Surface Electrocardiogram Research Articles

Relationship between corrected QT prolongation and new-onset atrial fibrillation in the general Japanese population

Abstract Background The QT interval, an electrocardiogram (ECG) parameter, can be corrected for heart rate, giving the QTc. The QTc is an indicator of ventricular repolarisation and is widely used as a predictor of ventricular arrhythmia. Recent studies have reported a relationship between prolonged QTc and atrial fibrillation (AF) development. Purpose We investigated whether a relationship between QTc prolongation and new-onset AF could be identified in the general Japanese population. Methods This retrospective study evaluated the annual health check-up data of 103,304 adults (50,438 males; age, 54±15 years) that did not have AF at baseline, from April 2005 to October 2018. Most participants underwent annual health examinations as recommended by the Japanese health welfare policy. The QTc times were calculated with the Bazett formula (QTc=QT/√RR), using the mean QT and RR intervals. Participants were grouped according to QTc time: long-QTc group (males, >440 ms; females, >460 ms) and normal-QTc group. AF was diagnosed using a 12-lead surface ECG. Logistic regression analyses were performed to determine the strength of the association between prolonged QTc and new-onset AF. Multivariate analyses were adjusted for clinical variables (age, sex, obesity, hypertension, dyslipidaemia, diabetes, estimated glomerular filtration rate, and habitual drinking). Results The median follow-up time for the total study population was 6 years. During follow-up, 341 (0.3%) new cases of AF were recorded. Univariate analysis revealed a significant increase in new-onset AF in the long-QTc group (odds ratio [OR], 2.80; 95% confidence interval [CI], 1.85–4.25; p<0.001). After adjusting for clinical variables, multivariate analysis revealed that a long QTc was significantly associated with new-onset AF (OR, 1.71; 95% CI, 1.12–2.61; p=0.013). In the multivariate analysis, age (OR, 1.06; 95% CI, 1.05–1.07; p<0.001), sex (OR, 2.55; 95% CI, 1.95–3.33; p<0.001), obesity (OR, 1.46; 95% CI, 1.16–1.84; p=0.001), and habitual drinking (OR, 1.90; 95% CI, 1.50–2.41; p<0.001) were also significant predictors of new-onset AF. Conclusions Prolonged QTc is associated with an increased risk of new-onset AF in the general Japanese population. The mechanism of this causal relationship requires further investigation.

Abstract 14862: Electrophysiological Predictors of Susceptible Atrial Substrate for the Onset and Recurrence of Atrial Fibrillation

Introduction: Atrial electro-anatomical substrate is essential for pathogenesis of atrial fibrillation (AF). Electrophysiological markers of AF susceptibility, however, are not well characterized. Identifying markers of atrial substrate has significant clinical implications in early detection and intervention for AF or stratifying patients at higher risk of AF recurrence following ablation. Hypothesis: We hypothesized that conduction indices on surface electrocardiogram (ECG) and intracardiac electrogram (EGM) may predict susceptibility and/or AF recurrence following ablation. Methods: We studied all consecutive patients who underwent electrophysiological study and catheter ablation for AF or atrioventricular nodal reentrant tachycardia (AVNRT) from 2013 to 2018. Atrial conduction indices were measured on ECG and EGM during the electrophysiology study. We evaluated correlations between both AF and AF recurrence with conduction indices, and associations between AF recurrence and conduction indices were evaluated with univariate logistic regression analyses and machine learning. Results: The study population included 387 patients with AF (mean age 63 years, 31% female) and compared to patients with AVNRT (n=94) who were considered as a control group. AF was associated with significant prolongation of all measured surface ECG and EGM parameters (p<0.05). During a mean follow up of 5±2 years, recurrence of AF (48%) after successful ablation occurred in patient with longer P wave duration, left atrial (LA) conduction duration, proximal to distal coronary sinus (pCS-dCS) interval, and P-dCS interval (p<0.05). Prolonged P wave duration, LA duration, pCS-dCS interval, and QRS duration were predictors of AF recurrence. Machine learning modeling determined that pCS-dCS interval, QRS duration, and LA duration (lead V1 and II) were the most impactful conduction indices for AF recurrence. Conclusions: Atrial conduction indices are markers of susceptible substrate for the onset and the recurrence of AF after successful ablation. LA conduction parameters can be used for risk stratification to facilitate early detection and management of AF.

