Publications
32 results found
Sau A, Ahmed A, Chen JY, et al., 2023, Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients, European Heart Journal - Digital Health
<jats:title>Abstract</jats:title> <jats:sec> <jats:title>Aims</jats:title> <jats:p>Implantable cardioverter defibrillator (ICD) therapies have been associated with increased mortality and should be minimized when safe to do so. We hypothesized that machine learning-derived ventricular tachycardia (VT) cycle length (CL) variability metrics could be used to discriminate between sustained and spontaneously terminating VT.</jats:p> </jats:sec> <jats:sec> <jats:title>Methods and results</jats:title> <jats:p>In this single-centre retrospective study, we analysed data from 69 VT episodes stored on ICDs from 27 patients (36 spontaneously terminating VT, 33 sustained VT). Several VT CL parameters including heart rate variability metrics were calculated. Additionally, a first order auto-regression model was fitted using the first 10 CLs. Using features derived from the first 10 CLs, a random forest classifier was used to predict VT termination. Sustained VT episodes had more stable CLs. Using data from the first 10 CLs only, there was greater CL variability in the spontaneously terminating episodes (mean of standard deviation of first 10 CLs: 20.1 ± 8.9 vs. 11.5 ± 7.8 ms, P &lt; 0.0001). The auto-regression coefficient was significantly greater in spontaneously terminating episodes (mean auto-regression coefficient 0.39 ± 0.32 vs. 0.14 ± 0.39, P &lt; 0.005). A random forest classifier with six features yielded an accuracy of 0.77 (95% confidence interval 0.67 to 0.87) for prediction of VT termination.</jats:p> </jats:sec> <jats:sec> <jats:title>Conclusion</jats:title> <jats:p>Ventricular tachycardia CL variability and instability are associated with spontane
Shi X, Sau A, Li X, et al., 2023, Information theory-based direct causality measure to assess cardiac fibrillation dynamics., J R Soc Interface, Vol: 20
Understanding the mechanism sustaining cardiac fibrillation can facilitate the personalization of treatment. Granger causality analysis can be used to determine the existence of a hierarchical fibrillation mechanism that is more amenable to ablation treatment in cardiac time-series data. Conventional Granger causality based on linear predictability may fail if the assumption is not met or given sparsely sampled, high-dimensional data. More recently developed information theory-based causality measures could potentially provide a more accurate estimate of the nonlinear coupling. However, despite their successful application to linear and nonlinear physical systems, their use is not known in the clinical field. Partial mutual information from mixed embedding (PMIME) was implemented to identify the direct coupling of cardiac electrophysiology signals. We show that PMIME requires less data and is more robust to extrinsic confounding factors. The algorithms were then extended for efficient characterization of fibrillation organization and hierarchy using clinical high-dimensional data. We show that PMIME network measures correlate well with the spatio-temporal organization of fibrillation and demonstrated that hierarchical type of fibrillation and drivers could be identified in a subset of ventricular fibrillation patients, such that regions of high hierarchy are associated with high dominant frequency.
Stabenau HF, Sau A, Kramer DB, et al., 2023, Limits of the spatial ventricular gradient and QRST angles in patients with normal electrocardiograms and no known cardiovascular disease stratified by age, sex, and race, JOURNAL OF CARDIOVASCULAR ELECTROPHYSIOLOGY, ISSN: 1045-3873
Sau A, Kapadia S, Al-Aidarous S, et al., 2023, Temporal trends and lesion sets for persistent atrial fibrillation ablation: a meta-analysis with trial sequential analysis and meta-regression, Circulation: Arrhythmia and Electrophysiology, Vol: 16, Pages: 536-545, ISSN: 1941-3084
BACKGROUND: Ablation for persistent atrial fibrillation (PsAF) has been performed for over 20 years, although success rates have remained modest. Several adjunctive lesion sets have been studied but none have become standard of practice. We sought to describe how the efficacy of ablation for PsAF has evolved in this time period with a focus on the effect of adjunctive ablation strategies. METHODS: Databases were searched for prospective studies of PsAF ablation. We performed meta-regression and trial sequential analysis. RESULTS: A total of 99 studies (15 424 patients) were included. Ablation for PsAF achieved the primary outcome (freedom of atrial fibrillation/atrial tachycardia rate at 12 months follow-up) in 48.2% (5% CI, 44.0-52.3). Meta-regression showed freedom from atrial arrhythmia at 12 months has improved over time, while procedure time and fluoroscopy time have significantly reduced. Through the use of cumulative meta-analyses and trial sequential analysis, we show that some ablation strategies may initially seem promising, but after several randomized controlled trials may be found to be ineffective. Trial sequential analysis showed that complex fractionated atrial electrogram ablation is ineffective and further study of this treatment would be futile, while posterior wall isolation currently does not have sufficient evidence for routine use in PsAF ablation. CONCLUSIONS: Overall success rates from PsAF ablation and procedure/fluoroscopy times have improved over time. However, no adjunctive lesion set, in addition to pulmonary vein isolation, has been conclusively demonstrated to be beneficial. Through the use of trial sequential analysis, we highlight the importance of adequately powered randomized controlled trials, to avoid reaching premature conclusions, before widespread adoption of novel therapies.
