Machine learning-derived cycle length variability metrics predict spontaneously terminating ventricular tachycardia in implantable cardioverter defibrillator recipients.

Aims: 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. Methods and results: 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 < 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 < 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. Conclusion: Ventricular tachycardia CL variability and instability are associated with spontaneously terminating VT and can be used to predict spontaneous VT termination. Given the harmful effects of unnecessary ICD shocks, this machine learning model could be incorporated into ICD algorithms to defer therapies for episodes of VT that are likely to self-terminate.

Ventricular Conduction Stability Noninvasively Identifies an Arrhythmic Substrate in Survivors of Idiopathic Ventricular Fibrillation.

Background Idiopathic ventricular fibrillation (VF) is a diagnosis of exclusion following normal cardiac investigations. We sought to determine if exercise-induced changes in electrical substrate could distinguish patient groups with various ventricular arrhythmic pathophysiological conditions and identify patients susceptible to VF. Methods and Results Computed tomography and exercise testing in patients wearing a 252-electrode vest were combined to determine ventricular conduction stability between rest and peak exercise, as previously described. Using ventricular conduction stability, conduction heterogeneity in idiopathic VF survivors (n=14) was compared with those surviving VF during acute ischemia with preserved ventricular function following full revascularization (n=10), patients with benign ventricular ectopy (n=11), and patients with normal hearts, no arrhythmic history, and negative Ajmaline challenge during Brugada family screening (Brugada syndrome relatives; n=11). Activation patterns in normal subjects (Brugada syndrome relatives) are preserved following exercise, with mean ventricular conduction stability of 99.2±0.9%. Increased heterogeneity of activation occurred in the idiopathic VF survivors (ventricular conduction stability: 96.9±2.3%) compared with the other groups combined (versus 98.8±1.6%; P=0.001). All groups demonstrated periodic variation in activation heterogeneity (frequency, 0.3-1 Hz), but magnitude was greater in idiopathic VF survivors than Brugada syndrome relatives or patients with ventricular ectopy (7.6±4.1%, 2.9±2.9%, and 2.8±1.2%, respectively). The cause of this periodicity is unknown and was not replicable by introducing exercise-induced noise at comparable frequencies. Conclusions In normal subjects, ventricular activation patterns change little with exercise. In contrast, patients with susceptibility to VF experience activation heterogeneity following exercise that requires further investigation as a testable manifestation of underlying myocardial abnormalities otherwise silent during routine testing.

Artificial intelligence-enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia.

Background: Accurately 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. Methods: We 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. Results: The 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. Conclusion: We 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.

Feasibility of mapping and ablating ectopy-triggering ganglionated plexus reproducibly in persistent atrial fibrillation.

BACKGROUND: Ablation of autonomic ectopy-triggering ganglionated plexuses (ET-GP) has been used to treat paroxysmal atrial fibrillation (AF). It is not known if ET-GP localisation is reproducible between different stimulators or whether ET-GP can be mapped and ablated in persistent AF. We tested the reproducibility of the left atrial ET-GP location using different high-frequency high-output stimulators in AF. In addition, we tested the feasibility of identifying ET-GP locations in persistent atrial fibrillation. METHODS: Nine patients undergoing clinically-indicated paroxysmal AF ablation received pacing-synchronised high-frequency stimulation (HFS), delivered in SR during the left atrial refractory period, to compare ET-GP localisation between a custom-built current-controlled stimulator (Tau20) and a voltage-controlled stimulator (Grass S88, SIU5). Two patients with persistent AF underwent cardioversion, left atrial ET-GP mapping with the Tau20 and ablation (Precision™, Tacticath™ [n = 1] or Carto™, SmartTouch™ [n = 1]). Pulmonary vein isolation (PVI) was not performed. Efficacy of ablation at ET-GP sites alone without PVI was assessed at 1 year. RESULTS: The mean output to identify ET-GP was 34 mA (n = 5). Reproducibility of response to synchronised HFS was 100% (Tau20 vs Grass S88; [n = 16] [kappa = 1, SE = 0.00, 95% CI 1 to 1)][Tau20 v Tau20; [n = 13] [kappa = 1, SE = 0, 95% CI 1 to 1]). Two patients with persistent AF had 10 and 7 ET-GP sites identified requiring 6 and 3 min of radiofrequency ablation respectively to abolish ET-GP response. Both patients were free from AF for > 365 days without anti-arrhythmics. CONCLUSIONS: ET-GP sites are identified at the same location by different stimulators. ET-GP ablation alone was able to prevent AF recurrence in persistent AF, and further studies would be warranted.