Abstract 15352: Deep Learning Model-Enabled Electrocardiogram to Localize Premature Ventricular Contractions in Patients Referred for Catheter Ablation

Introduction: Accurate pre-procedure localization of the premature ventricular contractions (PVCs) is critical in preparing for the ablation. So far, no tools exist for artificial intelligence guided PVC-localization. Hypothesis: Trained deep learning model will be able to predict the site of origin (SOO) of PVCs using 12 lead electrocardiogram (ECG). Methods: We analyzed 707 ECG-ablation pairs from 2403 patients who had at least one ECG flagged for PVCs in the 6 months prior to their procedure. First, we used a cohort of 100,000 ECGs without PVCs and 10,000 ECGs with PVCs to train a convolutional neural network (CNN) to isolate the ECG segments with PVCs in them from a 12-lead ECG (Figure 1). We then trained two models, one utilizing a 10-second 12-lead ECG, and the other with just the isolated PVC beats as input. The models were trained using the Keras framework with TensorFlow (Google). Results: The mean age of the patients in the cohort was 61.2 ± 15.0 years. 66.3 percent of patients underwent left ventricle ablation, 54.6 percent had outflow ablation, and 55.3 percent had ablation of sites with leftward axis (septal location for midline and left ventricle PVCs). The model trained on 10-second ECGs yielded validation and test AUCs of 0.941 and 0.931 for outflow, 0.898 and 0.891 for ventricle, and 0.863 and 0.846 for leftward axis (Figure 1A). The model trained on isolated segments containing PVC beats yielded validation and test AUCs of 0.966 and 0.957 for outflow, 0.879 and 0.907 for ventricle, and 0.846 and 0.829 for SOO for leftward axis (Figure 1B). Conclusions: We have trained an accurate convolutional neural network that can predict SOO using the surface ECG. Further fine-tuning would be needed to refine the model and develop prediction of specific sites.

Abstract 17893: Deep Learning for the Prediction of Myocardial Scarring Through Electrocardiograms

Introduction: Cardiac Magnetic Resonance Imaging (MRI) with Late Gadolinium enhancement (LGE), the gold standard for myocardial scarring evaluation, is limited by cost, time, and expertise required. The Electrocardiogram (ECG), in contrast, is a widely available and affordable diagnostic tool. We applied deep learning (DL) models to ECGs to directly predict the presence of LGE on MRI. Methods: Patients who underwent a cardiac MRI and had at least one ECG collected from 2012 to 2022 were considered. Patients were classified into positive and negative for myocardial scarring via their MRI reports, labeled by experts. Patients with non-specific scarring were excluded from the dataset. Patient ECGs most recent to their MRI were utilized as input for the model. MRI reports and ECG waveforms were collected from NYU Langone Health. We implemented a 34-layer Neural Network architecture, adapted from a state-of-the-art arrhythmia detection model, to learn from a 10 second ECG sampled at 250hz. We evaluated the model using area under the receiver-operating curve (AUROC), specificity, sensitivity, and precision. Results: 8813 patients with MRI and ECG were included. 1559 were excluded due to non-specific scarring. 2739 patients were positive for scarring. Patients were split into a 70% train, 10% validation, and 20% test dataset. The deep learning model obtained a test set AUROC of 0.80 [95%CI, 0.77 - 0.81]. At 20% false positive rate, we obtained a sensitivity of 0.63, specificity of 0.80, and precision of 0.64. Conclusions: This study suggests that our DL model can predict for the presence of LGE from a surface ECG. A future direction of this work would be to evaluate the use of DL for the prediction of LGE location, quantity and specific phenotypes of myocardial disease.

Abstract 16400: Development of Deep Neural Network Models for Identification of Pulmonary Vein Potentials From Body Surface Electrocardiograms of Patients With Atrial Fibrillation