Sau A, Amoiradaki K, Ardissino M, et al., 2023, British Cardiovascular Society/British Heart Foundation/British Atherosclerosis Society/British Society for Cardiovascular Research Young Investigator Award 2023, HEART, ISSN: 1355-6037
Sau A, Ng FS, 2023, The emerging role of artificial intelligence-enabled electrocardiograms in healthcare, BMJ Medicine, Vol: 2, ISSN: 2754-0413
Sau A, Pastika L, Ng FS, 2023, Atrial fibrillation phenotypes: the route to personalised care?, HEART, ISSN: 1355-6037
Sau A, Ng FS, 2023, Response to letter by Saumarez et al. entitled 'Regarding the editorial by Sau and Ng. "Hypertrophic cardiomyopathy risk stratification based on clinical or dynamic electrophysiological features: two sides of the same coin"', EUROPACE, Vol: 25, ISSN: 1099-5129
Sau A, 2023, Artificial intelligence-enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia, Cardiovascular Digital Health Journal, Vol: 4, Pages: 60-67, ISSN: 2666-6936
BackgroundAccurately determining arrhythmia mechanism from a 12-lead electrocardiogram (ECG) of supraventricular tachycardia can be challenging. We hypothesized a convolutional neural network (CNN) can be trained to classify atrioventricular re-entrant tachycardia (AVRT) vs atrioventricular nodal re-entrant tachycardia (AVNRT) from the 12-lead ECG, when using findings from the invasive electrophysiology (EP) study as the gold standard.MethodsWe trained a CNN on data from 124 patients undergoing EP studies with a final diagnosis of AVRT or AVNRT. A total of 4962 5-second 12-lead ECG segments were used for training. Each case was labeled AVRT or AVNRT based on the findings of the EP study. The model performance was evaluated against a hold-out test set of 31 patients and compared to an existing manual algorithm.ResultsThe model had an accuracy of 77.4% in distinguishing between AVRT and AVNRT. The area under the receiver operating characteristic curve was 0.80. In comparison, the existing manual algorithm achieved an accuracy of 67.7% on the same test set. Saliency mapping demonstrated the network used the expected sections of the ECGs for diagnoses; these were the QRS complexes that may contain retrograde P waves.ConclusionWe describe the first neural network trained to differentiate AVRT from AVNRT. Accurate diagnosis of arrhythmia mechanism from a 12-lead ECG could aid preprocedural counseling, consent, and procedure planning. The current accuracy from our neural network is modest but may be improved with a larger training dataset.
Sau A, Ng FS, 2023, Hypertrophic cardiomyopathy risk stratification based on clinical or dynamic electrophysiological features: two sides of the same coin, EUROPACE, ISSN: 1099-5129
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Wu H, Patel KHK, Li X, et al., 2022, A fully-automated paper ECG digitisation algorithm using deep learning, Scientific Reports, Vol: 12, ISSN: 2045-2322
There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects.
