High Prevalence of New Clinically Significant Findings in Patients With Embolic Stroke of Unknown Source Evaluated by Cardiac Magnetic Resonance Imaging.

BACKGROUND: Embolic stroke of unknown source (ESUS) accounts for 1 in 6 ischemic strokes. Current guidelines do not recommend routine cardiac magnetic resonance (CMR) imaging in ESUS, and beyond the identification of cardioembolic sources, there are no data assessing new clinical findings from CMR in ESUS. This study aimed to assess the prevalence of new cardiac and noncardiac findings and to determine their impact on clinical care in patients with ESUS. METHODS AND RESULTS: In this prospective, multicenter, observational study, CMR imaging was performed within 3 months of ESUS. All scans were reported according to standard clinical practice. A new clinical finding was defined as one not previously identified through prior clinical evaluation. A clinically significant finding was defined as one resulting in further investigation, follow-up, or treatment. A change in patient care was defined as initiation of medical, interventional, surgical, or palliative care. From 102 patients recruited, 96 underwent CMR imaging. One or more new clinical findings were observed in 59 patients (61%). New findings were clinically significant in 48 (81%) of these patients. Of 40 patients with a new clinically significant cardiac finding, 21 (53%) experienced a change in care (medical therapy, n=15; interventional/surgical procedure, n=6). In 12 patients with a new clinically significant extracardiac finding, 6 (50%) experienced a change in care (medical therapy, n=4; palliative care, n=2). CONCLUSIONS: CMR imaging identifies new clinically significant cardiac and noncardiac findings in half of patients with recent ESUS. Advanced cardiovascular screening should be considered in patients with ESUS. REGISTRATION: URL: https://www.clinicaltrials.gov; Unique identifier: NCT04555538.

ECG Feature Importance Rankings: Cardiologists vs. Algorithms.

Feature importance methods promise to provide a ranking of features according to importance for a given classification task. A wide range of methods exist but their rankings often disagree and they are inherently difficult to evaluate due to a lack of ground truth beyond synthetic datasets. In this work, we put feature importance methods to the test on real-world data in the domain of cardiology, where we try to distinguish three specific pathologies from healthy subjects based on ECG features comparing to features used in cardiologists' decision rules as ground truth. We found that the SHAP and LIME methods and Chi-squared test all worked well together with the native Random forest and Logistic regression feature rankings. Some methods gave inconsistent results, which included the Maximum Relevance Minimum Redundancy and Neighbourhood Component Analysis methods. The permutation-based methods generally performed quite poorly. A surprising result was found in the case of left bundle branch block, where T-wave morphology features were consistently identified as being important for diagnosis, but are not used by clinicians.

Magnetic resonance imaging for diagnostic workup of embolic stroke of undetermined source: A systematic review.

BACKGROUND: Embolic stroke of undetermined source (ESUS) refers to ischemic stroke where the underlying cause of thromboembolism cannot be found despite the recommended diagnostic workup. Unidentified source of emboli hinders clinical decision-making and patient management with detrimental consequences on long-term prognosis. The rapid development and versatility of magnetic resonance imaging (MRI) make it an appealing addition to the diagnostic routine of patients with ESUS for the assessment of potential vascular and cardiac embolic sources. AIMS: To review the use of MRI in the identification of cardiac and vascular embolic sources in ESUS and to assess the reclassification value of MRI examinations added to the conventional workup of ESUS. SUMMARY OF REVIEW: We reviewed the use of cardiac and vascular MRI for the identification of a variety of embolic sources associated with ESUS, including atrial cardiomyopathy, left ventricular pathologies, and supracervical atherosclerosis in carotid and intracranial arteries and in distal thoracic aorta. The additional reclassification after MRI examinations added to the workup of patients with ESUS ranged from 6.1% to 82.3% and varied depending on the combination of imaging modalities. CONCLUSION: MRI techniques allow us to identify additional cardiac and vascular embolic sources and may further decrease the prevalence of patients with the diagnosis of ESUS.

