volume = {56},\n number = {3},\n pages = {213-227},\n abstract = {Low atrial endocardial bipolar voltage, measured during catheter ablation for atrial fibrillation (AF), is a commonly used surrogate marker for the presence of atrial fibrosis. Low voltage shows many useful associations with clinical outcomes, comorbidities and has links to trigger sites for AF. Several contemporary trials have shown promise in targeting low voltage areas as the substrate for AF ablation; however, the results have been mixed. In order to understand these results, a thorough understanding of voltage mapping techniques, the relationship between low voltage and the pathophysiology of AF, as well as the inherent limitations in voltage measurement are needed. Two key questions must be answered in order to optimally apply voltage mapping as the road map for ablation. First, are the inherent limitations of voltage mapping small enough as to be ignored when targeting specific tissue based on voltage? Second, can conventional criteria, using a binary threshold for voltage amplitude, truly define the extent of the atrial fibrotic substrate? Here, we review the latest clinical evidence with regard to voltage-based ablation procedures before analysing the utility and limitations of voltage mapping. Finally, we discuss omnipole mapping and dynamic voltage attenuation as two possible approaches to resolving these issues.},\n DOI = {10.1007/s10840-019-00537-8},\n year = {2019},\n type = {Journal Article}\n}\n\n\n\n Low atrial endocardial bipolar voltage, measured during catheter ablation for atrial fibrillation (AF), is a commonly used surrogate marker for the presence of atrial fibrosis. Low voltage shows many useful associations with clinical outcomes, comorbidities and has links to trigger sites for AF. Several contemporary trials have shown promise in targeting low voltage areas as the substrate for AF ablation; however, the results have been mixed. In order to understand these results, a thorough understanding of voltage mapping techniques, the relationship between low voltage and the pathophysiology of AF, as well as the inherent limitations in voltage measurement are needed. Two key questions must be answered in order to optimally apply voltage mapping as the road map for ablation. First, are the inherent limitations of voltage mapping small enough as to be ignored when targeting specific tissue based on voltage? Second, can conventional criteria, using a binary threshold for voltage amplitude, truly define the extent of the atrial fibrotic substrate? Here, we review the latest clinical evidence with regard to voltage-based ablation procedures before analysing the utility and limitations of voltage mapping. Finally, we discuss omnipole mapping and dynamic voltage attenuation as two possible approaches to resolving these issues.\n
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\n \n \n\n \n \n \n \n \n Local activation time sampling density for atrial tachycardia contact mapping: how much is enough?.\n \n \n \n\n\n \n Williams, S. E.; Harrison, J. L.; Chubb, H.; Whitaker, J.; Kiedrowicz, R.; Rinaldi, C. A.; Cooklin, M.; Wright, M.; Niederer, S.; and O'Neill, M. D.\n\n\n \n\n\n\n Europace, 20(2): e11-e20. 2018.\n \n\n\n\n
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\n\n\n@article{RN314,\n author = {Williams, S. E. and Harrison, J. L. and Chubb, H. and Whitaker, J. and Kiedrowicz, R. and Rinaldi, C. A. and Cooklin, M. and Wright, M. and Niederer, S. and O'Neill, M. D.},\n title = {Local activation time sampling density for atrial tachycardia contact mapping: how much is enough?},\n journal = {Europace},\n volume = {20},\n number = {2},\n pages = {e11-e20},\n abstract = {AIMS: Local activation time (LAT) mapping forms the cornerstone of atrial tachycardia diagnosis. Although anatomic and positional accuracy of electroanatomic mapping (EAM) systems have been validated, the effect of electrode sampling density on LAT map reconstruction is not known. Here, we study the effect of chamber geometry and activation complexity on optimal LAT sampling density using a combined in silico and in vivo approach. METHODS AND RESULTS: In vivo 21 atrial tachycardia maps were studied in three groups: (1) focal activation, (2) macro-re-entry, and (3) localized re-entry. In silico activation was simulated on a 4x4cm atrial monolayer, sampled randomly at 0.25-10 points/cm2 and used to re-interpolate LAT maps. Activation patterns were studied in the geometrically simple porcine right atrium (RA) and complex human left atrium (LA). Activation