Sau A, Ibrahim S, Ahmed A, et al., 2022, Artificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanisms, European Heart Journal – Digital Health, Vol: 3, Pages: 405-414, ISSN: 2634-3916
Aims:Accurately determining atrial arrhythmia mechanisms from a 12-lead electrocardiogram (ECG) can be challenging. Given the high success rate of cavotricuspid isthmus (CTI) ablation, identification of CTI-dependent typical atrial flutter (AFL) is important for treatment decisions and procedure planning. We sought to train a convolutional neural network (CNN) to classify CTI-dependent AFL vs. non-CTI dependent atrial tachycardia (AT), using data from the invasive electrophysiology (EP) study as the gold standard.Methods and results:We trained a CNN on data from 231 patients undergoing EP studies for atrial tachyarrhythmia. A total of 13 500 five-second 12-lead ECG segments were used for training. Each case was labelled CTI-dependent AFL or non-CTI-dependent AT based on the findings of the EP study. The model performance was evaluated against a test set of 57 patients. A survey of electrophysiologists in Europe was undertaken on the same 57 ECGs. The model had an accuracy of 86% (95% CI 0.77–0.95) compared to median expert electrophysiologist accuracy of 79% (range 70–84%). In the two thirds of test set cases (38/57) where both the model and electrophysiologist consensus were in agreement, the prediction accuracy was 100%. Saliency mapping demonstrated atrial activation was the most important segment of the ECG for determining model output.Conclusion:We describe the first CNN trained to differentiate CTI-dependent AFL from other AT using the ECG. Our model matched and complemented expert electrophysiologist performance. Automated artificial intelligence-enhanced ECG analysis could help guide treatment decisions and plan ablation procedures for patients with organized atrial arrhythmias.
Falkenberg McGillivray M, Coleman JA, Dobson S, et al., 2022, Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps, PLoS One, Vol: 17, Pages: 1-24, ISSN: 1932-6203
Micro-anatomical reentry has been identified as a potential driver of atrial fibrillation (AF). In this paper, we introduce a novel computational method which aims to identify which atrial regions are most susceptible to micro-reentry. The approach, which considers the structural basis for micro-reentry only, is based on the premise that the accumulation of electrically insulating interstitial fibrosis can be modelled by simulating percolation-like phenomena on spatial networks. Our results suggest that at high coupling, where micro-reentry is rare, the micro-reentrant substrate is highly clusteredin areas where the atrial walls are thin and have convex wall morphology, likely facilitating localised treatment via ablation. However, as transverse connections between fibres are removed, mimicking the accumulation of interstitial fibrosis, the substrate becomes less spatially clustered, and the bias to forming in thin, convex regions of the atria is reduced, possibly restricting the efficacy of localised ablation. Comparing our algorithm on image-based models with and without atrial fibre structure, we find thatstrong longitudinal fibre coupling can suppress the micro-reentrant substrate, whereas regions with disordered fibre orientations have an enhanced risk of micro-reentry. With further development, these methods may be useful for modelling the temporal development of the fibrotic substrate on an individualised basis.
Sau A, Kaura A, Ahmed A, et al., 2022, Prognostic significance of ventricular arrhythmias in 13444 patients with acute coronary syndrome: a retrospective cohort study based on routine clinical data (NIHR Health Informatics Collaborative VA-ACS Study), Journal of the American Heart Association, Vol: 11, Pages: 1-19, ISSN: 2047-9980
Background: A minority of acute coronary syndrome (ACS) cases are associated with ventricular arrhythmias (VA) and/or cardiac arrest (CA). We investigated the effect of VA/CA at time of ACS on long-term outcomes.Methods and Results: We analysed routine clinical data from 5 NHS Trusts in the United Kingdom, collected between 2010 and 2017, by the National Institute for Health Research Health Informatics Collaborative (NIHR HIC).13,444 patients with ACS, of which 376 (2.8%) had concurrent VA, survived to hospital discharge and were followed up for a median of 3.42 years. Patients with VA or CA at index presentation had significantly increased risks of subsequent VA during follow-up (VA group: adjusted HR 4.15, 95% CI 2.42-7.09, CA group: adjusted HR 2.60 95% CI 1.23-5.48). Patients who suffered a CA in the context of ACS and survived to discharge also had a 36% increase in long-term mortality (adjusted hazard ratio 1.36 (95% 1.04-1.78)), though the concurrent diagnosis of VA alone during ACS did not affect all-cause mortality (adjusted HR 1.03, 95% CI 0.80-1.33). Conclusions: Patients who develop VA or CA during ACS, who survive to discharge, have increased risks of subsequent VA, while those who have CA during ACS also have an increase in long-term mortality. These individuals may represent a subgroup at greater risk of subsequent arrhythmic events due to intrinsically lower thresholds for developing VA.
