Purkinje network and myocardial substrate at the onset of human ventricular fibrillation: implications for catheter ablation.

AIMS: Mapping data of human ventricular fibrillation (VF) are limited. We performed detailed mapping of the activities underlying the onset of VF and targeted ablation in patients with structural cardiac abnormalities. METHODS AND RESULTS: We evaluated 54 patients (50 ± 16 years) with VF in the setting of ischaemic (n = 15), hypertrophic (n = 8) or dilated cardiomyopathy (n = 12), or Brugada syndrome (n = 19). Ventricular fibrillation was mapped using body-surface mapping to identify driver (reentrant and focal) areas and invasive Purkinje mapping. Purkinje drivers were defined as Purkinje activities faster than the local ventricular rate. Structural substrate was delineated by electrogram criteria and by imaging. Catheter ablation was performed in 41 patients with recurrent VF. Sixty-one episodes of spontaneous (n = 10) or induced (n = 51) VF were mapped. Ventricular fibrillation was organized for the initial 5.0 ± 3.4 s, exhibiting large wavefronts with similar cycle lengths (CLs) across both ventricles (197 ± 23 vs. 196 ± 22 ms, P = 0.9). Most drivers (81%) originated from areas associated with the structural substrate. The Purkinje system was implicated as a trigger or driver in 43% of patients with cardiomyopathy. The transition to disorganized VF was associated with the acceleration of initial reentrant activities (CL shortening from 187 ± 17 to 175 ± 20 ms, P < 0.001), then spatial dissemination of drivers. Purkinje and substrate ablation resulted in the reduction of VF recurrences from a pre-procedural median of seven episodes [interquartile range (IQR) 4-16] to 0 episode (IQR 0-2) (P < 0.001) at 56 ± 30 months. CONCLUSIONS: The onset of human VF is sustained by activities originating from Purkinje and structural substrate, before spreading throughout the ventricles to establish disorganized VF. Targeted ablation results in effective reduction of VF burden. KEY QUESTION: The initial phase of human ventricular fibrillation (VF) is critical as it involves the primary activities leading to sustained VF and arrhythmic sudden death. The origin of such activities is unknown. KEY FINDING: Body-surface mapping shows that most drivers (≈80%) during the initial VF phase originate from electrophysiologically defined structural substrates. Repetitive Purkinje activities can be elicited by programmed stimulation and are implicated as drivers in 37% of cardiomyopathy patients. TAKE-HOME MESSAGE: The onset of human VF is mostly associated with activities from the Purkinje network and structural substrate, before spreading throughout the ventricles to establish sustained VF. Targeted ablation reduces or eliminates VF recurrence.

Using a smartwatch electrocardiogram to detect abnormalities associated with sudden cardiac arrest in young adults.

AIMS: Smartwatch electrocardiograms (ECGs) could facilitate the detection of sudden cardiac arrest (SCA)-associated abnormalities. We evaluated the feasibility of using smartwatch-derived ECGs for detecting SCA-associated abnormalities in young adults and its agreement with 12-lead ECGs. METHODS AND RESULTS: Twelve-lead and Apple Watch ECGs were registered in 155 healthy volunteers and 67 patients aged 18-45 years with diagnosis and ECG signs of long-QT syndrome (n = 10), Brugada syndrome (n = 12), ventricular pre-excitation (n = 19), hypertrophic cardiomyopathy (HCM, n = 13), and arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVC/D, n = 13). Cardiologists separately analysed 12-lead ECGs and the smartwatch ECGs taken from the left wrist (AW-I) and then from chest positions V1, V3, and V6 (AW-4). Compared with AW-I, AW-4 improved the classification of ECGs as 'abnormal', increasing the sensitivity from 64% to 89% (P < 0.01). Pre-excitation was detected in most cases using AW-I (sensitivity 89%) and in all cases using AW-4 (sensitivity 100%, P = 0.48 compared with AW-I, specificity 100% for both). Brugada was missed using AW-I but was detected in 11/12 patients using AW-4 (sensitivity 92%, specificity 100%, P = 0.003). Long QT was detected in 8/10 cases using AW-I (sensitivity 80%, specificity 100%) and in 9 patients using AW-4 (sensitivity 90%, specificity 100%, P > 0.99). Hypertrophic cardiomyopathy was correctly suspected using AW-I and AW-4 (sensitivity 92% and 85%, specificity 85%, and 100%, P > 0.99). AW-I was mostly (62%) considered normal in ARVC/D whereas AW-4 was useful in suspecting ARVC/D (100% sensitivity, 99% specificity, P = 0.004). CONCLUSIONS: Detection of SCA-associated ECG abnormalities (pre-excitation, Brugada patterns, long QT, and signs suggestive of HCM and ARVC/D) is possible with an ECG smartwatch.