Cycle Length Evaluation in Persistent Atrial Fibrillation Using Kernel Density Estimation to Identify Transient and Stable Rapid Atrial Activity.

PURPOSE: Left atrial (LA) rapid AF activity has been shown to co-localise with areas of successful atrial fibrillation termination by catheter ablation. We describe a technique that identifies rapid and regular activity. METHODS: Eight-second AF electrograms were recorded from LA regions during ablation for psAF. Local activation was annotated manually on bipolar signals and where these were of poor quality, we inspected unipolar signals. Dominant cycle length (DCL) was calculated from annotation pairs representing a single activation interval, using a probability density function (PDF) with kernel density estimation. Cumulative annotation duration compared to total segment length defined electrogram quality. DCL results were compared to dominant frequency (DF) and averaging. RESULTS: In total 507 8 s AF segments were analysed from 7 patients. Spearman's correlation coefficient was 0.758 between independent annotators (P < 0.001), 0.837-0.94 between 8 s and ≥ 4 s segments (P < 0.001), 0.541 between DCL and DF (P < 0.001), and 0.79 between DCL and averaging (P < 0.001). Poorer segment organization gave greater errors between DCL and DF. CONCLUSION: DCL identifies rapid atrial activity that may represent psAF drivers. This study uses DCL as a tool to evaluate the dynamic, patient specific properties of psAF by identifying rapid and regular activity. If automated, this technique could rapidly identify areas for ablation in psAF.

Ectopy-triggering ganglionated plexuses ablation to prevent atrial fibrillation: GANGLIA-AF study.

BACKGROUND: The ganglionated plexuses (GPs) of the intrinsic cardiac autonomic system may play a role in atrial fibrillation (AF). OBJECTIVE: We hypothesized that ablating the ectopy-triggering GPs (ET-GPs) prevents AF. METHODS: GANGLIA-AF (ClinicalTrials.gov identifier NCT02487654) was a prospective, randomized, controlled, 3-center trial. ET-GPs were mapped using high frequency stimulation, delivered within the atrial refractory period and ablated until nonfunctional. If triggered AF became incessant, atrioventricular dissociating GPs were ablated. We compared GP ablation (GPA) without pulmonary vein isolation (PVI) against PVI in patients with paroxysmal AF. Follow-up was for 12 months including 3-monthly 48-hour Holter monitors. The primary end point was documented ≥30 seconds of atrial arrhythmia after a 3-month blanking period. RESULTS: A total of 102 randomized patients were analyzed on a per-protocol basis after GPA (n = 52; 51%) or PVI (n = 50; 49%). Patients who underwent GPA had 89 ± 26 high frequency stimulation sites tested, identifying a median of 18.5% (interquartile range 16%-21%) of GPs. The radiofrequency ablation time was 22.9 ± 9.8 minutes in GPA and 38 ± 14.4 minutes in PVI (P < .0001). The freedom from ≥30 seconds of atrial arrhythmia at 12-month follow-up was 50% (26 of 52) with GPA vs 64% (32 of 50) with PVI (log-rank, P = .09). ET-GPA without atrioventricular dissociating GPA achieved 58% (22 of 38) freedom from the primary end point. There was a significantly higher reduction in antiarrhythmic drug usage postablation after GPA than after PVI (55.5% vs 36%; P = .05). Patients were referred for redo ablation procedures in 31% (16 of 52) after GPA and 24% (12 of 50) after PVI (P = .53). CONCLUSION: GPA did not prevent atrial arrhythmias more than PVI. However, less radiofrequency ablation was delivered to achieve a higher reduction in antiarrhythmic drug usage with GPA than with PVI.

Artificial intelligence-enabled electrocardiogram to distinguish cavotricuspid isthmus dependence from other atrial tachycardia mechanisms.

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.

A Multicenter External Validation of a Score Model to Predict Risk of Events in Patients With Brugada Syndrome.