complexity was introduced into the porcine RA by incomplete inter-caval linear ablation. In all cases, optimal sampling density was defined as the highest density resulting in minimal further error reduction in the re-interpolated maps. Optimal sampling densities for LA tachycardias were 0.67 +/- 0.17 points/cm2 (focal activation), 1.05 +/- 0.32 points/cm2 (macro-re-entry) and 1.23 +/- 0.26 points/cm2 (localized re-entry), P = 0.0031. Increasing activation complexity was associated with increased optimal sampling density both in silico (focal activation 1.09 +/- 0.14 points/cm2; re-entry 1.44 +/- 0.49 points/cm2; spiral-wave 1.50 +/- 0.34 points/cm2, P < 0.0001) and in vivo (porcine RA pre-ablation 0.45 +/- 0.13 vs. post-ablation 0.78 +/- 0.17 points/cm2, P = 0.0008). Increasing chamber geometry was also associated with increased optimal sampling density (0.61 +/- 0.22 points/cm2 vs. 1.0 +/- 0.34 points/cm2, P = 0.0015). CONCLUSION: Optimal sampling densities can be identified to maximize diagnostic yield of LAT maps. Greater sampling density is required to correctly reveal complex activation and represent activation across complex geometries. Overall, the optimal sampling density for LAT map interpolation defined in this study was approximately 1.0-1.5 points/cm2.},\n DOI = {10.1093/europace/eux037},\n year = {2018},\n type = {Journal Article}\n}\n\n\n\n AIMS: Local activation time (LAT) mapping forms the cornerstone of atrial tachycardia diagnosis. Although anatomic and positional accuracy of electroanatomic mapping (EAM) systems have been validated, the effect of electrode sampling density on LAT map reconstruction is not known. Here, we study the effect of chamber geometry and activation complexity on optimal LAT sampling density using a combined in silico and in vivo approach. METHODS AND RESULTS: In vivo 21 atrial tachycardia maps were studied in three groups: (1) focal activation, (2) macro-re-entry, and (3) localized re-entry. In silico activation was simulated on a 4x4cm atrial monolayer, sampled randomly at 0.25-10 points/cm2 and used to re-interpolate LAT maps. Activation patterns were studied in the geometrically simple porcine right atrium (RA) and complex human left atrium (LA). Activation complexity was introduced into the porcine RA by incomplete inter-caval linear ablation. In all cases, optimal sampling density was defined as the highest density resulting in minimal further error reduction in the re-interpolated maps. Optimal sampling densities for LA tachycardias were 0.67 +/- 0.17 points/cm2 (focal activation), 1.05 +/- 0.32 points/cm2 (macro-re-entry) and 1.23 +/- 0.26 points/cm2 (localized re-entry), P = 0.0031. Increasing activation complexity was associated with increased optimal sampling density both in silico (focal activation 1.09 +/- 0.14 points/cm2; re-entry 1.44 +/- 0.49 points/cm2; spiral-wave 1.50 +/- 0.34 points/cm2, P < 0.0001) and in vivo (porcine RA pre-ablation 0.45 +/- 0.13 vs. post-ablation 0.78 +/- 0.17 points/cm2, P = 0.0008). Increasing chamber geometry was also associated with increased optimal sampling density (0.61 +/- 0.22 points/cm2 vs. 1.0 +/- 0.34 points/cm2, P = 0.0015). CONCLUSION: Optimal sampling densities can be identified to maximize diagnostic yield of LAT maps. Greater sampling density is required to correctly reveal complex activation and represent activation across complex geometries. Overall, the optimal sampling density for LAT map interpolation defined in this study was approximately 1.0-1.5 points/cm2.\n
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\n\n \n \n 2017\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n Intra-Atrial Conduction Delay Revealed by Multisite Incremental Atrial Pacing is an Independent Marker of Remodeling in Human Atrial Fibrillation.\n \n \n \n\n\n \n Williams, S. E.; Linton, N. W. F.; Harrison, J.; Chubb, H.; Whitaker, J.; Gill, J.; Rinaldi, C. A.; Razavi, R.; Niederer, S.; Wright, M.; and O'Neill, M.\n\n\n \n\n\n\n JACC Clin Electrophysiol, 3(9): 1006-1017. 2017.\n \n\n\n\n