Causes of impaired biventricular pacing in cardiac resynchronization devices with left ventricular sensing.

BACKGROUND: Loss of biventricular stimulation can result in nonresponse to cardiac resynchronization therapy (CRT). Problems associated with the left ventricular (LV) lead and LV sensing can be challenging to detect and their incidence is unclear. The purpose of this study was to investigate mechanisms of loss of biventricular pacing due to LV lead- and LV sensing-associated problems. METHODS: In this bicentric study, CRT patients were surveilled using a novel remote monitoring algorithm from Biotronik (Germany) that registers LV electrograms (EGMs) during intermittent loss of resynchronization. The episodes were analyzed to assess the mechanisms of resynchronization interruptions. RESULTS: We analyzed 582 EGMs from 61 patients. During a median follow-up of 6 months, 59% of the patients had such episodes. The majority of the episodes (61%) were related to inappropriate inhibition of LV pacing, mostly due to upper rate lock-in caused by LV sensing (58%). In contrast, 8% of episodes showed intermittent loss of LV capture, which was identified thanks to LV sensing. The remaining 31% of episodes were due to physiological reasons for resynchronization interruptions (eg, supraventricular tachycardia [18%], premature beats [8%], and others [5%]). Patients with CRT interruption episodes had lower resynchronization rates (median: 98.5% vs 100%, P = .044). CONCLUSIONS: Inadequate programming (active LV sensing with T-wave protection) is the main cause of impaired resynchronization in devices with LV sensing. In general, we recommend the deactivation of the LV T-wave protection function.

Left ventricular sensing in cardiac resynchronization devices-opportunities and pitfalls for device programming.

INTRODUCTION: Some cardiac resynchronization therapy (CRT) device manufacturers (Biotronik, Germany; Boston Scientific, United States) have implemented left ventricular (LV) sensing functionality to prevent pacing into the vulnerable phase. Physicians are only partially aware of programming pitfalls related to LV sensing and general programming advice is lacking. METHODS AND RESULTS: We provide an illustrative case-series-based review of the variety of potential problems with LV sensing. LV sensing may inappropriately impair CRT delivery due to LV-sensing issues or improper device programming. This can cause beat-wise loss of resynchronization but also ongoing desynchronization. On the other hand, LV sensing provides additional diagnostic information, which may reveal intermittent problems of the LV lead such as capture loss. We summarize the available evidence to provide manufacturer-specific recommendations on device programming and troubleshooting for daily clinical practice. CONCLUSION: CRT devices with LV sensing may suffer from impaired resynchronization due to programming pitfalls. If LV sensing is active (nominal setting in Biotronik and Boston Scientific devices), careful lookout for related problems and resynchronization percentage is required. Optimization is mandatory and even deactivation of LV sensing may have to be considered.

Local VA index for the differential diagnosis of supraventricular tachycardia.

BACKGROUND: Differentiating between atypical atrioventricular nodal reentrant tachycardia (AVNRT) and orthodromic reciprocating tachycardia utilizing a septal accessory pathway is a complex challenge. OBJECTIVE: The purpose of this study was to describe the "local VA index," a straightforward method based on signals from the coronary sinus catheter, to distinguish between these arrhythmias during tachycardia and entrainment. The ventriculoatrial (VA) interval on the coronary sinus catheter is measured during tachycardia and entrainment, at the site of earliest atrial activity. The difference between these 2 situations defines the "local VA index." We also propose a mechanism to clarify the limitations of historical pacing maneuvers, such as postpacing interval minus tachycardia cycle length (PPI-TCL) and stimulus-atrial interval minus ventriculoatrial interval (SA-VA), by examining nodal decrement and intraventricular conduction delay. METHODS: In a retrospective study of 75 patients referred for supraventricular tachycardia evaluation, 37 were diagnosed with atrioventricular reentrant tachycardia (AVRT) with orthodromic reciprocating tachycardia, and 38 with AVNRT (27 typical, 11 atypical). RESULTS: In comparison to AVRT patients, AVNRT patients exhibited longer PPI-TCL (176 ± 47 ms vs 113 ± 42 ms; P <.01) and SA-VA (138 ± 47 ms vs 64 ± 28 ms; P <.01). The AVRT group had mean local VA index of -1 ± 13 ms, whereas the AVNRT group had a significantly longer index of 91 ± 46 ms (P <.01). An optimal threshold for differentiation was a local VA index of 40 ms. Importantly, there was no significant correlation between pacing cycle length and nodal decrement as well as intraventricular delay related to pathway location. This interindividual variability might explain misleading interpretations of PPI-TCL and SA-VA. CONCLUSION: This novel approach is advantageous because of its simplicity and effectiveness, requiring only 2 diagnostic catheters. A local VA interval difference <40 ms provides a clear distinction for AVRT.