Patel KHK, Reddy RK, Sau A, et al., 2022, Obesity as a risk factor for cardiac arrhythmias., BMJ Med, Vol: 1
Obesity is global health problem with an estimated three billion people worldwide being classified as overweight or obese. In addition to being associated with a range of adverse health outcomes, obesity is linked to higher risks of atrial and ventricular arrhythmias, as well as sudden cardiac death. Obesity is a multifactorial disease that often co-exists with hypertension, diabetes, and sleep apnoea, which are also independent risk factors for cardiac arrhythmias. Nevertheless, compelling evidence suggests that increasing adiposity is an independent proarrhythmic risk factor and that weight loss can be a mitigating and preventative intervention to reduce arrhythmia incidence. This review briefly outlines the economic and social burden of obesity and summarises evidence for the direct and indirect effects of increasing adiposity on risk of atrial and ventricular arrhythmias. The paper also summarises the evidence for electrocardiographic changes indicative of obesity-related atrial and ventricular remodelling and how weight reduction and management of comorbidity might reduce arrhythmic burden.
Li X, Shi X, Handa BS, et al., 2021, Classification of fibrillation organisation using electrocardiograms to guide mechanism-directed treatments, Frontiers in Physiology, Vol: 12, Pages: 1-14, ISSN: 1664-042X
Background: Atrial fibrillation (AF) and ventricular fibrillation (VF) are complex heart rhythm disorders and may be sustained by distinct electrophysiological mechanisms. Disorganised self-perpetuating multiple-wavelets and organised rotational drivers (RDs) localising to specific areas are both possible mechanisms by which fibrillation is sustained. Determining the underlying mechanisms of fibrillation may be helpful in tailoring treatment strategies. We investigated whether global fibrillation organisation, a surrogate for fibrillation mechanism, can be determined from electrocardiograms (ECGs) using band-power (BP) feature analysis and machine learning.Methods: In this study, we proposed a novel ECG classification framework to differentiate fibrillation organisation levels. BP features were derived from surface ECGs and fed to a linear discriminant analysis classifier to predict fibrillation organisation level. Two datasets, single-channel ECGs of rat VF (n = 9) and 12-lead ECGs of human AF (n = 17), were used for model evaluation in a leave-one-out (LOO) manner.Results: The proposed method correctly predicted the organisation level from rat VF ECG with the sensitivity of 75%, specificity of 80%, and accuracy of 78%, and from clinical AF ECG with the sensitivity of 80%, specificity of 92%, and accuracy of 88%.Conclusion: Our proposed method can distinguish between AF/VF of different global organisation levels non-invasively from the ECG alone. This may aid in patient selection and guiding mechanism-directed tailored treatment strategies.
Sau A, Howard J, Al-Aidarous S, et al., 2019, Meta-analysis of randomized controlled trials of atrial fibrillation ablation with pulmonary vein isolation versus without, JACC: Clinical Electrophysiology, Vol: 5, Pages: 968-976, ISSN: 2405-5018
ObjectivesThis meta-analysis examined the ability of pulmonary vein isolation (PVI) to prevent atrial fibrillation in randomized controlled trials (RCTs) in which the patients not receiving PVI nevertheless underwent a procedure.BackgroundPVI is a commonly used procedure for the treatment of atrial fibrillation (AF), and its efficacy has usually been judged against therapy with anti-arrhythmic drugs in open-label trials. There have been several RCTs of AF ablation in which both arms received an ablation, but the difference between the treatment arms was inclusion or omission of PVI. These trials of an ablation strategy with PVI versus an ablation strategy without PVI may provide a more rigorous method for evaluating the efficacy of PVI.MethodsMedline and Cochrane databases were searched for RCTs comparing ablation including PVI with ablation excluding PVI. The primary efficacy endpoint was freedom from atrial fibrillation (AF) and atrial tachycardia at 12 months. A random-effects meta-analysis was performed using the restricted maximum likelihood estimator.ResultsOverall, 6 studies (610 patients) met inclusion criteria. AF recurrence was significantly lower with an ablation including PVI than an ablation without PVI (RR: 0.54; 95% confidence interval [CI]: 0.33 to 0.89; p 1⁄4 0.0147; I2 1⁄4 79.7%). Neither the type of AF (p 1⁄4 0.48) nor the type of non-PVI ablation (p 1⁄4 0.21) was a significant moderator of the effect size. In 3 trials the non-PVI ablation procedure was performed in both arms, whereas PVI was performed in only 1 arm. In these studies, AF recurrence was significantly lower when PVI was included (RR: 0.32; 95% CI: 0.14 to 0.73; p 1⁄4 0.007, I2 78%ConclusionIn RCTs where both arms received an ablation, and therefore an expectation amongst patients and doctors of benefit, being randomized to PVI had a striking effect, reducing AF recurrence by a half.