[Background] Pulmonary vein (PV) potential is essential as the origin of atrial fibrillation (AF). However, no established method exists to evaluate PV potentials on a body surface ECG (BS-ECG). [Objective] To develop a method to identify PV potentials from BS-ECGs using state-of-the-art deep neural network (DNN) technology. [Methods and results] A cohort of 799 consecutive paroxysmal AF patients, who underwent Pulmonary Vein Isolation (PVI), were enrolled, and pre- and post-PVI 12-lead ECGs (12ECGs) were collected, amounting to a dataset of 1598 ECGs. Four DNN models were constructed, each employing distinct architectures: 1D-ResNet, 1D-DenseNet, 1D-EfficientNet-b3, and 1D-ViT (Vision Transformer). These models were rigorously trained and evaluated for the identification of PV potentials. The 1D-EfficientNet-b3-based model exhibited optimal performance, achieving an accuracy of 89.4% in the training set and 89.1% in the test set. The Gradient-weighted Class Activation Mapping (Grad-CAM) effectively demonstrated that the segments 200-300ms before the P-wave is crucial for identifying PV potentials. Furthermore, DNN models were developed for each single-lead ECG. While there were variations in the accuracy of PV potential identification among single-lead models, the V6-lead model demonstrated superior performance with an accuracy of 83.2%. [Conclusions] The findings of this research strongly suggest that our DNN-based model can accurately differentiate PV potentials from BS-ECGs. Additionally, Grad-CAM visualizations indicate that pertinent features for the identification of PV potentials are predominantly clustered in the P-wave frontal region. This signifies a promising advancement in the non-invasive detection and analysis of PV potentials, paving the way for improved management of paroxysmal AF.

Abstract 13785: QT Prolongation and QT Variability Predict New-Onset Atrial Fibrillation in the General Japanese Population

Background: The QT interval, an electrocardiogram (ECG) parameter, can be corrected for heart rate to obtain the QTc, an indicator of ventricular repolarization widely used as a predictor of ventricular arrhythmia. Although prolonged QTc and atrial fibrillation (AF) development are linked, there are few reports of QTc variability and AF development. Purpose: To identify a relationship between QTc prolongation and variability and new-onset AF in the general Japanese population. Methods: This retrospective study evaluated the annual health check-up data of 103,304 adults (50,438 males; age, 54±15 years) without AF at baseline, between April 2005 and October 2018. Most participants underwent annual health examinations as recommended by the Japanese health welfare policy. The QTc times were calculated with the Bazett formula (QTc=QT/√RR), using the mean QT and RR intervals. QTc variability was evaluated as the difference between the maximum and minimum QTc values at multiple checkups. AF was diagnosed using a 12-lead surface ECG. The strength of the association between QTc prolongation and variability and new-onset AF was determined using logistic regression analyses. Multivariate analyses were adjusted for clinical variables (age, sex, obesity, hypertension, dyslipidemia, diabetes, estimated glomerular filtration rate, and habitual drinking). Results: The median follow-up time was 6 years. During follow-up, 341 (0.3%) new AF cases were recorded. Univariate analysis revealed a significant increase in new-onset AF in the QTc prolongation (odds ratio [OR] per 10 ms, 1.08; 95% confidence interval [CI], 1.03-1.13; p<0.001) and the QTc variability (OR per 10 ms, 1.15; 95% CI, 1.07-1.23; p<0.001). After adjusting for clinical variables, multivariate analysis revealed that QTc prolongation and variability were significantly associated with new-onset AF (OR per 10 ms, 1.09; 95% CI, 1.04-1.14; p<0.001) (OR per 10 ms, 1.16; 95% CI, 1.09-1.24; p<0.001). Conclusions: QTc prolongation and variability are associated with an increased risk of new-onset AF in the general Japanese population. Although they have been reported to be involved with the autonomic nervous system, the mechanism of this causal relationship requires further investigation.

A novel feature extraction method for the localization of accessory pathways in patients with Wolff-Parkinson-White syndrome

Background and objectiveThe localization of accessory pathways (APs) in patients with Wolff-Parkinson-White (WPW) syndrome using surface electrocardiogram (ECG) is often entirely subjective. This study aimed to develop a novel, more automated, and non-invasive localization method to differentiate right and left APs. Materials and methodsThe participants were 31 patients (aged 8–69 y, mean age: 31.19 ± 14.69 y, 32.3 % women) with manifest WPW syndrome who were treated successfully in the first ablation session. The novel localization of APs was based on feature extraction using McSharry’s model, and SFS-LOV, a combination of three methods (sequential forward selection (SFS), leave-one-subject-out cross-validation (LOSO), and majority voting). The k-nearest neighbor (KNN) and support vector machine (SVM) classifier were separately used in the SFS-LOV method. In this method, the features were extracted from three segments of the ECG signals. Each segment was started from the onset of the P-wave and terminated with the offset of the T-wave. The majority voting was applied to the results of the classifier on the segments of each participant. ResultsThe proposed method differentiated the right from the left APs with an accuracy of 87% (sensitivity: 80%, specificity: 94%). This was achieved by feeding four selected parameters of McSharry’s model into the KNN classifier. These selected parameters were estimated from the lead V4 of the ECG signals. ConclusionThe parameters of McSharry’s model can semi-automatically localize the right and left APs in patients with manifest WPW syndrome.