A multivariate risk score model was proposed by Sieira et al in 2017 for sudden death in Brugada syndrome; their validation in 150 patients was highly encouraging, with a C-index of 0.81; however, this score is yet to be validated by an independent group. A total of 192 records of patients with Brugada syndrome were collected from 2 centers in the United Kingdom and retrospectively scored according to a score model by Sieira et al. Data were compiled summatively over follow-up to mimic regular risk re-evaluation as per current guidelines. Sudden cardiac death survivor data were considered perievent to ascertain the utility of the score before cardiac arrest. Scores were compared with actual outcomes. Sensitivity in our cohort was 22.7%, specificity was 57.6%, and C-index was 0.58. In conclusion, up to 75% of cardiac arrest survivors in this cohort would not have been offered a defibrillator if evaluated before their event. This casts doubt on the utility of the score model for primary prevention of sudden death. Inherent issues with modern risk scoring strategies decrease the likelihood of success even in robustly designed tools such as the Sieira score model.

Correction: Reusable snorkel masks adapted as particulate respirators.

[This corrects the article DOI: 10.1371/journal.pone.0249201.].

Postinfarct ventricular tachycardia substrate: Characterization and ablation of conduction channels using ripple mapping.

BACKGROUND: Conduction channels have been demonstrated within the postinfarct scar and seem to be co-located with the isthmus of ventricular tachycardia (VT). Mapping the local scar potentials (SPs) that define the conduction channels is often hindered by large far-field electrograms generated by healthy myocardium. OBJECTIVE: The purpose of this study was to map conduction channel using ripple mapping to categorize SPs temporally and anatomically. We tested the hypothesis that ablation of early SPs would eliminate the latest SPs without direct ablation. METHODS: Ripple maps of postinfarct scar were collected using the PentaRay (Biosense Webster) during normal rhythm. Maps were reviewed in reverse, and clusters of SPs were color-coded on the geometry, by timing, into early, intermediate, late, and terminal. Ablation was delivered sequentially from clusters of early SPs, checking for loss of terminal SPs as the endpoint. RESULTS: The protocol was performed in 11 patients. Mean mapping time was 65 ± 23 minutes, and a mean 3050 ± 1839 points was collected. SP timing ranged from 98.1 ± 60.5 ms to 214.8 ± 89.8 ms post QRS peak. Earliest SPs were present at the border, occupying 16.4% of scar, whereas latest SPs occupied 4.8% at the opposing border or core. Analysis took 15 ± 10 minutes to locate channels and identify ablation targets. It was possible to eliminate latest SPs in all patients without direct ablation (mean ablation time 16.3 ± 11.1 minutes). No VT recurrence was recorded (mean follow-up 10.1 ± 7.4 months). CONCLUSION: Conduction channels can be located using ripple mapping to analyze SPs. Ablation at channel entrances can eliminate the latest SPs and is associated with good medium-term results.

Reusable snorkel masks adapted as particulate respirators.

INTRODUCTION: During viral pandemics, filtering facepiece (FFP) masks together with eye protection form the essential components of personal protective equipment (PPE) for healthcare workers. There remain concerns regarding insufficient global supply and imperfect protection offered by currently available PPE strategies. A range of full-face snorkel masks were adapted to accept high grade medical respiratory filters using bespoke-designed 3D-printed connectors. We compared the protection offered by the snorkel to that of standard PPE using a placebo-controlled respirator filtering test as well as a fluorescent droplet deposition experiment. Out of the 56 subjects tested, 42 (75%) passed filtering testing with the snorkel mask compared to 31 (55%) with a FFP3 respirator mask (p = 0.003). Amongst the 43 subjects who were not excluded following a placebo control, 85% passed filtering testing with the snorkel versus to 68% with a FFP3 mask (p = 0.008). Following front and lateral spray of fluorescence liquid particles, the snorkel mask also provided superior protection against droplet deposition within the subject's face, when compared to a standard PPE combination of FFP3 masks and eye protection (3.19x108 versus 6.81x108 fluorescence units, p<0.001). The 3D printable adaptors are available for free download online at https://www.ImperialHackspace.com/COVID-19-Snorkel-Respirator-Project/. CONCLUSION: Full-face snorkel masks adapted as particulate respirators performed better than a standard PPE combination of FFP3 mask and eye protection against aerosol inhalation and droplet deposition. This adaptation is therefore a promising PPE solution for healthcare workers during highly contagious viral outbreaks.