AF and in-hospital mortality in COVID-19 patients.

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.

MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations.

Mechanistic cardiac electrophysiology models allow for personalized simulations of the electrical activity in the heart and the ensuing electrocardiogram (ECG) on the body surface. As such, synthetic signals possess known ground truth labels of the underlying disease and can be employed for validation of machine learning ECG analysis tools in addition to clinical signals. Recently, synthetic ECGs were used to enrich sparse clinical data or even replace them completely during training leading to improved performance on real-world clinical test data. We thus generated a novel synthetic database comprising a total of 16,900 12 lead ECGs based on electrophysiological simulations equally distributed into healthy control and 7 pathology classes. The pathological case of myocardial infraction had 6 sub-classes. A comparison of extracted features between the virtual cohort and a publicly available clinical ECG database demonstrated that the synthetic signals represent clinical ECGs for healthy and pathological subpopulations with high fidelity. The ECG database is split into training, validation, and test folds for development and objective assessment of novel machine learning algorithms.

Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study.

This work presents an open-source software pipeline to create patient-specific left atrial models with fibre orientations and a fibrDEFAULTosis map, suitable for electrophysiology simulations, and quantifies the intra and inter observer reproducibility of the model creation. The semi-automatic pipeline takes as input a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR). Five operators were allocated 20 cases each from a set of 50 CMR datasets to create a total of 100 models to evaluate inter and intra-operator variability. Each output model consisted of: (1) a labelled surface mesh open at the pulmonary veins and mitral valve, (2) fibre orientations mapped from a diffusion tensor MRI (DTMRI) human atlas, (3) fibrosis map extracted from the LGE-CMR scan, and (4) simulation of local activation time (LAT) and phase singularity (PS) mapping. Reproducibility in our pipeline was evaluated by comparing agreement in shape of the output meshes, fibrosis distribution in the left atrial body, and fibre orientations. Reproducibility in simulations outputs was evaluated in the LAT maps by comparing the total activation times, and the mean conduction velocity (CV). PS maps were compared with the structural similarity index measure (SSIM). The users processed in total 60 cases for inter and 40 cases for intra-operator variability. Our workflow allows a single model to be created in 16.72 ± 12.25 min. Similarity was measured with shape, percentage of fibres oriented in the same direction, and intra-class correlation coefficient (ICC) for the fibrosis calculation. Shape differed noticeably only with users' selection of the mitral valve and the length of the pulmonary veins from the ostia to the distal end; fibrosis agreement was high, with ICC of 0.909 (inter) and 0.999 (intra); fibre orientation agreement was high with 60.63% (inter) and 71.77% (intra). The LAT showed good agreement, where the median ± IQR of the absolute difference of the total activation times was 2.02 ± 2.45 ms for inter, and 1.37 ± 2.45 ms for intra. Also, the average ± sd of the mean CV difference was -0.00404 ± 0.0155 m/s for inter, and 0.0021 ± 0.0115 m/s for intra. Finally, the PS maps showed a moderately good agreement in SSIM for inter and intra, where the mean ± sd SSIM for inter and intra were 0.648 ± 0.21 and 0.608 ± 0.15, respectively. Although we found notable differences in the models, as a consequence of user input, our tests show that the uncertainty caused by both inter and intra-operator variability is comparable with uncertainty due to estimated fibres, and image resolution accuracy of segmentation tools.

The Impact of Atrial Fibrillation Treatment Strategies on Cognitive Function.

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.

State of the art paper: Cardiac computed tomography of the left atrium in atrial fibrillation.

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.

Deep learning estimation of three-dimensional left atrial shape from two-chamber and four-chamber cardiac long axis views.