Brugada Syndrome: A Comprehensive Review of Fundamental and Electrophysiological New Findings.

Brugada syndrome is characterized by pronounced J-ST segment elevation in the right precordial leads on surface electrocardiograms. The etiological underpinnings of these distinctive features have been the subject of extensive debate, encompassing various theories related to repolarization anomalies and conduction irregularities. Genetic investigations have unveiled SCN5A, the gene encoding NaV1.5, a critical sodium channel, as the most frequently implicated causative gene, with mutations typically manifesting as loss of function. Nonetheless, the detection rate of SCN5A mutations remains below 20%, underscoring the intricate genetic landscape of the syndrome. Histological analyses have divulged localized structural irregularities, primarily marked by fibrotic alterations, within the right ventricular outflow tract. Electrophysiological inquiries employing direct epicardial mapping techniques have uncovered localized conduction impediments concomitant with modifications in unipolar morphologies within the J-ST segment. Thus, the theory positing conduction abnormalities emerges as a compelling mechanism accounting for J-ST segment elevation. However, the precise mechanisms governing the onset of life-threatening tachyarrhythmias remain shrouded in uncertainty. Recent clinical case reports have proffered evidence supporting the notion that phase 2 reentry, arising from the marked heterogeneity in action potentials within the epicardial domain, may serve as the instigator of premature ventricular contractions, ultimately culminating in ventricular fibrillation. In light of these developments, it becomes increasingly evident that comprehending the mechanisms underlying the electrocardiographic manifestations and lethal arrhythmias in Brugada syndrome necessitates the consideration of a multifaceted perspective, transcending the binary discourse of repolarization versus depolarization anomalies.

Smartphone-based atrial fibrillation screening in the general population: feasibility and impact on medical treatment.

The aim of this study is to determine the feasibility, detection rate, and therapeutic implications of large-scale smartphone-based screening for atrial fibrillation (AF). Subjects from the general population in Belgium were recruited through a media campaign to perform AF screening during 8 consecutive days with a smartphone application. The application analyses photoplethysmography traces with artificial intelligence and offline validation of suspected signals to detect AF. The impact of AF screening on medical therapy was measured through questionnaires. Atrial fibrillation was detected in the screened population (n = 60.629) in 791 subjects (1.3%). From this group, 55% responded to the questionnaire. Clinical AF [AF confirmed on a surface electrocardiogram (ECG)] was newly diagnosed in 60 individuals and triggered the initiation of anti-thrombotic therapy in 45%, adjustment of rate or rhythm controlling strategies in 62%, and risk factor management in 17%. In subjects diagnosed with known AF before screening, a positive screening result led to these therapy adjustments in 9%, 39%, and 11%, respectively. In all subjects with clinical AF and an indication for oral anti-coagulation (OAC), OAC uptake increased from 56% to 74% with AF screening. Subjects with clinical AF were older with more co-morbidities compared with subclinical AF (no surface ECG confirmation of AF) (P < 0.001). In subjects with subclinical AF (n = 202), therapy adjustments were performed in only 7%. Smartphone-based AF screening is feasible at large scale. Screening increased OAC uptake and impacted therapy of both new and previously diagnosed clinical AF but failed to impact risk factor management in subjects with subclinical AF.

Effect of body mass index (BMI) on electrocardiographic P-wave dispersion among healthy adults