Nursing Students Reported More Positive Emotions about Training during COVID-19 After Using a Virtual Simulation Paired with an In-person Simulation.

Background: Virtual simulations (VS) are educational tools that can help overcome the limitations of in-person learning highlighted during the COVID-19 pandemic. Research has illustrated that VS can support learning, but little is known about the usability of VS as a distance learning tool. Research on students' emotions about VS is also scarce, despite the influence of emotions on learning. Methods: A quantitative longitudinal study was conducted with undergraduate nursing students. 18 students participated in a hybrid learning experience involving a virtual simulation (VS) followed by an in-person simulation. Students completed questionnaires about their emotions, perceived success, and usability and received a performance score from the VS. Results: Nursing students reported statistically significant improvements in their emotions about completing their program after completing both VS and in-person simulations compared to their emotions before the pair of simulations. Emotions directed toward the VS were weak-to-moderate in strength, but predominantly positive. Positive emotions were positively associated with nursing students' performance. Findings replicated "okay" approaching "good" usability ratings from a recent study with key methodological differences that used the same software. Conclusions: VS can be an emotionally positive, effective, efficient, and satisfying distance learning supplement to traditional simulations.

Gaps after linear ablation of persistent atrial fibrillation (Marshall-PLAN): Clinical implication.

BACKGROUND: Beyond pulmonary vein (PV) isolation, anatomic isthmus transection is an adjunctive strategy for persistent atrial fibrillation. Data on the durability of multiple lines of block remain scarce. OBJECTIVE: The purpose of this study was to evaluate the impact of gaps within such a lesion set. METHODS: We followed 291 consecutive patients who underwent (1) vein of Marshall ethanol infusion, (2) PV isolation, and (3) mitral, cavotricuspid, and dome isthmus transection. Dome transection relied on 2 distinct strategies over time: a single roof line with touch-ups applied in case of gap demonstrated by conventional maneuvers (first leg), and an alternative floor line if the roof line exhibited a gap during high-density mapping with careful electrogram reannotation (second leg). RESULTS: Twelve-month sinus rhythm maintenance was 70% after 1 procedure and 94% after 1 or 2 procedures. Event-free survival after the first procedure was lower in case of residual gaps within the lesion set (log-rank, P = .004). Delayed gaps were found in 94% of a second procedure performed in the 69 patients relapsing despite a complete lesion set with PV gaps increasing the risk of recurrence of atrial fibrillation (67% vs 34%; P = .02) and anatomic isthmus gaps supporting a majority of atrial tachycardias (60%). Between the first leg and the second leg, a significant decrease was found in roof lines considered blocked during the first procedure (99% vs 78%; P < .001) and in delayed dome gaps observed during a second procedure (68% vs 43%; P = .05). CONCLUSION: Gaps are arrhythmogenic and can be reduced by optimized ablation and assessment of lines of block. Closing these gaps improves sinus rhythm maintenance.

Smartwatch Electrocardiograms for Automated and Manual Diagnosis of Atrial Fibrillation: A Comparative Analysis of Three Models.

AIMS: The diagnostic accuracy of proprietary smartwatch algorithms and the interpretability of smartwatch ECG tracings may differ between available models. We compared the diagnostic potential for detecting atrial fibrillation (AF) of three commercially available smartwatches. METHODS: We performed a prospective, non-randomized, and adjudicator-blinded clinical study of 100 patients in AF and 100 patients in sinus rhythm, patients with atrial flutter were excluded. All patients underwent 4 ECG recordings: a conventional 12-lead ECG, Apple Watch Series 5®, Samsung Galaxy Watch Active 3®, and Withings Move ECG® in random order. All smartwatch ECGs were analyzed using their respective automated proprietary software and by clinical experts who also graded the quality of the tracings. RESULTS: The accuracy of automated AF diagnoses by Apple and Samsung outperformed that of Withings, which was attributable to a higher proportion of inconclusive ECGs with the latter (sensitivity/specificity: 87%/86% and 88%/81% vs. 78%/80%, respectively, p < 0.05). Expert interpretation was more accurate for Withings and Apple than for Samsung (sensitivity/specificity: 96%/86% and 94%/84% vs. 86%/76%, p < 0.05), driven by the high proportion of uninterpretable tracings with the latter (2 and 4% vs. 15%, p < 0.05). CONCLUSION: Diagnosing AF is possible using various smartwatch models. However, the diagnostic accuracy of their automated interpretations varies between models as does the quality of ECG tracings recorded for manual interpretation.