The ectopy-triggering ganglionated plexuses in atrial fibrillation.

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.

Ventricular conduction stability test: a method to identify and quantify changes in whole heart activation patterns during physiological stress.

AIMS: Abnormal rate adaptation of the action potential is proarrhythmic but is difficult to measure with current electro-anatomical mapping techniques. We developed a method to rapidly quantify spatial discordance in whole heart activation in response to rate cycle length changes. We test the hypothesis that patients with underlying channelopathies or history of aborted sudden cardiac death (SCD) have a reduced capacity to maintain uniform activation following exercise. METHODS AND RESULTS: Electrocardiographical imaging (ECGI) reconstructs >1200 electrograms (EGMs) over the ventricles from a single beat, providing epicardial whole heart activation maps. Thirty-one individuals [11 SCD survivors; 10 Brugada syndrome (BrS) without SCD; and 10 controls] with structurally normal hearts underwent ECGI vest recordings following exercise treadmill. For each patient, we calculated the relative change in EGM local activation times (LATs) between a baseline and post-exertion phase using custom written software. A ventricular conduction stability (V-CoS) score calculated to indicate the percentage of ventricle that showed no significant change in relative LAT (<10 ms). A lower score reflected greater conduction heterogeneity. Mean variability (standard deviation) of V-CoS score over 10 consecutive beats was small (0.9 ± 0.5%), with good inter-operator reproducibility of V-CoS scores. Sudden cardiac death survivors, compared to BrS and controls, had the lowest V-CoS scores post-exertion (P = 0.011) but were no different at baseline (P = 0.50). CONCLUSION: We present a method to rapidly quantify changes in global activation which provides a measure of conduction heterogeneity and proof of concept by demonstrating SCD survivors have a reduced capacity to maintain uniform activation following exercise.

Prevalence of spontaneous type I ECG pattern, syncope, and other risk markers in sudden cardiac arrest survivors with Brugada syndrome.

INTRODUCTION: A spontaneous type I electrocardiogram (ECG) pattern and/or unheralded syncope are conventionally used as risk markers for primary prevention of sudden cardiac arrest/death (SCA/SCD) in Brugada syndrome (BrS). In this study, we determine the prevalence of conventional and newer markers of risk in those with and without previous aborted SCA events. METHODS: All patients with BrS were identified at our institute. History of symptoms was obtained from medical tests or from interviews. Other markers of risk were also obtained, such as presence of (1) spontaneous type I pattern, (2) fractionated QRS (fQRS), (3) early repolarization (ER) pattern, (4) late potentials on signal-averaged ECG (SAECG), and (5) response to programmed electrical stimulation. RESULTS: In 133 patients with Bars, 10 (7%) patients (mean age = 39 ± 11 years; nine males) were identified with a previous ventricular fibrillation/ventricular tachycardia episode (n = 8) or requiring cardio-pulmonary resuscitation (n = 2). None of these patients had a prior history of syncope before their SCA event. Only two (20%) patients reported a history of palpitations or dizziness. None had apneic breathing and three (30%) patients had a family history of SCA. From their ECGs, a spontaneous pattern was only found in one (10%) of these patients. Further, 10% of patients had fQRS, 17% had late potentials on SAECG, 20% had deep S waves in lead I, and 10% had an ER pattern in the peripheral leads. No significant differences were observed in the non-SCA group. CONCLUSION: The majority of BrS patients with previous aborted SCA events did not have a spontaneous type I and/or prior history of syncope. Conventional and newer markers of risk appear to only have limited ability to predict SCA.

Repolarization abnormalities unmasked with exercise in sudden cardiac death survivors with structurally normal hearts.