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\n\n\n@article{RN313,\n author = {Williams, S. E. and Linton, N. W. F. and Harrison, J. and Chubb, H. and Whitaker, J. and Gill, J. and Rinaldi, C. A. and Razavi, R. and Niederer, S. and Wright, M. and O'Neill, M.},\n title = {Intra-Atrial Conduction Delay Revealed by Multisite Incremental Atrial Pacing is an Independent Marker of Remodeling in Human Atrial Fibrillation},\n journal = {JACC Clin Electrophysiol},\n volume = {3},\n number = {9},\n pages = {1006-1017},\n abstract = {OBJECTIVES: This study sought to characterize direction-dependent and coupling interval-dependent changes in left atrial conduction and electrogram morphology in uniformly classified patients with paroxysmal atrial fibrillation (AF) and normal bipolar voltage mapping. BACKGROUND: Although AF classifications are based on arrhythmia duration, the clinical course, and treatment response vary between patients within these groups. Electrophysiological mechanisms responsible for this variability are incompletely described. METHODS: Intracardiac contact mapping during incremental atrial pacing was used to characterize atrial conduction, activation dispersion, and electrogram morphology in 15 consecutive paroxysmal AF patients undergoing first-time pulmonary vein isolation. Outcome measures were vulnerability to AF induction at electrophysiology study and 2-year follow-up for arrhythmia recurrence. RESULTS: Conduction delay showed a bimodal distribution, occurring at either long (high right atrium pacing: 326 +/- 13 ms; coronary sinus pacing: 319 +/- 16 ms) or short (high right atrium pacing: 275 +/- 11 ms; coronary sinus pacing: 271 +/- 11 ms) extrastimulus coupling intervals. Arrhythmia recurrence was found only in patients with conduction delay at long extrastimulus coupling intervals, and patients with inducible AF were characterized by increased activation dispersion (activation dispersion time: 168 +/- 29 ms vs. 136 +/- 11 ms). Electrogram voltage and duration varied throughout the left atrium, between patients, and with pacing site but were not correlated with AF vulnerability or arrhythmia recurrence. CONCLUSIONS: Within the single clinical entity of paroxysmal AF, incremental atrial pacing identified a spectrum of activation patterns correlating with AF vulnerability and arrhythmia recurrence. In contrast, electrogram morphology (characterized by electrogram voltage and duration) was highly variable and not associated with AF vulnerability or recurrence. An improved understanding of the electrical phenotype in AF could lead to improved mechanistic classifications.},\n DOI = {10.1016/j.jacep.2017.02.012},\n year = {2017},\n type = {Journal Article}\n}\n\n\n\n OBJECTIVES: This study sought to characterize direction-dependent and coupling interval-dependent changes in left atrial conduction and electrogram morphology in uniformly classified patients with paroxysmal atrial fibrillation (AF) and normal bipolar voltage mapping. BACKGROUND: Although AF classifications are based on arrhythmia duration, the clinical course, and treatment response vary between patients within these groups. Electrophysiological mechanisms responsible for this variability are incompletely described. METHODS: Intracardiac contact mapping during incremental atrial pacing was used to characterize atrial conduction, activation dispersion, and electrogram morphology in 15 consecutive paroxysmal AF patients undergoing first-time pulmonary vein isolation. Outcome measures were vulnerability to AF induction at electrophysiology study and 2-year follow-up for arrhythmia recurrence. RESULTS: Conduction delay showed a bimodal distribution, occurring at either long (high right atrium pacing: 326 +/- 13 ms; coronary sinus pacing: 319 +/- 16 ms) or short (high right atrium pacing: 275 +/- 11 ms; coronary sinus pacing: 271 +/- 11 ms) extrastimulus coupling intervals. Arrhythmia recurrence was found only in patients with conduction delay at long extrastimulus coupling intervals, and patients with inducible AF were characterized by increased activation dispersion (activation dispersion time: 168 +/- 29 ms vs. 136 +/- 11 ms). Electrogram voltage and duration varied throughout the left atrium, between patients, and with pacing site but were not correlated with AF vulnerability or arrhythmia recurrence. CONCLUSIONS: Within the single clinical entity of paroxysmal AF, incremental atrial pacing identified a spectrum of activation patterns correlating with AF vulnerability and arrhythmia recurrence. In contrast, electrogram morphology (characterized by electrogram voltage and duration) was highly variable and not associated with AF vulnerability or recurrence. An improved understanding of the electrical phenotype in AF could lead to improved mechanistic classifications.\n
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\n\n\n\n\n \n \n 2015\n \n \n (1)\n \n \n
