Sinus Rhythm ECG Research Articles

Prediction of incident atrial fibrillation using deep learning, clinical models and polygenic scores.

Deep learning applied to electrocardiograms (ECG-AI) is an emerging approach for predicting atrial fibrillation or flutter (AF). This study introduces an ECG-AI model developed and tested at a tertiary cardiac centre, comparing its performance with clinical and AF polygenic scores (PGS). ECG in sinus rhythm from the Montreal Heart Institute were analysed, excluding those from patients with preexisting AF. The primary outcome was incident AF at 5 years. An ECG-AI model was developed by splitting patients into non-overlapping datasets: 70% for training, 10% for validation, and 20% for testing. Performance of ECG-AI, clinical models and PGS was assessed in the test dataset. The ECG-AI model was externally validated in the Medical Information Mart for Intensive Care-IV (MIMIC-IV) hospital dataset. A total of 669,782 ECGs from 145,323 patients were included. Mean age was 61±15 years, and 58% were male. The primary outcome was observed in 15% of patients and the ECG-AI model showed an area under the receiver operating characteristic curve (AUC) of 0.78. In time-to-event analysis including the first ECG, ECG-AI inference of high risk identified 26% of the population with a 4.3-fold increased risk of incident AF (95% confidence interval 4.02-4.57). In a subgroup analysis of 2,301 patients, ECG-AI outperformed CHARGE-AF (AUC=0.62) and PGS (AUC=0.59). Adding PGS and CHARGE-AF to ECG-AI improved goodness-of-fit (likelihood ratio test p<0.001), with minimal changes to the AUC (0.76-0.77). In the external validation cohort (mean age 59±18 years, 47% male, median follow-up 1.1 year) ECG-AI model performance= remained consistent (AUC=0.77). ECG-AI provides an accurate tool to predict new-onset AF in a tertiary cardiac centre, surpassing clinical and polygenic scores.

Predictors of neuromodulation during single-shot thermal atrial fibrillation ablation: a computed tomography imaging analysis

Abstract Background Ganglionated plexi (GPs) are part of the cardiac autonomic nervous system (CANS), which play an important role in the induction and maintenance of atrial fibrillation (AF). Because of the anatomic proximity of the pulmonary veins with GPs, pulmonary vein isolation (PVI) using thermal (radiofrequency (RF) or cryo) ablation, has been shown to concomitantly affect the GPs, as suggested by increased heart rate (HR) after ablation. Right superior pulmonary vein (RSPV) isolation has been reported to have the most evident inhibitory responses to the CANS via modulation of the anterior right ganglionated plexus. Nonetheless, there are also cases with little to no effect on neuromodulation post-ablation. Purpose To assess the clinical and anatomical predictors of thermal ablation-induced CANS changes, as assessed via variations in resting HR post-ablation. Methods We retrospectively analyzed consecutive patients with symptomatic paroxysmal AF who underwent first-time PVI by cryoballoon (CB) or RF balloon (RFB) between January 2022 to November 2022. Patients who were not in sinus rhythm, had cardiac implantable electronic devices, or did not have a contrast-enhanced computed tomography before the ablation were excluded. Pre-ablation and 24-hour post-ablation ECGs in sinus rhythm were collected and analyzed to assess HR. Results A total of 83 patients (CB group, n = 43 vs RFB group, n = 40) were included in the analysis. Baseline patients’ characteristics were similar between the two groups (Table 1). A significant HR increase (ΔHR ≥ 15 bpm) at 24h post-ablation occurred in 31 patients [CB group 16 (52 %), RFB group 15 (48 %)]. Predictors of increased HR were the distance between superior vena cava (SVC) and RSPV (OR 0.51, CI 0.35 - 0.75, p value &amp;lt; 0.01) and age (OR 0.94, CI 0.89 - 0.99, p value &amp;lt; 0.01) (Table 2). Conclusions Single-shot thermal PVI influences the CANS, especially in patients with a shorter SVC - RSPV distance and in younger patients.Table 1Table 2

Predicting atrial fibrillation ablation success: a novel approach using pre-procedure sinus rhythm ecg and deep learning

