Safety and Efficacy of Mini-invasive left atrial appendage closure : a propensity score analysis.

BACKGROUND: Left atrial appendage closure (LAAC) for stroke prevention is validated in patients with non-valvular atrial fibrillation (NVAF) contraindicated to oral anticoagulation. General anesthesia (GA) is often used for procedural guidance by trans-oesophageal echocardiography (TEE); however, its use may be challenging in some patients. The aim of the study was to evaluate the safety and the mid-term efficacy of a mini-invasive LAAC strategy using micro-TEE under procedural sedation. METHODS: Comparison by propensity score of two cohorts of consecutive patients who underwent LAAC: standard TEE-guided LAAC (3D-TEE under GA) and, mini-invasive LAAC strategy (micro-TEE under procedural sedation). The primary endpoint was a composite of embolic or bleeding events, significant per-procedural complication, and cardiovascular deaths within 3 months after LAAC. RESULTS: In total, 432 patients were included (78.7±8 years old, 32.4% of women, CHA2DS2VASC score:4.9±1.1); 127 patients underwent mini-invasive LAAC strategy and were compared to 305 patients standard TEE-guided LAAC. The mini-invasive strategy was acheived in 122/127 (96.1%) planned patients. The primary endpoint occurred in 11.2% of patients from the mini-invasive LAAC strategy group and in 10.3% of patients from the standard TEE group (absolute difference = 0.9%[-6.4; 4.5], hazard-ratio=1.11[0.56; 2.19], p=0.76). Procedural times, fluoroscopy duration and hospital stays were shorter in the mini-invasive LAAC strategy group (p<0.001). CONCLUSIONS: The mini-invasive LAAC strategy is safe and effective compared to the standard TEE-guided LAAC strategy. A mini-invasive LAAC strategy may also be an important tool to help physicians to treat more patients as LAAC indications evolve in the future.

Current Role of Electrocardiographic Imaging in Patient Selection for Cardiac Resynchronization Therapy.

Cardiac resynchronization therapy (CRT) is a recognized therapy for heart failure with altered ejection fraction and abnormal left ventricular activation time. Since the introduction of the therapy, a 30% rate of non-responders is observed and unchanged. The 12-lead ECG remains the only recommended tool for patient selection to CRT. The 12-lead ECG is, however, limited in its inability to provide a precise pattern of regional electrical activity. Electrocardiographic imaging (ECGi) provides a non-invasive detailed mapping of cardiac activation and therefore appears as a promising tool for CRT candidates. The non-invasive ventricular activation maps acquired by ECGi have been primarily explored for the diagnosis and guidance of therapy in patients with atrial or ventricular tachyarrhythmia. However, the accuracy of the system in this field is lacking and needs further improvement before considering a clinical application. On the other hand, its use for patient selection for CRT is encouraging. In this review, we introduce the technical considerations and we describe how ECGi can precisely characterize ventricular activation, especially in patients with left bundle branch block, thus identifying the electrical substrate responsive to CRT.

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.

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.

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.

Smartwatch-based detection of cardiac arrhythmias: Beyond the differentiation between sinus rhythm and atrial fibrillation.

Within the span of a few years, watches have functionally morphed from objects that tell time to wearable minicomputers that allow real-time recording of electrocardiograms (ECGs). Considerable information can be deduced from these single lead tracings, and it is now not uncommon to see patients in whom diagnostic tracings of clinically relevant but elusive arrhythmias are captured using a smartwatch. Empowering individuals to record their own ECG tracings in scenarios such as palpitations, syncope, and for risk stratification of sudden death intuitively has considerable potential, but its value remains to be robustly demonstrated. The main objective of this review is to describe the information that can be obtained from smartwatch-based single-lead ECG recordings beyond simply differentiating between sinus rhythm and atrial fibrillation. We also review the strengths and limitations of using these devices in clinical settings and offer potential solutions to address the latter.

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.

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.

Progressive implantable cardioverter-defibrillator therapies for ventricular tachycardia: The efficacy and safety of multiple bursts, ramps, and low-energy shocks.

BACKGROUND: The Heart Rhythm Society, the European Heart Rhythm Association, the Asia Pacific Heart Rhythm Society, the Latin American Heart Rhythm Society expert consensus statement on optimal implantable cardioverter-defibrillator programming recommends burst antitachycardia pacing (ATP) for the treatment of ventricular tachycardia (VT) up to high rates. The number of bursts is not specified, and treatment by ramps or low-energy shocks is not recommended. OBJECTIVES: We investigated the efficacy and safety of progressive therapies for VTs between 150 and 200 beats/min. After 3 failed bursts, we compared 3 ramps vs 3 bursts followed by a low-energy shock vs high-energy shock. METHODS: Using remote monitoring, we included monomorphic VT episodes treated with ≥1 burst. RESULTS: A total of 1126 VT episodes were included. A single burst was as likely to terminate VT between 150 and 200 beats/min as VT between 200 and 230 beats/min (63% vs 64%; P=.41), but was more likely to accelerate the latter (3.2% vs 0.25%; P<.01). For VT <200 beats/min, the likelihood of ATP success increased progressively (73% with 2 bursts, 78% with 3 bursts). Three additional bursts further increased VT termination to 89%, similar to the success rate with 3 additional ramps (88%; P=.17). Programming 6 bursts is associated with the probability of acceleration requiring shock of 6.6%. A low-energy first shock was less successful than a high-energy shock (66% vs 86%; P<.01) and more likely to accelerate VT (17% vs 0%; P<.01). CONCLUSION: Programming up to 6 burst ATP therapies for VTs 150-200 beats/min can avoid implantable cardioverter-defibrillator shocks in most patients. Ramp ATP after failed bursts were similarly effective. Low-energy shocks are less effective and more arrhythmogenic than high-energy shocks.