BACKGROUND: Models of cardiac arrhythmogenesis predict that nonuniformity in repolarization and/or depolarization promotes ventricular fibrillation and is modulated by autonomic tone, but this is difficult to evaluate in patients. We hypothesize that such spatial heterogeneities would be detected by noninvasive ECG imaging (ECGi) in sudden cardiac death (SCD) survivors with structurally normal hearts under physiological stress. METHODS: ECGi was applied to 11 SCD survivors, 10 low-risk Brugada syndrome patients (BrS), and 10 controls undergoing exercise treadmill testing. ECGi provides whole heart activation maps and >1,200 unipolar electrograms over the ventricular surface from which global dispersion of activation recovery interval (ARI) and regional delay in conduction were determined. These were used as surrogates for spatial heterogeneities in repolarization and depolarization. Surface ECG markers of dispersion (QT and Tpeak-end intervals) were also calculated for all patients for comparison. RESULTS: Following exertion, the SCD group demonstrated the largest increase in ARI dispersion compared to BrS and control groups (13 ± 8 ms vs. 4 ± 7 ms vs. 4 ± 5 ms; P = 0.009), with baseline dispersion being similar in all groups. In comparison, surface ECG markers of dispersion of repolarization were unable to discriminate between the groups at baseline or following exertion. Spatial heterogeneities in conduction were also present following exercise but were not significantly different between SCD survivors and the other groups. CONCLUSION: Increased dispersion of repolarization is apparent during physiological stress in SCD survivors and is detectable with ECGi but not with standard ECG parameters. The electrophysiological substrate revealed by ECGi could be the basis of alternative risk-stratification techniques.

Comparison of the Prognostic Usefulness of the European Society of Cardiology and American Heart Association/American College of Cardiology Foundation Risk Stratification Systems for Patients With Hypertrophic Cardiomyopathy.

Implantable cardiodefibrillators (ICDs) have proven benefit in preventing sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC), making risk stratification essential. Data on the predictive accuracy on the European Society of Cardiology (ESC) risk scoring system have been conflicting. We independently evaluated the ESC risk scoring system in our cohort of patients with HC from a large tertiary center and compared this with previous guidance by the American College of Cardiology Foundation and Heart Association (ACCF/AHA). Risk factor profiles, 5-year SCD risk estimates, and ICD recommendations, as defined by the ACCF/AHA and ESC guidelines, were retrospectively ascertained for 288 HC patients with and without SCD or equivalent events at our center. In the SCD group (n = 14), a significantly higher proportion of patients would not have met the criteria for an ICD implant using the ESC scoring algorithm compared with ACCF/AHA guidance (43% vs 7%, p = 0.029). In those without SCD events (n = 274), a larger proportion of individuals not requiring an ICD was identified using the ESC risk score model compared with the ACCF/AHA model (82% vs 57%; p < 0.0001). Based on risk stratification criteria alone, 5 more individuals with a previously aborted SCD event would not have received an ICD with the ESC risk model compared with the ACCF/AHA risk model. In conclusion, we found that the current ESC scoring system potentially leaves more high-risk patients unprotected from sudden death in our cohort of patients.

ST-Elevation Magnitude Correlates With Right Ventricular Outflow Tract Conduction Delay in Type I Brugada ECG.

BACKGROUND: The substrate location and underlying electrophysiological mechanisms that contribute to the characteristic ECG pattern of Brugada syndrome (BrS) are still debated. Using noninvasive electrocardiographical imaging, we studied whole heart conduction and repolarization patterns during ajmaline challenge in BrS individuals. METHODS AND RESULTS: A total of 13 participants (mean age, 44±12 years; 8 men), 11 concealed patients with type I BrS and 2 healthy controls, underwent an ajmaline infusion with electrocardiographical imaging and ECG recordings. Electrocardiographical imaging activation recovery intervals and activation timings across the right ventricle (RV) body, outflow tract (RVOT), and left ventricle were calculated and analyzed at baseline and when type I BrS pattern manifested after ajmaline infusion. Peak J-ST point elevation was calculated from the surface ECG and compared with the electrocardiographical imaging-derived parameters at the same time point. After ajmaline infusion, the RVOT had the greatest increase in conduction delay (5.4±2.8 versus 2.0±2.8 versus 1.1±1.6 ms; P=0.007) and activation recovery intervals prolongation (69±32 versus 39±29 versus 21±12 ms; P=0.0005) compared with RV or left ventricle. In controls, there was minimal change in J-ST point elevation, conduction delay, or activation recovery intervals at all sites with ajmaline. In patients with BrS, conduction delay in RVOT, but not RV or left ventricle, correlated to the degree of J-ST point elevation (Pearson R, 0.81; P<0.001). No correlation was found between J-ST point elevation and activation recovery intervals prolongation in the RVOT, RV, or left ventricle. CONCLUSIONS: Magnitude of ST (J point) elevation in the type I BrS pattern is attributed to degree of conduction delay in the RVOT and not prolongation in repolarization time.