Background: According to the results of an electrocardiogram (ECG), a higher body mass index (BMI) indicates body fat and is linked toa higher risk of cardiovascular illnesses. A noninvasive ECG signal called P-wave dispersion (Pd) can predict the likelihood of an atrialarrhythmia and reflects atrial remodeling. Pd is calculated by computing the difference between the maximum and minimum P-wavelengths captured from the 12 ECG lead recordings. To assess the early-stage risk of acquiring cardiovascular illnesses, we investigatedthe effect of BMI on P-wave dispersion in healthy, overweight, and obese adults. Materials and Methods: Depending on their body massindex (BMI), we divided 200 participants into three groups (normal weight, overweight, and obese) during a comparative cross-sectionalstudy. We measured 12-lead surface ECG and P-wave dispersion in study participants. Results: Mean P-wave lengths were 33.23(±6.08), 33.15(± 5.16), and 46.15 (± 5.37), respectively, for people who were normal weight, overweight, and obese. Between the normalweightand obese groups and the overweight and obese groups, there was a statistically significant difference (p &lt; 0.001), according toTukey’s post-hoc analysis. However, the normal-weight and overweight groups showed no statistically significant difference (P = 0.997).Pearson’s correlation analysis shows that P-wave dispersion and BMI showed a significant positive connection (r = 0.632). Conclusion:A higher BMI is linked to a longer P-wave duration and dispersion, even in healthy adults. The likelihood of left atrial hypertrophy andatrial arrhythmia increases with a prolonged P-wave duration. This study emphasizes the importance of raising awareness about theneed to adopt healthy lifestyles to avoid the harmful effects of obesity on the heart

Electrophysiological basis of cardiac arrhythmia in a mouse model of myotonic dystrophy type 1.

Introduction: Myotonic dystrophy type 1 (DM1) is a multisystemic genetic disorder caused by the increased number of CTG repeats in 3' UTR of Dystrophia Myotonia Protein Kinase (DMPK) gene. DM1 patients experience conduction abnormalities as well as atrial and ventricular arrhythmias with increased susceptibility to sudden cardiac death. The ionic basis of these electrical abnormalities is poorly understood. Methods: We evaluated the surface electrocardiogram (ECG) and key ion currents underlying the action potential (AP) in a mouse model of DM1, DMSXL, which express over 1000 CTG repeats. Sodium current (INa), L-type calcium current (ICaL), transient outward potassium current (Ito), and APs were recorded using the patch-clamp technique. Results: Arrhythmic events on the ECG including sinus bradycardia, conduction defects, and premature ventricular and atrial arrhythmias were observed in DMSXL homozygous mice but not in WT mice. PR interval shortening was observed in homozygous mice while ECG parameters such as QRS duration, and QTc did not change. Further, flecainide prolonged PR, QRS, and QTc visually in DMSXL homozygous mice. At the single ventricular myocyte level, we observed a reduced current density for Ito and ICaL with a positive shift in steady state activation of L-type calcium channels carrying ICaL in DMSXL homozygous mice compared with WT mice. INa densities and action potential duration did not change between DMSXL and WT mice. Conclusion: The reduced current densities of Ito, and ICaL and alterations in gating properties in L-type calcium channels may contribute to the ECG abnormalities in the DMSXL mouse model of DM1. These findings open new avenues for novel targeted therapeutics.

Localization and characterization of atrial depolarization waves on the surface electrocardiogram in dogs with rapid supraventricular tachycardia.

Supraventricular tachycardias (SVTs), despite having various anatomical substrates and pathophysiological mechanisms, frequently show similar electrocardiographic presentations. To locate and characterize atrial deflections (ADs) on 12-lead electrocardiograms in dogs with sustained rapid SVT and assess the utility of different electrocardiographic variables in differentiating types of tachycardia. Ninety-two dogs with orthodromic atrioventricular reciprocating tachycardia, 17 with atrial flutter, 33 with focal atrial tachycardia recorded and confirmed by electrophysiological study, and 40 dogs with sinus tachycardia. Atrial deflection position on the 12-lead surface electrocardiogram was assessed according to the sequence of intracardiac activation. Its features were evaluated together with the relationship between AD and QRS complex interval (AD-R) and QRS complex and AD interval (R-AD). Orthodromic atrioventricular reciprocating tachycardia was characterized by an AD-AD interval of 213 ± 30 ms, mean electrical axis (MEA) of AD of -90 (-90/-78)°, R-AD interval of 75 (65-80) ms, and R-AD/AD-R of 0.54 (0.45-0.64). Atrial flutter was characterized by an AD-AD interval of 199 ± 57 ms, MEA of 76° (72/81), R-AD of 120 (72-144) ms, and R-AD/AD-R of 0.81 (0.63-1.13). Focal atrial tachycardia was characterized by an AD-AD interval of 270 ± 38 ms, MEA of 49 (-72/76)°, R-AD of 160 (120-200) ms, and R-AD/AD-R of 1.45 (0.92-1.67). Sinus tachycardia was characterized by an AD-AD interval of 292 ± 31 ms, MEA of 66° (52/73), R-AD of 215 (192-222) ms, and R-AD/AD-R of 2.68 (2.25-3.08). Analyzing AD on 12-lead electrocardiogram is helpful in differentiating the most common SVTs in dogs.