Beyond the wrist: Using a smartwatch electrocardiogram to detect electrocardiographic abnormalities.

BACKGROUND: When worn on the wrist, smartwatch electrocardiograms may provide important but incomplete information. AIMS: We sought to evaluate the added benefit of placing the smartwatch on the ankle and on the chest to diagnose various electrocardiographic abnormalities compared with 12-lead electrocardiograms. METHODS: Two hundred and sixty patients with (n=189) or without (n=71) known cardiac disorders underwent 12-lead electrocardiogram and smartwatch electrocardiogram recordings of lead I (AW-I) and of leads I and II and pseudo chest leads V1 and V6 (AW-4). AW-I and AW-4 diagnoses (three-cardiologist consensus) were compared with 12-lead electrocardiogram diagnoses (three-cardiologist consensus) to calculate sensitivity and specificity. RESULTS: AW-I showed high accuracy for the diagnoses of atrial fibrillation (96% sensitivity, 91% specificity) and complete bundle branch block (85% sensitivity, 98% specificity). Compared with AW-I, AW-4 improved detection of an abnormal 12-lead electrocardiogram (91% vs. 80% sensitivity; P<0.01), atrial flutter/tachycardia (69% vs. 25% sensitivity; P=0.04), T-wave abnormalities (77% vs. 34% sensitivity; P<0.01), pathological Q-waves (41% vs. 7% sensitivity; P<0.01) and left anterior hemiblock (70% vs. 0% sensitivity; P=0.02). AW-4 also enabled better differentiation between atrioventricular block and sinus bradycardia (from 81% to 95% correct; P=0.03) and between atrial fibrillation and atrial flutter/tachycardia (from 71% to 89% correct; P=0.02), but not between bundle branch blocks (from 82% to 87% correct; P=0.57). CONCLUSIONS: A smartwatch electrocardiogram on the wrist accurately diagnoses atrial fibrillation and bundle branch block. Recording additional leads significantly improves the accuracy of detecting an abnormal electrocardiogram and repolarization changes, and also allows for better differentiation of brady- and tachyarrhythmias.

Sinus node exit, crista terminalis conduction, interatrial connection, and wavefront collision: Key features of human atrial activation in sinus rhythm.

BACKGROUND: An understanding of normal atrial activation during sinus rhythm can inform catheter ablation strategies to avoid deleterious impacts of ablation lesions on atrial conduction and mechanics. OBJECTIVE: The purpose of this study was to describe how the sinus node impulse originates, propagates, and collides in right and left atria with normal voltage. METHODS: Fifty consecutive patients undergoing catheter ablation of atrial fibrillation with endocardial atrial voltage >0.5 mV during high-density 3-dimensional mapping were studied. RESULTS: Sinus node exits varied among patients along a lateral oblique arc extending from the anterior aspect of the superior vena cava (SVC) to the mid-posterior wall of the right atrium (RA). Conduction slowing or block at one of the smooth components that faces the crista terminalis was observed in 54% of cases, including complete block at the SVC musculature and systemic venous sinus in 6% of cases. Depending on these 2 key features of RA activation, interatrial conduction was mediated by the Bachmann bundle (64%) and posterior bundles (54%), with an overlap of the resulting left atrial breakthrough location. Wavefront collision was consistently observed at 3 sites: the septal aspect of the cavotricuspid isthmus, and the lower aspects of the dome and of the mitral isthmus. CONCLUSION: During sinus rhythm, atrial activation occurs via distinct sequences mediated by a complex interaction of anatomic factors.

Remote monitoring of patients with heart failure during the first national lockdown for COVID-19 in France.