AIMS: Left atrial volume is commonly estimated using the bi-plane area-length method from two-chamber (2CH) and four-chamber (4CH) long axes views. However, this can be inaccurate due to a violation of geometric assumptions. We aimed to develop a deep learning neural network to infer 3D left atrial shape, volume and surface area from 2CH and 4CH views. METHODS AND RESULTS: A 3D UNet was trained and tested using 2CH and 4CH segmentations generated from 3D coronary computed tomography angiography (CCTA) segmentations (n = 1700, with 1400/100/200 cases for training/validating/testing). An independent test dataset from another institution was also evaluated, using cardiac magnetic resonance (CMR) 2CH and 4CH segmentations as input and 3D CCTA segmentations as the ground truth (n = 20). For the 200 test cases generated from CCTA, the network achieved a mean Dice score value of 93.7%, showing excellent 3D shape reconstruction from two views compared with the 3D segmentation Dice of 97.4%. The network also showed significantly lower mean absolute error values of 3.5 mL/4.9 cm2 for LA volume/surface area respectively compared to the area-length method errors of 13.0 mL/34.1 cm2 respectively (P < 0.05 for both). For the independent CMR test set, the network achieved accurate 3D shape estimation (mean Dice score value of 87.4%), and a mean absolute error values of 6.0 mL/5.7 cm2 for left atrial volume/surface area respectively, significantly less than the area-length method errors of 14.2 mL/19.3 cm2 respectively (P < 0.05 for both). CONCLUSIONS: Compared to the bi-plane area-length method, the network showed higher accuracy and robustness for both volume and surface area.

Quantifying the impact of shape uncertainty on predicted arrhythmias.

BACKGROUND: Personalised computer models are increasingly used to diagnose cardiac arrhythmias and tailor treatment. Patient-specific models of the left atrium are often derived from pre-procedural imaging of anatomy and fibrosis. These images contain noise that can affect simulation predictions. There are few computationally tractable methods for propagating uncertainties from images to clinical predictions. METHOD: We describe the left atrium anatomy using our Bayesian shape model that captures anatomical uncertainty in medical images and has been validated on 63 independent clinical images. This algorithm describes the left atrium anatomy using Nmodes=15 principal components, capturing 95% of the shape variance and calculated from 70 clinical cardiac magnetic resonance (CMR) images. Latent variables encode shape uncertainty: we evaluate their posterior distribution for each new anatomy. We assume a normally distributed prior. We use the unscented transform to sample from the posterior shape distribution. For each sample, we assign the local material properties of the tissue using the projection of late gadolinium enhancement CMR (LGE-CMR) onto the anatomy to estimate local fibrosis. To test which activation patterns an atrium can sustain, we perform an arrhythmia simulation for each sample. We consider 34 possible outcomes (31 macro-re-entries, functional re-entry, atrial fibrillation, and non-sustained arrhythmia). For each sample, we determine the outcome by comparing pre- and post-ablation activation patterns following a cross-field stimulus. RESULTS: We create patient-specific atrial electrophysiology models of ten patients. We validate the mean and standard deviation maps from the unscented transform with the same statistics obtained with 12,000 Monte Carlo (ground truth) samples. We found discrepancies <3% and <2% for the mean and standard deviation for fibrosis burden and activation time, respectively. For each patient case, we then compare the predicted outcome from a model built on the clinical data (deterministic approach) with the probability distribution obtained from the simulated samples. We found that the deterministic approach did not predict the most likely outcome in 80% of the cases. Finally, we estimate the influence of each source of uncertainty independently. Fixing the anatomy to the posterior mean and maintaining uncertainty in fibrosis reduced the prediction of self-terminating arrhythmias from ≃14% to ≃7%. Keeping the fibrosis fixed to the sample mean while retaining uncertainty in shape decreased the prediction of substrate-driven arrhythmias from ≃33% to ≃18% and increased the prediction of macro-re-entries from ≃54% to ≃68%. CONCLUSIONS: We presented a novel method for propagating shape uncertainty in atrial models through to uncertainty in numerical simulations. The algorithm takes advantage of the unscented transform to compute the output distribution of the outcomes. We validated the unscented transform as a viable sampling strategy to deal with anatomy uncertainty. We then showed that the prediction computed with a deterministic model does not always coincide with the most likely outcome. Finally, we found that shape uncertainty affects the predictions of macro-re-entries, while fibrosis uncertainty affects the predictions of functional re-entries.