Electrophysiologic Determinants of Isoelectric Intervals on Surface Electrocardiograms During Atrial Tachycardia: Insights From High-Density Mapping

BackgroundSubstrate abnormalities can alter atrial activation during atrial tachycardias (ATs) thereby influencing AT-wave morphology on the surface electrocardiogram. ObjectivesThis study sought to identify determinants of isoelectric intervals during ATs with complex atrial activation patterns. MethodsHigh-density activation maps of 126 ATs were studied. To assess the impact of the activated atrial surface on the presence of isoelectric intervals, this study measured the minimum activated area throughout the AT cycle, defined as the smallest activated area within a 50-millisecond period, by using signal processing algorithms (LUMIPOINT). ResultsATs with isoelectric intervals (P-wave ATs) included 23 macro–re-entrant ATs (40%), 26 localized–re-entrant ATs (46%), and 8 focal ATs (14%), whereas those without included 46 macro–re-entrant ATs (67%), 21 localized–re-entrant ATs (30%), and 2 focal ATs (3%). Multivariable regression identified smaller minimum activated area and larger very low voltage area as independent predictors of P-wave ATs (OR: 0.732; 95% CI: 0.644-0.831; P < 0.001; and OR: 1.042; 95% CI: 1.006-1.080; P = 0.023, respectively). The minimum activated area with the cutoff value of 10 cm2 provided the highest predictive accuracy for P-wave ATs with sensitivity, specificity, and positive and negative predictive values of 96%, 97%, 97%, and 95%, respectively. In re-entrant ATs, smaller minimum activated area was associated with lower minimum conduction velocity within the circuit and fewer areas of delayed conduction outside of the circuit (standardized β: 0.524; 95% CI: 0.373-0.675; P < 0.001; and standardized β: 0.353; 95% CI: 0.198-0.508; P < 0.001, respectively). ConclusionsReduced atrial activation area and voltage were associated with isoelectric intervals during ATs.

Correlation between Global Longitudinal and Circumferential Peak Systolic Strain and Coronary Artery Disease Severity as Assessed by the Angiographically Derived SYNTAX Score

Abstract Aim and objectives the main objective of the current study is to assess the correlation between the SYNTAX score in patients undergoing elective coronary angiography and the longitudinal and circumferential peak systolic strain performed at rest by speckle tracking echocardiography to predict the presence, extent and severity of coronary artery disease. Patients and Methods The study included 60 patients suspected to have coronary artery disease either by symptoms or done a positive non-invasive test for myocardial ischemia with no history of previous ischemia and no previously detected segmental wall motion abnormalities in previous resting echocardiogram. All patients were recruited from the catheter laboratory at the cardiology department of Ain Shams university hospitals and they underwent echocardiographic assessment before elective coronary angiography. Results The study included 60 symptomatic patients suspected to have chronic coronary syndrome aging 31 to 77 years (excluding those with significant structural heart disease). All patients underwent clinical evaluation, surface electrocardiogram, transthoracic echocardiographic, twodimensional speckle tracking, and conventional coronary angiography with SYNTAX score calculation. Patients were divided into 3 groups based on the results of the coronary angiogram: normal CAD on angiogram (n = 12), low SS (n = 28, SS &amp;lt; 22), and high SS (n = 20, SS ≥ 22). Age, sex and most of the risk factors had no statistically significant correlation with the SYNTAX score while There was a significant inverse correlation between GLPSS and GCPSS and SS values (r = 0.458, P = 0.0002), (r = 0.411, P = 0.0011) respectively. Conclusion The results of our study suggest that GLPSS and GCPSS might be promising for the detection of patients with high SYNTAX score on coronary angiogram. There is an inverse correlation between resting GLPSS and GCPSS and SYNTAX score as assessed by coronary angiography.

Evaluation of right atrium structure and function in a rat model of monocrotaline-induced pulmonary hypertension: Exploring the possible antiarrhythmic properties of amiodarone.

