Novel Computational Analysis of Left Atrial Anatomy Improves Prediction of Atrial Fibrillation Recurrence after Ablation

The left atrium (LA) can change in size and shape due to atrial fibrillation (AF)-induced remodeling. These alterations can be linked to poorer outcomes of AF ablation. In this study, we propose a novel comprehensive computational analysis of LA anatomy to identify what features of LA shape can optimally predict post-ablation AF recurrence. To this end, we construct smooth 3D geometrical models from the segmentation of the LA blood pool captured in pre-procedural MR images. We first apply this methodology to characterize the LA anatomy of 144 AF patients and build a statistical shape model that includes the most salient variations in shape across this cohort. We then perform a discriminant analysis to optimally distinguish between recurrent and non-recurrent patients. From this analysis, we propose a new shape metric called vertical asymmetry, which measures the imbalance of size along the anterior to posterior direction between the superior and inferior left atrial hemispheres. Vertical asymmetry was found, in combination with LA sphericity, to be the best predictor of post-ablation recurrence at both 12 and 24 months (area under the ROC curve: 0.71 and 0.68, respectively) outperforming other shape markers and any of their combinations. We also found that model-derived shape metrics, such as the anterior-posterior radius, were better predictors than equivalent metrics taken directly from MRI or echocardiography, suggesting that the proposed approach leads to a reduction of the impact of data artifacts and noise. This novel methodology contributes to an improved characterization of LA organ remodeling and the reported findings have the potential to improve patient selection and risk stratification for catheter ablations in AF

Ventricular tachycardia burden reduction after substrate ablation: predictors of recurrence.

BACKGROUND Substrate-based ventricular tachycardia (VT) ablation is a first-line treatment in patients with structural cardiac disease and sustained VT refractory to medical therapy. Despite technological improvements and increased knowledge of VT substrate, recurrence still is frequent. Published data are lacking on the possible reduction in VT burden after ablation despite recurrence. OBJECTIVE The purpose of this study was to assess VT burden reduction during long-term follow-up after substrate ablation and identify predictors of VT recurrence. METHODS We analyzed 234 consecutive VT ablation procedures in 207 patients (age 63 6 14.9 years; 92% male; ischemic heart disease in 65%) who underwent substrate ablation in a single center from 2013 to 2018. RESULTS After follow-up of 3.14 6 1.8 years, the VT recurrence rate was 41.4%. Overall, a 99.6% reduction in VT burden (median VT episodes per year: preprocedural 3.546 [1.347-13.951] vs postprocedural 0.001 [0-0.689]; P 5 .001) and a 96.3% decrease in implantable cardioverter-defibrillator (ICD) shocks (preprocedural 1.145 [0.118-4.467] vs postprocedural 0.042 [0-0.111] per year; P 5 .017) were observed. In the subgroup of patients who experienced VT recurrences, VT burden decreased by 69.2% (median VT episodes per year: preprocedural 2.876 [1.105-8.801] vs postprocedural 0.882 [0.505-2.283]; P ,.001). Multivariable analysis showed persistence of late potentials (67% vs 19%; hazard ratio 3.18 [2.18- 6.65]; P ,.001) and lower left ventricular ejection fraction (EF) (30 [25-40] vs 39 [30-50]; P 5 .022) as predictors of VT recurrence. CONCLUSION Despite a high recurrence rate during long-term follow-up, substrate-based VT ablation is related to a large reduction in VT burden and a decrease in ICD therapies. Lower EF and persistence of late potentials are predictors of recurrence. KEYWORDS Arrhythmic burden reduction; Implantable cardioverter-defibrillator shock prevention; Ventricular tachycardia ablation; Ventricular tachycardia recurrence predictors; Ventricular tachycardia storm; Ventricular tachycardia substrate ablatio

Impact of left atrial volume, sphericity, and fibrosis on the outcome of catheter ablation for atrial fibrillation

INTRODUCTION: To investigate the relation between left atrial (LA) volume, sphericity, and fibrotic content derived from contrast-enhanced cardiac magnetic resonance imaging (CE-CMR) and their impact on the outcome of catheter ablation for atrial fibrillation (AF). METHODS AND RESULTS: In 83 patients undergoing catheter ablation for AF, CE-CMR was used to assess LA volume, sphericity, and fibrosis. There was a significant correlation between LA volume and sphericity (R = 0.535, P < 0.001) and between LA volume and fibrosis (R = 0.241, P = 0.029). Multivariate analyses demonstrated that LA volume was the strongest independent predictor of AF recurrence after catheter ablation (1.019, P = 0.018). CONCLUSION: LA volume, sphericity, and fibrosis were closely related; however, LA volume was the strongest predictor of AF recurrence after catheter ablation

Benefit of left atrial roof linear ablation in paroxysmal atrial fibrillation: a prospective, randomized study