Abstract Introduction Pulmonary vein isolation (PVI) has been the corner stone of the treatment of patients with atrial fibrillation (AF), yet its efficacy varies notably between patient groups. Specifically, it shows reduced efficacy in individuals with persistent AF and those with atrial fibrosis. Recent advancements suggest that additional substrate modification could enhance outcomes in specific patient groups. Therefore, predicting the outcomes of lone PVI could help in stratifying patients who would benefit most from this additional substrate modification. Addressing this gap, our study explores the prognostication of PVI outcomes using pre-procedural 12-lead ECG recordings taken in Sinus rhythm, employing advanced deep learning techniques. Methods This retrospective study analyzed 209 patients with paroxysmal and persistent AF who underwent PVI. These patients were followed up for 12 months after the blanking period. Pre-procedural sinus rhythm 12-lead ECGs were processed using an innovative deep learning architecture, integrating Long Short-Term Memory and Convolutional Neural Networks with a cross-attention module. This multimodal deep learning model was trained using ECG and clinical covariates like age, sex, and BMI to predict binary outcomes – AF type (Paroxysmal or Persistent) and AF recurrence. The model's efficacy was evaluated through a 5-fold stratified cross-validation to ensure accuracy and reliability. Model was also tested using only ECG data. Results The multimodal deep learning model performed significantly better as compared to the model trained using ECG data only. The multimodal deep learning model achieved an accuracy of 75.56% and a sensitivity of 82% in prognosticating ablation outcomes. Additionally, the model demonstrated a significant capability in classifying AF into paroxysmal or persistent types with an accuracy of 86.67% and a recall of 89%. Conclusion Our deep learning approach in analyzing sinus rhythm ECG recordings shows promising results in predicting PVI outcomes and identifying AF types. This approach could offer a more accessible and cost-effective tool to stratify AF patients undergoing PVI, identifying those less likely to benefit from standard procedures. Such insights can assist physicians and patients in determining ablation candidacy and selecting the most appropriate ablation strategy and technique.Proposed StrategyDeep learning model approach

Heart rate dynamics after pulmonary vein isolation: pulsed-field vs. radiofrequency ablation

Abstract Background The autonomic nervous system (ANS) plays a pivotal role in the onset and maintenance of atrial fibrillation (AF). During thermal ablation, the epicardial ganglionated plexi (GP) can inadvertently be affected, as indicated by a post-ablation increase in heart rate (HR), potentially impacting post-procedural outcomes. Pulsed-field ablation (PFA) offers a safer and potentially more effective alternative, as lesion transmurality is not inversely related to safety as in thermal ablation. However, PFA's tissue selectivity may limit its effectiveness in ablating potential AF triggers like GP. Purpose We aimed to assess the impact of PF and RF energy on GP ablation by examining HR changes following PVI procedures. Methods We examined consecutive paroxysmal AF patients undergoing PVI at a single center, using either a variable-loop circular PFA catheter, as part of the inspIRE trial, or a conventional point-by-point RF catheter (1:2 ratio). ECGs were collected before and after ablation at multiple time points, including 1-hour, 1-day, 1-month, and 3-month intervals, with a 24-hour Holter ECG at 3 months. Patients without obtainable sinus rhythm ECGs at these time points were excluded. We also gathered data on post-ablation antiarrhythmic drug and beta-blocker usage. Data are reported as mean±SD, and analysis employed Student’s t-tests, paired t-tests, chi-squared tests, and multiple linear regression analysis to identify influencing factors. Results A total of 105 patients were included (PF = 35, RF = 70). Baseline demographics were similar (Table A). In the RF group, HR significantly increased at all measured time-points. In contrast, this trend was not observed in the PF group (Figure A and B). Following the procedure, the RF cohort exhibited a significantly higher HR compared to PF (7.3±10.3 vs. 0.8±11 at 1-day, p=0.018), and this difference persisted after 3 months (8.5±10.1 vs. 0.9±9, p&amp;lt;0.001, Figure C). These findings remained consistent when HR was assessed using Holter ECG data (HR at 3 months: mean 72.1±8.8 vs 65.8±9.4, min 55.2 vs 48.9; p=0.002, p=0.001, Figure D). The use of RF energy was the only predictor of HR increase after the ablation procedure (β = 7.687, p &amp;lt; 0.001, Table B). Conclusion Our findings indicates that PFA does not increase resting HR as seen with thermal RF ablation, suggesting limited effect on ANS modulation. The role of adjunctive GP ablation during PVI procedures remains a topic of debate.While PFA shows promising improvement in lesion durability, current evidence does not conclusively establish its advantage over RF. This invites contemplation about whether the addition of GP ablation in RF procedures could be a contributing factor. The absence of HR acceleration may pose issues for patients with sick sinus syndrome or pure vagally mediated AF. Further research is essential to confirm these findings and their clinical implications.Central illustrationTables

Lead-Specific Performance for Atrial Fibrillation Detection in Convolutional Neural Network Models Using Sinus Rhythm Electrocardiography.