Atrial arrhythmias (AA) are common in pulmonary hypertension (PH) and are closely associated with poor clinical outcomes. One of the most studied models to investigate PH is the rat model of monocrotaline (MCT) induced PH (MCT-PH). To date, little is known about right atrium (RA) function in the MCT-PH model and the propensity of RA to develop arrhythmias. Therefore, the aim of the study was to evaluate the function of the RA of control (CTRL) and MCT treated rats, and the ability of amiodarone, a classical antiarrhythmic, to prevent the occurrence of AA in the RA in MCT-PH rats. RA function was studied in MCT-PH rats 20 days after a single subcutaneous injection of MCT 50 mg/kg. The histological results indicated the presence of RA and right ventricular hypertrophy. Surface electrocardiogram demonstrated increased P wave duration, PR wave duration and QT interval in MCT rats. RA from MCT rats were more susceptible to develop ex vivo burst pacing arrhythmias when compared to CTRL. Intriguingly, amiodarone in clinical relevant concentration was not able to prevent the occurrence arrhythmias in RA from MCT-PH animals. Hence, we conclude that the rat model of MCT-PH impairs RA structure and function, and acute exposure of RA to amiodarone in clinical relevant concentration is not able to attenuate the onset of arrhythmias in the ex vivo RA preparation.

New pattern of atypical advanced interatrial block

IntroductionInteratrial block (IAB) is defined as prolonged P-wave duration (≥ 120 ms) due to delayed conduction in the Bachmann bundle. This is readily identifiable using surface electrocardiogram (ECG). Advanced IAB can be classified as typical and atypical. Atypical IAB can be further categorized by (i) duration or (ii) morphology. In this report, we have identified a new pattern of atypical IAB with triphasic morphology of the P-wave in the inferior leads. MethodsTwo clinical cases were evaluated including surface ECGs. P-wave durations and amplitudes were measured with digital calipers using ECG analysis software (MUSE, GE HealthCare). Comparisons were made using prior data to evaluate IAB and P-wave duration and morphology. ResultsA new pattern of atypical advanced IAB shows prolonged P-wave duration (P wave >160 ms) and triphasic morphology in all inferior leads with P +/+/− and P +/−/+, respectively. We speculate that triphasic P-waves in the inferior leads represent three moments of atrial depolarization; from right to left. ConclusionThis study describes a novel pattern of atypical advanced IAB. Further investigation regarding the increased risk of atrial fibrillation and stroke associated with this new pattern is warranted in the future.

Response to Cardiac Resynchronization Therapy in Cardiomyopathy Patients with Right Bundle Branch Block.

The use of cardiac resynchronization therapy (CRT) in heart failure patients with right bundle branch block (RBBB) is under debate. We present early and late echocardiographic characteristics of a series of heart failure patients with RBBB who underwent CRT. In this retrospective descriptive study, 18 patients with RBBB in the surface electrocardiogram underwent CRT between 2005 and 2015. All the patients had the New York Heart Association functional class III/IV, a left ventricular ejection fraction (LVEF) ≤35%, and a QRS duration ≥120 milliseconds. The median follow-up duration was 19 months. The echocardiographic response was based on a ≥5% increase in LVEF. Within 48 hours after CRT implantation, LVEF increased from 24.58%±7.08% before to 28.46±8.91% after CRT (P=0.005) and to 30.00±9.44% at follow-up (P=0.008). Among the 18 patients, 12 (66.7%) were responders within 48 hours after CRT. The following baseline echocardiographic parameters were higher in the responders than in those without an increased LVEF, although the difference did not reach statistical significance: septal-to-lateral wall delay (48.33±33.53 vs 43.33±38.82 ms), anteroseptal-to-posterior wall delay (41.7±1.75 vs 38.33±18.35 ms), and interventricular mechanical delay (48.50±21.13 vs 31.17±19.93 ms). The mean QRS duration was higher in the responders than in the non-responders (183.58±40.69 vs 169.00±27.36 ms). Death was reported in 3 out of the 18 patients (16.7%) at follow-up. The 3 deceased patients had a higher baseline interventricular mechanical delay than those who survived. Our results indicated that patients with RBBB might benefit from CRT. Further, patients with higher intra and interventricular dyssynchrony and a wider QRS may show better responses.

