Posterior Left Atrial Adipose Tissue Attenuation Assessed by Computed Tomography and Recurrence of Atrial Fibrillation After Catheter Ablation

Mahdiui, Mohammed El; Simon, Judit; Smit, Jeff M.; Kuneman, Jurriën H.; Rosendael, Alexander R. van; Steyerberg, Ewout W.; Geest, Rob J. van der; Száraz, Lili; Herczeg, Szilvia; Szegedi, Nándor; Gellér, László; Delgado, Victoria; Merkely, Béla; Bax, Jeroen J.; Maurovich-Horvat, Pál

doi:10.1161/circep.120.009135

Cited by 37 publications

(34 citation statements)

References 37 publications

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“…Nalliah et al 15 investigated the mechanisms for how epicardial adipose tissue affects atrial fibrillation, showing that higher adipose tissue is associated with slower conduction, higher degrees of electrogram fractionation, increased fibrosis, and increased lateralization of connexin40 gap junctional protein. Further to this, El Mahdiui et al 5 found that posterior left atrial adipose tissue attenuation is predictive of atrial fibrillation recurrence post-ablation.…”

Section: Discussionmentioning

confidence: 85%

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models

Roney

Sim

et al. 2022

Circ: Arrhythmia and Electrophysiology

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Background: Current ablation therapy for atrial fibrillation is suboptimal, and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more interindividual variability. Methods: Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, and 16 long-standing persistent), undergoing first ablation. Patients were followed for 1 year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fiber orientation maps, electrical properties, and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were postprocessed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging, and atrial fibrillation simulation metrics. Results: We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging, and simulation stress tests (average 10-fold cross-validation area under the curve, 0.85±0.09; recall, 0.80±0.13; precision, 0.74±0.13) outperformed those trained to history and imaging (area under the curve, 0.66±0.17) or history alone (area under the curve, 0.61±0.14). Conclusion: A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalize selection for atrial fibrillation ablation.

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Section: Discussionmentioning

confidence: 85%

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models

Roney

Sim

et al. 2022

Circ: Arrhythmia and Electrophysiology

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“…However, a recent study published by El Mahdiui et al showed an association between the EAT density of the LA posterior wall and the risk of recurrence after ablation of any type of AF [ 24 ]. Batal et al had previously reported that only posterior EAT thickness was associated with AF burden [ 21 ].…”

Section: Discussionmentioning

confidence: 99%

Is There an Association between Epicardial Adipose Tissue and Outcomes after Paroxysmal Atrial Fibrillation Catheter Ablation?

Hammache

Pegorer-Sfes²,

Bénali

et al. 2021

JCM

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Background: In patients undergoing paroxysmal atrial fibrillation (PAF) ablation, pulmonary vein isolation (PVI) alone fails in maintaining sinus rhythm in up to one third of patients after a first catheter ablation. Epicardial adipose tissue (EAT), as an endocrine-active organ, could play a role in the recurrence of AF after catheter ablation. Objective: To evaluate the predictive value of clinical, echocardiographic, biological parameters and epicardial fat density measured by computed tomography scan (CT-scan) on AF recurrence in PAF patients who underwent a first pulmonary vein isolation procedure using radiofrequency (RF). Methods: This monocentric retrospective study included all patients undergoing first-time RF PAF ablation at the Nancy University Hospital between March 2015 and December 2018 with one-year follow-up. Results: 389 patients were included, of whom 128 (32.9%) had AF recurrence at one-year follow-up. Neither total-EAT volume (88.6 ± 37.2 cm3 vs. 91.4 ± 40.5 cm3, p = 0.519), nor total-EAT radiodensity (−98.8 ± 4.1 HU vs. −98.8 ± 3.8 HU, p = 0.892) and left atrium-EAT radiodensity (−93.7 ± 4.3 HU vs. −93.4 ± 6.0 HU, p = 0.556) were significantly associated with AF recurrence after PAF ablation. In multivariate analysis, previous cavo-tricuspid isthmus (CTI) ablation, ablation procedure duration, BNP and triglyceride levels remained independently associated with AF recurrence after catheter ablation at 12-months follow-up. Conclusion: Contrary to persistent AF, EAT parameters are not associated with AF recurrence after paroxysmal AF ablation. Thus, the role of the metabolic atrial substrate in PAF pathophysiology appears less obvious than in persistent AF.

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“…EAT volume is associated with incident persistent AF, with recurrent AF after cardioversion, and potentially with recurrent AF after ablation [ 67 , 68 ]. Although show salutary effects on AF from weight loss have been shown in animal models [ 69 ], and left atrial adipose tissue attenuation is associated with human AF recurrence [ 70 ], further studies are needed. Weight loss can reduce the symptoms and severity of AF in patients [ 31 ], although this was not shown in the LOOK-AHEAD trial of 5067 diabetics [ 32 ], or in the recent SORT-AF trial of 133 patients undergoing ablation [ 33 ].…”

Section: Pathophysiology For Af At the Cellular Levelmentioning

confidence: 99%

Identifying Atrial Fibrillation Mechanisms for Personalized Medicine

Deb

Ganesan

Feng

et al. 2021

JCM

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Atrial fibrillation (AF) is a major cause of heart failure and stroke. The early maintenance of sinus rhythm has been shown to reduce major cardiovascular endpoints, yet is difficult to achieve. For instance, it is unclear how discoveries at the genetic and cellular level can be used to tailor pharmacotherapy. For non-pharmacologic therapy, pulmonary vein isolation (PVI) remains the cornerstone of rhythm control, yet has suboptimal success. Improving these therapies will likely require a multifaceted approach that personalizes therapy based on mechanisms measured in individuals across biological scales. We review AF mechanisms from cell-to-organ-to-patient from this perspective of personalized medicine, linking them to potential clinical indices and biomarkers, and discuss how these data could influence therapy. We conclude by describing approaches to improve ablation, including the emergence of several mapping systems that are in use today.

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Posterior Left Atrial Adipose Tissue Attenuation Assessed by Computed Tomography and Recurrence of Atrial Fibrillation After Catheter Ablation

Cited by 37 publications

References 37 publications

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models

Predicting Atrial Fibrillation Recurrence by Combining Population Data and Virtual Cohorts of Patient-Specific Left Atrial Models

Is There an Association between Epicardial Adipose Tissue and Outcomes after Paroxysmal Atrial Fibrillation Catheter Ablation?

Identifying Atrial Fibrillation Mechanisms for Personalized Medicine

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