Background: We developed a convolutional neural network (CNN) model to detect atrial fibrillation (AF) using the sinus rhythm ECG (SR-ECG). However, the diagnostic performance of the CNN model based on different ECG leads remains unclear. Methods and Results: In this retrospective analysis of a single-center, prospective cohort study, we identified 616 AF cases and 3,412 SR cases for the modeling dataset among new patients (n=19,170). The modeling dataset included SR-ECGs obtained within 31 days from AF-ECGs in AF cases and SR cases with follow-up ≥1,095 days. We evaluated the CNN model's performance for AF detection using 8-lead (I, II, and V1-6), single-lead, and double-lead ECGs through 5-fold cross-validation. The CNN model achieved an area under the curve (AUC) of 0.872 (95% confidence interval (CI): 0.856-0.888) and an odds ratio of 15.24 (95% CI: 12.42-18.72) for AF detection using the eight-lead ECG. Among the single-lead and double-lead ECGs, the double-lead ECG using leads I and V1 yielded an AUC of 0.871 (95% CI: 0.856-0.886) with an odds ratio of 14.34 (95% CI: 11.64-17.67). Conclusions: We assessed the performance of a CNN model for detecting AF using eight-lead, single-lead, and double-lead SR-ECGs. The model's performance with a double-lead (I, V1) ECG was comparable to that of the 8-lead ECG, suggesting its potential as an alternative for AF screening using SR-ECG.

The R'' wave in V1 and the terminal QRS vector in aVF combine to a novel 12-lead ECG algorithm to identify slow conducting anatomical isthmus 3 in patients with tetralogy of Fallot

Abstract Background Patients with repaired tetralogy of Fallot (rTOF) have an increased risk for ventricular tachycardia (VT), with slow conducting anatomical isthmus (SCAI) 3 as the dominant VT substrate. Right bundle branch block (RBBB) is common in these patients. SCAI 3 can lead to additional activation delay affecting the terminal RV activation.(1) We hypothesize that these alterations can be detected by sinus rhythm (SR) 12-lead ECG. Purpose To develop a novel algorithm to non-invasively identify SCAI 3 with the SR 12-lead ECG in rTOF patients with RBBB. Methods Consecutive rTOF patients aged ≥16 years old with RBBB who underwent electroanatomical mapping (EAM) at our institution between 2017-2022, and 2010-2016, comprised the derivation, and validation cohort, respectively. 12-lead ECGs in SR were analyzed for presence of R’’ wave in V1 (any positive deflection after R’ in V1) and for duration of a negative terminal QRS portion (NTP) in aVF (ms between QRS complex traversing the isoelectric line from positive to negative vector until QRS offset) (Figure, A). Results Forty-six rTOF patients (aged 40±15 years, 70% male) comprised the derivation cohort. Patients were repaired at a median age of 3.3 [IQR 1.2-6.6] years via a ventricular incision in 21 (55%). Mean QRS duration was 165±23ms and 12 (26%) had a QRS duration of ≥180ms. SCAI 3 was detected in 31 (67%) patients and 15 (33%) had a normal conducting AI 3. Monomorphic sustained VT was inducible in 16 (35%). Among patients with SCAI 3, 17 (55%), and 16 (52%), had an R’’ in V1, and NTP ≥80ms in aVF, respectively, compared to only 1 (7%), and 1 (7%) patients with normal conducting AIs. Of the 24 patients with an R’’ in V1 and/or NTP ≥80ms in aVF, 22 (92%) had a SCAI 3. In univariable analysis an R’’ in V1 had an odds ratio (OR) of 17.0 (p=0.010), a NTP ≥80ms in aVF had an OR of 14.9 (p=0.014), and QRS duration had an OR of 1.04 (per ms, p=0.030) in identifying SCAI 3. After correction for QRS duration, R’’ in V1 and NTP ≥80ms in aVF both remained independently predictive of SCAI 3 (OR 15.4, p=0.017, and OR 10.0, p=0.042, respectively). Combining R’’ in V1 and NTP ≥80ms in aVF into a diagnostic algorithm (Figure, B) yielded a sensitivity of 71% and specificity of 87% in detecting SCAI 3. In the validation cohort (n=34, age 44±14 years, 19 (56%) with SCAI 3 and 15 (44%) with normal conducting AI 3) the diagnostic algorithm had a sensitivity of 84% and specificity of 80% for a SCAI. Conclusion A diagnostic algorithm including R’’ in V1 and NTP ≥80ms in aVF had excellent sensitivity and specificity in detecting SCAI 3 in rTOF patients with RBBB in SR. These ECGs characteristics likely represent a delayed terminal RV activation directed towards the lateral RVOT caused by slow infundibular conduction. Whether this algorithm can contribute to non-invasive risk stratification for VT in a general rTOF population requires additional studies.Diagnostic algorithm to identify SCAI 3