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\n \n \n\n \n \n \n \n \n The Effect of Contact Force in Atrial Radiofrequency Ablation: Electroanatomical, Cardiovascular Magnetic Resonance, and Histological Assessment in a Chronic Porcine Model.\n \n \n \n\n\n \n Williams, S. E.; Harrison, J.; Chubb, H.; Bloch, L. O.; Andersen, N. P.; Dam, H.; Karim, R.; Whitaker, J.; Gill, J.; Cooklin, M.; Rinaldi, C. A.; Rhode, K.; Wright, M.; Schaeffter, T.; Kim, W. Y.; Jensen, H.; Razavi, R.; and O'Neill, M. D.\n\n\n \n\n\n\n JACC Clin Electrophysiol, 1(5): 421-431. 2015.\n \n\n\n\n
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\n\n\n@article{RN208,\n author = {Williams, S. E. and Harrison, J. and Chubb, H. and Bloch, L. O. and Andersen, N. P. and Dam, H. and Karim, R. and Whitaker, J. and Gill, J. and Cooklin, M. and Rinaldi, C. A. and Rhode, K. and Wright, M. and Schaeffter, T. and Kim, W. Y. and Jensen, H. and Razavi, R. and O'Neill, M. D.},\n title = {The Effect of Contact Force in Atrial Radiofrequency Ablation: Electroanatomical, Cardiovascular Magnetic Resonance, and Histological Assessment in a Chronic Porcine Model},\n journal = {JACC Clin Electrophysiol},\n volume = {1},\n number = {5},\n pages = {421-431},\n abstract = {OBJECTIVES: This study sought to determine the effect of contact force (CF) on atrial lesion size, quality, and transmurality by using a chronic porcine model of radiofrequency ablation. BACKGROUND: CF is a major determinant of ventricular lesion formation, but uncertainty exists regarding the most appropriate CF parameters to safely achieve permanent, transmural lesions in the atria. METHODS: Intercaval linear ablation (30 W, 42 degrees C, 17 ml/min irrigation) was performed in 8 Gottingen minipigs by using a force-sensing catheter with CF >20 g (high force) or <10 g (low force) at alternate ends of the line, separated by an intentional gap. Voltage mapping and cardiovascular magnetic resonance (CMR) imaging were performed pre-ablation, immediately after ablation, and at 2 months' post-procedure. Lesions were sectioned orthogonal to the axis of ablation to assess transmurality. RESULTS: Mean CF was 22.6 +/- 11.4 g and 7.8 +/- 4.0 g in the high and low CF regions. Acute tissue edema was greater with high CF, both caudally (7.0 mm vs. 4.6 mm; p = 0.016) and cranially (6.9 mm vs. 4.6 mm; p = 0.038). There was no difference in chronic lesion size (voltage mapping) or volume (late gadolinium enhancement CMR) between high and low CF regions. There was no difference in scar density (assessed by low-voltage criteria and late gadolinium enhancement signal intensity) or histological transmurality between high and low CF regions. CONCLUSIONS: Although high CF (>20 g) resulted in more acute tissue edema than low CF (<10 g), chronically there was no difference in lesion size, quality, or transmurality. Appropriate CF targets for atrial ablation may be lower than previously thought.},\n DOI = {10.1016/j.jacep.2015.06.003},\n year = {2015},\n type = {Journal Article}\n}\n\n\n\n OBJECTIVES: This study sought to determine the effect of contact force (CF) on atrial lesion size, quality, and transmurality by using a chronic porcine model of radiofrequency ablation. BACKGROUND: CF is a major determinant of ventricular lesion formation, but uncertainty exists regarding the most appropriate CF parameters to safely achieve permanent, transmural lesions in the atria. METHODS: Intercaval linear ablation (30 W, 42 degrees C, 17 ml/min irrigation) was performed in 8 Gottingen minipigs by using a force-sensing catheter with CF >20 g (high force) or <10 g (low force) at alternate ends of the line, separated by an intentional gap. Voltage mapping and cardiovascular magnetic resonance (CMR) imaging were performed pre-ablation, immediately after ablation, and at 2 months' post-procedure. Lesions were sectioned orthogonal to the axis of ablation to assess transmurality. RESULTS: Mean CF was 22.6 +/- 11.4 g and 7.8 +/- 4.0 g in the high and low CF regions. Acute tissue edema was greater with high CF, both caudally (7.0 mm vs. 4.6 mm; p = 0.016) and cranially (6.9 mm vs. 4.6 mm; p = 0.038). There was no difference in chronic lesion size (voltage mapping) or volume (late gadolinium enhancement CMR) between high and low CF regions. There was no difference in scar density (assessed by low-voltage criteria and late gadolinium enhancement signal intensity) or histological transmurality between high and low CF regions. CONCLUSIONS: Although high CF (>20 g) resulted in more acute tissue edema than low CF (<10 g), chronically there was no difference in lesion size, quality, or transmurality. Appropriate CF targets for atrial ablation may be lower than previously thought.\n
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