Sau A, Al-Aidarous S, Howard J, et al., 2019, Optimum lesion set and predictors of outcome in persistent atrial fibrillation ablation: a meta-regression analysis, Europace, Vol: 21, Pages: 1176-1184, ISSN: 1099-5129
AIMS: Ablation of persistent atrial fibrillation (PsAF) has been performed by many techniques with varying success rates. This may be due to ablation techniques, patient demographics, comorbidities, and trial design. We conducted a meta-regression of studies of PsAF ablation to elucidate the factors affecting atrial fibrillation (AF) recurrence. METHODS AND RESULTS : Databases were searched for prospective studies of PsAF ablation. A meta-regression was performed. Fifty-eight studies (6767 patients) were included. Complex fractionated atrial electrogram (CFAE) ablation reduced freedom from AF by 8.9% [95% confidence interval (CI) -15 to -2.3, P = 0.009). Left atrial appendage [LAA isolation (three study arms)] increased freedom from AF by 39.5% (95% CI 9.1-78.4, P = 0.008). Posterior wall isolation (PWI) (eight study arms) increased freedom from AF by 19.4% (95% CI 3.3-38.1, P = 0.017). Linear ablation or ganglionated plexi ablation resulted in no significant effect on freedom from AF. More extensive ablation increased intraprocedural AF termination; however, intraprocedural AF termination was not associated with improved outcomes. Increased left atrial diameter was associated with a reduction in freedom from AF by 4% (95% CI -6.8% to -1.1%, P = 0.007) for every 1 mm increase in diameter. CONCLUSION : Linear ablation, PWI, and CFAE ablation improves intraprocedural AF termination, but such termination does not predict better long-term outcomes. Study arms including PWI or LAA isolation in the lesion set were associated with improved outcomes in terms of freedom from AF; however, further randomized trials are required before these can be routinely recommended. Left atrial size is the most important marker of AF chronicity influencing outcomes.
Sau A, Sikkel MB, 2019, Let's get down to the nitty-gritty in persistent atrial fibrillation: the continuous criticalmass of the atria-reply, Europace, Vol: 21, Pages: 1280-1280, ISSN: 1099-5129
Sau A, Howard J, Al-Aidarous S, et al., 2019, Efficacy of pulmonary vein isolation in preventing atrial fibrillation: meta-analysis of randomized controlled trials with an invasive control procedure, Annual Conference of the British-Cardiovascular-Society (BCS) - Digital Health Revolution, Publisher: BMJ Publishing Group, Pages: A31-A31, ISSN: 1355-6037
Introduction Pulmonary vein isolation (PVI) is a commonly used element in treatment of atrial fibrillation (AF) but has never been tested in an intentionally placebo (sham) controlled trial. Nevertheless there have been several randomized controlled trials (RCTs) in which both arms receive an ablation procedure but the only difference between treatment arms is inclusion or omission of PVI. As long as both doctor and patient have reason to believe that the procedures in both arms are effective, such RCTs could be an effective proxy for placebo controlled trials.Methods Medline and Cochrane databases were searched for RCTs comparing catheter ablation including PVI with left atrial ablation excluding PVI. The primary efficacy endpoint was freedom from AF/atrial tachycardia at 6 months. A random-effects meta-analysis was performed using the restricted maximum likelihood (REML) estimator.Results Overall, seven studies (909 patients) met inclusion criteria. Across the 7 trials, mean age was 57.3, 70.2% of participants were male. In four trials (352 patients) the non-PVI ablation procedure was performed in both arms, while PVI was performed in only one arm. The non-PVI ablation procedures were complex fractionated atrial electrogram ablation (2 studies), ganglionated plexi ablation (1 study) and focal impulse and rotor modulation (1 study). In these, AF recurrence was significantly lower when PVI was included (RR 0.48, 95% CI 0.26-0.90, I2 64.4%)In an analysis of all 7 studies, AF recurrence was significantly lower in ablation with an ablation strategy including PVI compared to one without PVI (Figure 1, RR 0.67, 95% CI 0.53-0.85, p = 0.001, I2 0%). Neither type of AF (persistent vs. paroxysmal, p=0.43) nor type of non-PVI ablation (p=0.35) were significant moderators of the effect size. A sensitivity analysis omitting each study in turn showed similar results to the primary analysis. In particular exclusion of the retracted OASIS trial showed results similar to the primar