Aims: Multiparametric remote monitoring of patients with heart failure (HF) has the potential to mitigate the health risks of lockdowns for COVID-19. We aimed to compare healthcare use, physiological variables, and HF decompensations during 1 month before and during the first month of the first French national lockdown for COVID-19 among patients undergoing remote monitoring. Methods and results: Transmitted vital parameters and data from cardiac implantable electronic devices were analysed in 51 patients. Medical contact was defined as the sum of visits and days of hospitalization. The lockdown was associated with a marked decrease in cardiology medical contact (118 days before vs. 26 days during, -77%, P = 0.003) and overall medical contact (180 days before vs. 79 days during, -58%, P = 0.005). Patient adherence with remote monitoring was 84 ± 21% before and 87 ± 19% during lockdown. The lockdown was not associated with significant changes in various parameters, including physical activity (2 ± 1 to 2 ± 1 h/day), weight (83 ± 16 to 83 ± 16 kg), systolic blood pressure (121 ± 19 to 121 ± 18 mmHg), heart rate (68 ± 10 to 67 ± 10 b.p.m.), heart rate variability (89 ± 44 to 78 ± 46 ms, P = 0.05), atrial fibrillation burden (84 ± 146 vs. 86 ± 146 h/month), or thoracic impedance (66 ± 8 to 66 ± 9 Ω). Seven cases of HF decompensations were observed before lockdown, all but one of which required hospitalization, vs. six during lockdown, all but one of which were managed remotely. Conclusions: The lockdown restrictions caused a marked decrease in healthcare use but no significant change in the clinical status of HF patients under multiparametric remote monitoring.

Left-axis deviation in patients with nonischemic heart failure and left bundle branch block is a purely electrical phenomenon.

BACKGROUND: Possible mechanisms of left-axis deviation (LAD) in the setting of left bundle branch block (LBBB) include differences in cardiac electrophysiology, structure, or anatomic axis. OBJECTIVE: The purpose of this study was to clarify the mechanism(s) responsible for LAD in patients with LBBB. METHODS: Twenty-nine patients with nonischemic cardiomyopathies and LBBB underwent noninvasive electrocardiographic imaging (ECGi), cardiac computed tomography, and magnetic resonance imaging in order to define ventricular electrical activation, characterize cardiac structure, and determine the cardiac anatomic axis. RESULTS: Sixteen patients had a normal QRS axis (NA) (mean axis 8° ± 23°), whereas 13 patients had LAD (mean axis -48° ± 13°; P <.001). Total activation times were longer in the LAD group (112 ± 25 ms vs 91 ± 14 ms; P = .01) due to delayed activation of the basal anterolateral region (107 ± 10 ms vs 81 ± 17 ms; P <.001). Left ventricular (LV) activation in patients with LAD was from apex to base, in contrast to a circumferential pattern of activation in patients with NA. Apex-to-base delay was longer in the LA group (95 ± 13 ms vs 64 ± 21 ms; P <.001) and correlated with QRS frontal axis (R2 = 0.67; P <.001). Both groups were comparable with regard to LV end-diastolic volume (295 ± 84 mL vs LAD 310 ± 91 mL; P = .69), LV mass (177 ± 33 g vs LAD 180 ± 37 g; P = .83), and anatomic axis. CONCLUSION: LAD in LBBB appears to be due to electrophysiological abnormalities rather than structural factors or cardiac anatomic axis.

Oversensing issues leading to device extraction: When subcutaneous implantable cardioverter-defibrillator reached a dead-end.

BACKGROUND: Subcutaneous implantable cardioverter-defibrillator (S-ICD) implantations are rapidly expanding. However, the subcutaneous detection and interpretation of cardiac signals in S-ICDs is much more challenging than by conventional devices. There is a complete paradigm shift in cardiac signal sensing with subcutaneous signal detection, leading in some cases to oversensing with restricted programming options. OBJECTIVES: The aim of this single-center study was to quantify and describe cases where recurring oversensing made the extraction of the device necessary. METHODS: Consecutive patients (n = 108) implanted with an S-ICD in our tertiary referral hospital were considered for analysis. Clinical and remote monitoring data were analyzed. RESULTS: The S-ICD had to be explanted in 6 of 108 implanted patients (5.6%) because of refractory oversensing issues: myopotential oversensing, P- or T-wave oversensing, rate-dependent left bundle branch block aberrancy during exercise with R-wave double counting, and R-wave amplitude decrease after ventricular tachycardia ablation leading to noise detection. Seventeen of 108 patients experienced oversensing (15.7%): 9 patients had at least 1 inappropriate charge without a shock (8.3%), 3 patients had at least 1 inappropriate shock (2.8%), and 5 patients had both episodes (4.6%). CONCLUSION: So far, cardiologists have had to deal with transvenous ICD lead fractures, but signal oversensing without correcting programming option could be the equivalent weakness of S-ICDs, despite an adequate screening.