Abstract P3050: Cpne5 Is Necessary For Normal Murine Sinoatrial Node Function In Vivo

Cpne5 ( Copine 5 ) is a Ca 2+ -dependent phospholipid binding protein implicated in membrane trafficking but has no known role within the heart to date. In single-cell RNA-sequencing of the developing mouse heart, we previously found Cpne5 gene expression to be highly enriched in the cardiac conduction system (CCS). Further, variants in CPNE5 were identified in 6 independent GWAS related to heart rate variability in humans. We therefore hypothesized that Cpne5 is necessary for normal CCS function through Ca 2+ -dependent membrane trafficking of ion channels. Whole mount immunostaining with 3D volumetric imaging (iDISCO+) of intact WT hearts demonstrated robust and specific protein expression of Cpne5 throughout all CCS components, including the sinoatrial node (SAN) and SAN-transitional cells. Next, to assess the necessity of Cpne5 within the CCS, systemic knockout mice (Cpne5KO) were generated using CRISPR/Cas9. Successful deletion of Cpne5 in CRISPR-generated KO mice was confirmed by PCR and, at the protein level, immunofluorescence. Homozygous Cpne5KO mice were viable, showed normal growth and allele frequencies segregated in a normal Mendelian fashion. No gross anatomic defects of the CCS were observed in Cpne5KO mice and transthoracic echocardiogram studies showed a slight reduction in left ventricular fractional shortening but otherwise a structurally normal heart. Notably, while surface electrocardiogram (ECG) demonstrated normal intervals (PR, QRS, QTc), Cpne5KO mice exhibited pronounced sinus arrhythmia, with notable sinus pauses, sinus bradycardia, and increased heart rate variability as compared to WT littermate controls on long term telemetry. Systemic loss-of-function of Cpne5 in mice resulted in marked sinus node dysfunction, consistent with human GWAS data, implicating a novel role for Cpne5 in CCS function and/or development. Additional phenotyping and molecular studies, including CPNE5 deletion in human induced-pluripotent cell-derived SAN-like cells, are ongoing to further elucidate its underlying mechanism.

Electrocardiography of Atrioventricular Block.

Delayed atrioventricular (AV) conduction most commonly occurs in the AV node, resulting from AH prolongation on an intracardiac electrocardiogram and PR prolongation on a surface electrocardiogram. AV conduction may be blocked in a 2:1 manner, with a normal PR interval and wide QRS suggesting infranodal disease, whereas a prolonged PR interval and narrow QRS are more suggestive of AV nodal disease. Block within the His is suspected when there is 2:1 AV block with normal PR and QRS intervals. Complete heart block occurs when the atrial rhythm is totally independent of a junctional or lower escape rhythm.

Identification of supraventricular tachycardia mechanisms with surface electrocardiograms using a convolutional neural network.

It remains difficult to definitively distinguish supraventricular tachycardia (SVT) mechanisms using a 12-lead electrocardiogram (ECG) alone. Machine learning may identify visually imperceptible changes on 12-lead ECGs and may improve ability to determine SVT mechanisms. We sought to develop a convolutional neural network (CNN) that identifies the SVT mechanism according to the gold standard of SVT ablation and to compare CNN performance against experienced electrophysiologists among patients with atrioventricular nodal re-entrant tachycardia (AVNRT), atrioventricular reciprocating tachycardia (AVRT), and atrial tachycardia (AT). All patients with 12-lead surface ECG during sinus rhythm and SVT and had successful SVT ablation from 2013 to 2020 were included. A CNN was trained using data from 1505 surface ECGs that were split into 1287 training and 218 test ECG datasets. We compared the CNN performance against independent adjudicationby 2 experienced cardiac electrophysiologists on the test dataset. Our dataset comprised 1505 ECGs (368 AVNRT, 304 AVRT, 95 AT, and 738 sinus rhythm) from 725 patients. The CNN areas under the receiver-operating characteristic curve for AVNRT, AVRT, and AT were 0.909, 0.867, and 0.817, respectively. When fixing the specificity of the CNN to the electrophysiologist adjudicators' specificity, the CNN identified all SVT classes with higher sensitivity: (1) AVNRT (91.7% vs 65.9%), (2) AVRT (78.4% vs 63.6%), and (3) AT (61.5% vs 50.0%). A CNN can be trained to differentiate SVT mechanisms from surface 12-lead ECGs with high overall performance, achieving similar performance to experienced electrophysiologists at fixed specificities.