The novel P-Wave Area index predicts low voltage areas in patients undergoing pulmonary vein isolation for treatment of atrial fibrillation

Abstract Introduction Presence of low voltage areas (LVA) mirrors progression of atrial fibrillation (AF) and is associated with an impaired rhythm outcome after AF ablation. P-wave indices can predict LVA, however accuracy is depending on myocardial mass, therefore the P Wave area was indexed for the left atrial area. The aim of current analysis was to investigate the association between the novel P-Wave Area Index (PWAi) and the presence of LVA. Methods Patients undergoing first AF ablation were included into analysis. LVA were determined using high-density maps and defined as &amp;lt;0.5 mV. All patients underwent CMR imaging (Ingenia 1.5T Philips) and received 12 lead ecg in sinusrhythm before intervention. CMR data (LA area) and P-Wave Area in Leads I ( PWA(I)) and II (PWA(II)) were measured in all patients. Afterwards the P-Wave Area was indexed for LA area to create the PWAi. Results A total of 264 consecutive patients were included in the analysis (65 years, 41% females, 57% persistent AF, 30.6% LVAs). Of all analyzed P-Wave indices, P Wave Area in lead II could effectively identify LVA (AUC: 0.813, p&amp;lt;0.001). In patients with LVA, PWAi below 0.4781 msec*mV*m²/cm² identified patients with LVA with an accuracy of 81% (81% sensitivity, 81% specificity, 65% pos. predictive value and 91% neg. predictive value) and was the strongest predictor among all analyzed parameters (OR: 20,578, AUC; 0.860) Conclusion The PWAi is a highly significant tool to identify potential LVA before AF Ablation. This findings help to further individualize AF treatment.Multivariate AnalysisROC analysis

Deep Learning to Identify Undiagnosed AF Using ECGs in Sinus Rhythm—Should We Rewire Our Models?

Deep Learning to Identify Undiagnosed AF Using ECGs in Sinus Rhythm—Should We Rewire Our Models?

Clinical applicability of an artificial intelligence prediction algorithm for early prediction of non-persistent atrial fibrillation.

It is difficult to document atrial fibrillation (AF) on ECG in patients with non-persistent atrial fibrillation (non-PeAF). There is limited understanding of whether an AI prediction algorithm could predict the occurrence of non-PeAF from the information of normal sinus rhythm (SR) of a 12-lead ECG. This study aimed to derive a precise predictive AI model for screening non-PeAF using SR ECG within 4 weeks. This retrospective cohort study included patients aged 18 to 99 with SR ECG on 12-lead standard ECG (10 seconds) in Ewha Womans University Medical Center for 3 years. Data were preprocessed into three window periods (which are defined with the duration from SR to non-PeAF detection) - 1 week, 2 weeks, and 4 weeks from the AF detection prospectively. For experiments, we adopted a Residual Neural Network model based on 1D-CNN proposed in a previous study. We used 7,595 SR ECGs (extracted from 215,875 ECGs) with window periods of 1 week, 2 weeks, and 4 weeks for analysis. The prediction algorithm showed an AUC of 0.862 and an F1-score of 0.84 in the 1:4 matched group of a 1-week window period. For the 1:4 matched group of a 2-week window period, it showed an AUC of 0.864 and an F1-score of 0.85. Finally, for the 1:4 matched group of a 4-week window period, it showed an AUC of 0.842 and an F1-score of 0.83. The AI prediction algorithm showed the possibility of risk stratification for early detection of non-PeAF. Moreover, this study showed that a short window period is also sufficient to detect non-PeAF.