Sau A, Taraborrelli P, Moore P, et al., 2018, Cardioinhibitory and vasodepressor responses to different stressors on head-up tilt, EUROPACE, Vol: 20, Pages: 115-115, ISSN: 1099-5129
Sau A, Sikkel MB, Luther V, et al., 2017, The sawtooth EKG pattern of typical atrial flutter is not related to slow conduction velocity at the cavotricuspid isthmus., Journal of Cardiovascular Electrophysiology, Vol: 28, Pages: 1445-1453, ISSN: 1045-3873
INTRODUCTION: We hypothesized that very high density mapping of typical atrial flutter (AFL) would facilitate a more complete understanding of its circuit. Such very high density mapping was performed with the Rhythmia mapping system using its 64 electrode basket catheter. METHODS AND RESULTS: Data were acquired from 13 patients in AFL. Functional anatomy of the right atrium (RA) was readily identified during mapping including the Crista Terminalis and Eustachian ridge. The leading edge of the activation wavefront was identified without interruption and its conduction velocity (CV) calculated. CV was not different at the cavotricuspid isthmus (CTI) compared to the remainder of the RA (1.02 vs. 1.03 m/s, p = 0.93). The sawtooth pattern of the surface EKG flutter waves were compared to the position of the dominant wavefront. The downslope of the surface EKG flutter waves represented on average, 73% ± 9% of the total flutter cycle length. During the downslope the activation wavefront travelled significantly further than during the upslope (182 ± 21 ms vs. 68 ± 29 ms, p < 0.0001) with no change in conduction velocity between the two phases (0.88 vs. 0.91 m/s, p = 0.79). CONCLUSION: CV at the CTI is not slower than other RA regions during typical AFL. The gradual downslope of the sawtooth EKG is not due to slow conduction at the CTI suggesting that success of ablation at this site relates to anatomical properties rather than presence of a "slow isthmus". This article is protected by copyright. All rights reserved.
Sikkel MB, Luther V, Sau A, et al., 2017, High-Density Electroanatomical Mapping to Identify Point of Epicardial to Endocardial Breakthrough in Perimitral Flutter, JACC: Clinical Electrophysiology, Vol: 3, Pages: 637-639, ISSN: 2405-500X
Mereu R, Taraborrelli P, Sau A, et al., 2016, Diagnostic role of head-up tilt test in patients with cough syncope, EUROPACE, Vol: 18, Pages: 1273-1279, ISSN: 1099-5129
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Sau A, Mereu R, Taraborrelli P, et al., 2016, A long-term follow-up of patients with prolonged asystole of greater than 15 s on head-up tilt testing, INTERNATIONAL JOURNAL OF CARDIOLOGY, Vol: 203, Pages: 482-485, ISSN: 0167-5273
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- Citations: 3
Lindsay AC, Sriharan M, Lazoura O, et al., 2015, Clinical and economic consequences of non-cardiac incidental findings detected on cardiovascular computed tomography performed prior to transcatheter aortic valve implantation (TAVI), INTERNATIONAL JOURNAL OF CARDIOVASCULAR IMAGING, Vol: 31, Pages: 1435-1446, ISSN: 1569-5794
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- Citations: 22
Sau A, Qureshi N, Roney C, et al., 2014, The influence of late-gadolinium enhanced cardiac MRI defined scar on left atrial electrophysiological properties in patients with persistent atrial fibrillation, Annual Meeting of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 780-780, ISSN: 0195-668X
Sau A, Qureshi N, Bai W, et al., 2014, Late-gadolinium enhanced cardiac MRI defined scar is predominantly located on the left atrial septum and posterior wall in patients with persistent atrial fibrillation, Annual Meeting of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 799-799, ISSN: 0195-668X
Sau A, Mereu R, Taraborrelli P, et al., 2014, A long-term follow-up of patients with prolonged asystole > 15secs on head-up tilt testing, Annual Meeting of the European-Society-of-Cardiology (ESC), Publisher: OXFORD UNIV PRESS, Pages: 1066-1066, ISSN: 0195-668X
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Mereu R, Sau A, Lim PB, 2014, Diagnostic algorithm for syncope, AUTONOMIC NEUROSCIENCE-BASIC & CLINICAL, Vol: 184, Pages: 10-16, ISSN: 1566-0702
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- Citations: 5
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