BackgroundLocalizing the origin of outflow tract ventricular tachycardias (OTVT) is hindered by lack of accuracy of electrocardiographic (ECG) algorithms and infrequent spontaneous premature ventricular complexes (PVCs) during electrophysiological studies.ObjectivesTo prospectively assess the performance of noninvasive electrocardiographic mapping (ECM) in the pre-/periprocedural localization of OTVT origin to guide ablation and to compare the accuracy of ECM with that of published ECG algorithms.MethodsPatients with symptomatic OTVT/PVCs undergoing clinically indicated ablation were recruited. The OTVT/PVC origin was mapped preprocedurally by using ECM, and 3 published ECG algorithms were applied to the 12-lead ECG by 3 blinded electrophysiologists. Ablation was guided by using ECM. The OTVT/PVC origin was defined as the site where ablation caused arrhythmia suppression. Acute success was defined as abolition of ectopy after ablation. Medium-term success was defined as the abolition of symptoms and reduction of PVC to less than 1000 per day documented on Holter monitoring within 6 months.ResultsIn 24 patients (mean age 50 ± 18 years) recruited ECM successfully identified OTVT/PVC origin in 23/24 (96%) (right ventricular outflow tract, 18; left ventricular outflow tract, 6), sublocalizing correctly in 100% of this cohort. Acute ablation success was achieved in 100% of the cases with medium-term success in 22 of 24 patients. PVC burden reduced from 21,837 ± 23,241 to 1143 ± 4039 (P < .0001). ECG algorithms identified the correct chamber of origin in 50%–88% of the patients and sublocalized within the right ventricular outflow tract (septum vs free-wall) in 37%–58%.ConclusionsECM can accurately identify OTVT/PVC origin in the left and the right ventricle pre- and periprocedurally to guide catheter ablation with an accuracy superior to that of published ECG algorithms.

Gordon (2020) Cost-effectiveness of targeted screening for the identification of patients with atrial fibrillation: evaluation of a machine learning risk prediction algorithm,

It is feasible to map the entire left atrium for AVD-GPs before AF ablation. Aggregated data from multiple patients, producing a distribution probability atlas of AVD-GPs, identified three regions with a higher likelihood for finding AVD-GPs and these matched the histological descriptions. This approach could be used to better characterize the autonomic network.

Aims To evaluate the ability of a machine learning algorithm to identify patients at high risk of atrial fibrillation in primary care. Methods A retrospective cohort study was undertaken using the DISCOVER registry to validate an algorithm developed using a Clinical Practice Research Datalink (CPRD) dataset. The validation dataset included primary care patients in London, England aged ≥30 years from 1 January 2006 to 31 December 2013, without a diagnosis of atrial fibrillation in the prior 5 years. Algorithm performance metrics were sensitivity, specificity, positive predictive value, negative predictive value (NPV) and number needed to screen (NNS). Subgroup analysis of patients aged ≥65 years was also performed. Results Of 2,542,732 patients in DISCOVER, the algorithm identified 604,135 patients suitable for risk assessment. Of these, 3.0% (17,880 patients) had a diagnosis of atrial fibrillation recorded before study end. The area under the curve of the receiver operating characteristic was 0.87, compared with 0.83 in algorithm development. The NNS was nine patients, matching the CPRD cohort. In patients aged ≥30 years, the algorithm correctly identified 99.1% of patients who did not have atrial fibrillation (NPV) and 75.0% of true atrial fibrillation cases (sensitivity). Among patients aged ≥65 years ( n = 117,965), the NPV was 96.7% with 91.8% sensitivity. Conclusions This atrial fibrillation risk prediction algorithm, based on machine learning methods, identified patients at highest risk of atrial fibrillation. It performed comparably in a large, real-world population-based cohort and the developmental registry cohort. If implemented in primary care, the algorithm could be an effective tool for narrowing the population who would benefit from atrial fibrillation screening in the United Kingdom.

Understanding the mechanisms responsible for driving AF is key to improving the procedural success for AF ablation. In this review, we look at some of the proposed drivers of AF, the disagreement between experts and the challenges confronted in attempting to map AF. Defining a 'driver' is also controversial, but for the purposes of this review we will consider an AF driver to be either a focal or localised source demonstrating fast, repetitive activity that propagates outward from this source, breaking down in to disorganisation further away from its origin.

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Background Epicardial ganglionated plexuses (GP) have an important role in the pathogenesis of atrial fibrillation (AF). The relationship between anatomical, histological and functional effects of GP is not well known. We previously described atrioventricular (AV) dissociating GP (AVD-GP) locations. In this study, we hypothesised that ectopy triggering GP (ET-GP) are upstream triggers of atrial ectopy/AF and have different anatomical distribution to AVD-GP. Objectives We mapped and characterised ET-GP to understand their neural mechanism in AF and anatomical distribution in the left atrium (LA). Methods 26 patients with paroxysmal AF were recruited. All were paced in the LA with an ablation catheter. High frequency stimulation (HFS) was synchronised to each paced stimulus for delivery within the local atrial refractory period. HFS responses were tagged onto CARTO™ 3D LA geometry. All geometries were transformed onto one reference LA shell. A probability distribution atlas of ET-GP was created. This identified high/low ET-GP probability regions. Results 2302 sites were tested with HFS, identifying 579 (25%) ET-GP. 464 ET-GP were characterised, where 74 (16%) triggered ≥30s AF/AT. Median 97 (IQR 55) sites were tested, identifying 19 (20%) ET-GP per patient. >30% of ET-GP were in the roof, mid-anterior wall, around all PV ostia except in the right inferior PV (RIPV) in the posterior wall. Conclusion ET-GP can be identified by endocardial stimulation and their anatomical distribution, in contrast to AVD-GP, would be more likely to be affected by wide antral circumferential ablation. This may contribute to AF ablation outcomes.

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