Active Tracking-based cardiac triggering for MR-thermometry during radiofrequency ablation therapy in the left ventricle.

Cardiac MR thermometry shows promise for real-time guidance of radiofrequency ablation of cardiac arrhythmias. This technique uses ECG triggering, which can be unreliable in this situation. A prospective cardiac triggering method was developed for MR thermometry using the active tracking (AT) signal measured from catheter microcoils. In the proposed AT-based cardiac triggering (AT-trig) sequence, AT modules were repeatedly acquired to measure the catheter motion until a cardiac trigger was identified to start cardiac MR thermometry using single-shot echo-planar imaging. The AT signal was bandpass filtered to extract the motion induced by the beating heart, and cardiac triggers were defined as the extremum (peak or valley) of the filtered AT signal. AT-trig was evaluated in a beating heart phantom and in vivo in the left ventricle of a swine during temperature stability experiments (6 locations) and during one ablation. Stability was defined as the standard deviation over time. In the phantom, AT-trig enabled triggering of MR thermometry and resulted in higher temperature stability than an untriggered sequence. In all in vivo experiments, AT-trig intervals matched ECG-derived RR intervals. Mis-triggers were observed in 1/12 AT-trig stability experiments. Comparable stability of MR thermometry was achieved using peak AT-trig (1.0 ± 0.4°C), valley AT-trig (1.1 ± 0.5°C), and ECG triggering (0.9 ± 0.4°C). These experiments show that continuously acquired AT signal for prospective cardiac triggering is feasible. MR thermometry with AT-trig leads to comparable temperature stability as with conventional ECG triggering. AT-trig could serve as an alternative cardiac triggering strategy in situations where ECG triggering is not effective.

Provocation and localization of atrial ectopy in patients with atrial septal defects.

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.

Effect of scar and pacing location on repolarization in a porcine myocardial infarction model.

Background: The effect of chronic ischemic scar on repolarization is unclear, with conflicting results from human and animal studies. An improved understanding of electrical remodeling within scar and border zone tissue may enhance substrate-guided ablation techniques for treatment of ventricular tachycardia. Computational modeling studies have suggested increased dispersion of repolarization during epicardial, but not endocardial, left ventricular pacing, in close proximity to scar. However, the effect of endocardial pacing near scar in vivo is unknown. Objective: The purpose of this study was to investigate the effect of scar and pacing location on local repolarization in a porcine myocardial infarction model. Methods: Six model pigs underwent late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) imaging followed by electroanatomic mapping of the left ventricular endocardium. LGE-CMR images were registered to the anatomic shell and scar defined by LGE. Activation recovery intervals (ARIs), a surrogate for action potential duration, and local ARI gradients were calculated from unipolar electrograms within areas of late gadolinium enhancement (aLGE) and healthy myocardium. Results: There was no significant difference between aLGE and healthy myocardium in mean ARI (304.20 ± 19.44 ms vs 300.59 ± 19.22 ms; P = .43), ARI heterogeneity (23.32 ± 11.43 ms vs 24.85 ± 12.99 ms; P = .54), or ARI gradients (6.18 ± 2.09 vs 5.66 ± 2.32 ms/mm; P = .39). Endocardial pacing distance from scar did not affect ARI gradients. Conclusion: Our findings suggest that changes in ARI are not an intrinsic property of surviving myocytes within scar, and endocardial pacing close to scar does not affect local repolarization.

Atrial CARdiac Magnetic resonance imaging in patients with embolic stroke of unknown source without documented Atrial Fibrillation (CARM-AF): Study design and clinical protocol.

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.

Impact of catheter ablation versus medical therapy on cognitive function in atrial fibrillation: a systematic review.

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.

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

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.

CArdiac MagnEtic resonance assessment of bi-Atrial fibrosis in secundum atrial septal defects patients: CAMERA-ASD study.

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.