Background Isolation of the pulmonary veins (PVs) for the treatment of atrial fibrillation (AF) is often supplemented with linear lesions within the left atrium (LA). However, there are conflicting data on the effects of creating a roof line (RL) joining the superior PVs in paroxysmal atrial fibrillation (PAF). Methods and Results A cohort of 120 patients with drug-refractory PAF referred for ablation were prospectively randomized into 2 strategies: (1) PV isolation in combination with RL ablation (LA roof ablation [LARA]-1: 59 patients) or (2) PV isolation (LARA-2: 61 patients). Follow-up was performed at 1, 3, and 6 months after the procedure and every 6 months thereafter. After a 3-month blanking period, recurrence was defined as the ocurrence of any atrial tachyarrhythmia lasting ≥30 seconds. PV isolation was achieved in 89% and complete RL block in 81%. RF duration, fluoroscopy, and procedural times were slightly, but not significantly, longer in the LARA-1 group. After 15±10 months, there was no difference in the arrhythmia-free survival after a single AF ablation procedure (LARA-1: 59% vs. LARA-2: 56% at 12 months; log rank P=0.77). The achievement of complete RL block did not influence the results. The incidence of LA macroreentrant tachycardias was 5.1% in the LARA-1 group (n=3) versus 8.2% in the LARA-2 (n=5) (P=ns). Univariate analysis only identified AF duration as a covariate associated with arrhythmia recurrence (hazard ratio, 1.01 [95% confidence interval, 1.002 to 1.012]; P<0.01). Conclusion The linear block at the LA roof is not associated with an improved clinical outcome compared with PV isolation alone

Emerging risk factors and the dose-response relationship between physical activity and lone atrial fibrillation: a prospective case-control study

A history of a parts per thousand yen2000 h of vigorous endurance training, tall stature, abdominal obesity, and OSA are frequently encountered as risk factors in patients with Ln-AF. Fewer than 2000 total hours of high-intensity endurance training associates with reduced Ln-AF risk

Scar channels in cardiac magnetic resonance to predict appropriate therapies in primary prevention.

Background Scar characteristics analyzed by late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) are related with ventricular arrhythmias. Current guidelines are based only on the left ventricular ejection fraction to recommend an implantable cardioverter-defibrillator (ICD) in primary prevention. Objectives Our study aims to analyze the role of imaging to stratify arrhythmogenic risk in patients with ICD for primary prevention. Methods From 2006 to 2017, we included 200 patients with LGE-CMR before ICD implantation for primary prevention. The scar, border zone, core, and conducting channels (CCs) were automatically measured by a dedicated software. Results The mean age was 60.9 ± 10.9 years; 81.5% (163) were men; 52% (104) had ischemic cardiomyopathy. The mean left ventricular ejection fraction was 29% ± 10.1%. After a follow-up of 4.6 ± 2 years, 46 patients (22%) reached the primary end point (appropriate ICD therapy). Scar mass (36.2 ± 19 g vs 21.7 ± 10 g; P 10 g (25.31% vs 5.26%; hazard ratio 4.74; P = .034) and the presence of CCs (34.75% vs 8.93%; hazard ratio 4.07; P = .003) were also strongly associated with the primary end point. However, patients without channels and with scar mass < 10 g had a very low rate of appropriate therapies (2.8%). Conclusion Scar characteristics analyzed by LGE-CMR are strong predictors of appropriate therapies in patients with ICD in primary prevention. The absence of channels and scar mass < 10 g can identify patients at a very low risk of ventricular arrhythmias in this population

Dual ventricular response: another road to supraventricular tachycardia in dual atrioventricular nodal physiology

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Novel Computational Analysis of Left Atrial Anatomy Improves Prediction of Atrial Fibrillation Recurrence after Ablation

The left atrium (LA) can change in size and shape due to atrial fibrillation (AF)-induced remodeling. These alterations can be linked to poorer outcomes of AF ablation. In this study, we propose a novel comprehensive computational analysis of LA anatomy to identify what features of LA shape can optimally predict post-ablation AF recurrence. To this end, we construct smooth 3D geometrical models from the segmentation of the LA blood pool captured in pre-procedural MR images. We first apply this methodology to characterize the LA anatomy of 144 AF patients and build a statistical shape model that includes the most salient variations in shape across this cohort. We then perform a discriminant analysis to optimally distinguish between recurrent and non-recurrent patients. From this analysis, we propose a new shape metric called vertical asymmetry, which measures the imbalance of size along the anterior to posterior direction between the superior and inferior left atrial hemispheres. Vertical asymmetry was found, in combination with LA sphericity, to be the best predictor of post-ablation recurrence at both 12 and 24 months (area under the ROC curve: 0.71 and 0.68, respectively) outperforming other shape markers and any of their combinations. We also found that model-derived shape metrics, such as the anterior-posterior radius, were better predictors than equivalent metrics taken directly from MRI or echocardiography, suggesting that the proposed approach leads to a reduction of the impact of data artifacts and noise. This novel methodology contributes to an improved characterization of LA organ remodeling and the reported findings have the potential to improve patient selection and risk stratification for catheter ablations in AF

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.This work has been funded by Generalitat Valenciana Grant AICO/2021/318 (Consolidables 2021) and Grant PID2020-114291RB-I00 funded by MCIN/10.13039/501100011033 and by “ERDF A way of making Europe”