Impact of different energy sources on the cardiac autonomic nervous system in the setting of single-shot pulmonary vein isolation

Abstract Funding Acknowledgements Type of funding sources: None. Background The cardiac autonomic nervous system is an epicardial neural network composed of ganglionated plexi (GPs) and interconnecting neurons, which plays a significant role in the induction and maintenance of atrial fibrillation (AF). Targeted GPs ablation in addition to pulmonary vein isolation (PVI) has been associated with superior AF outcomes. Due to the anatomical proximity of the GPs with the pulmonary veins, PVI with cryoballoon catheter ablation (CBA) has shown to concomitantly affect the GPs, as documented by increased heart rates (HR) after ablation. Recently, pulsed-field ablation (PFA) has emerged as a safe and effective non-thermal alternative for single-shot PVI, with specific selectivity for the cardiac tissue. The impact of PFA on the cardiac autonomic nervous system is still to be elucidated. Purpose To assess the effect of PFA on the cardiac autonomic nervous system, evaluated with the variation in resting heart rate pre- and post-ablation. Methods We retrospectively included all consecutive patients with symptomatic paroxysmal AF undergoing first-time PVI with the multispline PFA catheter between January and September 2022. They were compared with consecutive patients undergoing PVI with CBA in the same study period. Patients not in sinus rhythm before ablation or with cardiac implantable electronic devices were excluded. All antiarrhythmic drugs were discontinued at least 3 days before ablation. Pre-ablation and 24-hour post-ablation ECGs in sinus rhythm were collected and analysed to assess HR. Results A total of 90 patients (PFA group, n=30 vs CBA group, n=60) were included in the analysis. Baseline patients’ characteristics were similar between the two groups (Figure 1). Pre-ablation HRs in the PFA and the CBA groups were 64.2 ± 10.3 bpm and 64.2 ± 12.8 bpm, respectively. Vagal reactions during ablation occurred more frequently with PFA than CBA (77% vs 38%, p=0.001). After 24h post-ablation, the HR increase in the PFA and CBA groups was 2.2 ± 5.4 bpm and 12.4 ± 11.9 bpm, respectively (p&amp;lt;0.001, Figure 2). Conclusion PVI with the novel multispline PFA catheter is associated with less increase in HR 24 hours after ablation compared to CBA. This effect may be the result of tissue selectivity of PFA for cardiomyocytes, which has minimal effect on extracardiac structures, including the cardiac autonomic nervous system.

Diagnostic gain of cardiac MR in patients with premature ventricular complexes and structurally normal hearts on echocardiography

Abstract Funding Acknowledgements Type of funding sources: Public Institution(s). Main funding source(s): KG Jebsen Centre for Cardiac Research, University of Oslo, Oslo, Norway Background An important part of the diagnostic evaluation of patients with premature ventricular complexes (PVCs) is to identify underlying cardiac disease. The majority of these patients referred to catheter ablation have PVCs with inferior axis and left bundle branch block morphology (I-LBBB). Although this is the most common morphology in patients with idiopathic PVCs, cardiac magnetic resonance (CMR) is recommended to exclude potential other underlying pathology. However, the diagnostic gain of CMR in patients with I-LBBB PVCs is uncertain. Purpose The purpose of this study was to explore the prevalence of cardiac abnormalities identified by CMR in patients with I-LBBB PVCs evaluated for catheter ablation without signs of underlying cardiac disease from resting ECG, stress test, or echocardiography. Methods We retrospectively collected data from consecutive patients with I-LBBB PVCs evaluated for catheter ablation at our tertiary referral centre from 2011 to 2022. We included patients with normal ECG and stress test, and no evidence of functional or structural heart disease by echocardiography. We categorized CMR examinations as normal or as having signs of functional or structural abnormalities. Results We identified a total of 63 patients with I-LBBB PVCs evaluated for catheter ablation, with no signs of underlying conditions from sinus rhythm ECG, stress test or echocardiography. All were studied by CMR at our institution. The median age was 49 years (IQR 36-61), and 39 were females (62 %). The maximum PVC burden recorded in each patient prior to referral was 20 % (IQR 11-29.5). Left ventricular (LV) ejection fraction by echocardiography was 56±4 %. CMR was normal in 52 patients (83 %), while functional or structural abnormalities were detected in 11 patients (17 %). These abnormalities comprised reduced LV systolic function (2 patients), dilated right ventricle (2 patients), dilated LV (1 patient), ventricular septal defect (2 patients), non-compaction cardiomyopathy (1 patient), mitral annular disjunction (2 patients), tricuspidal annular disjunction (1 patient), and myocardial fibrosis by late contrast enhancement (3 patients). Conclusions In patients with I-LBBB PVCs evaluated for catheter ablation, and with no signs of underlying pathology from standard diagnostic work-up, CMR detected abnormalities in 17 %. These results support the routine use of CMR in the evaluation of patients with I-LBBB PVCs referred for catheter ablation.

Predicting risk of AF in ischaemic stroke using sinus rhythm ECG abnormalities: A meta-analysis.

To identify ECG changes in sinus rhythm that may be used to predict subsequent development of new AF. We identified prospective and retrospective cohort or case control studies evaluating ECG patterns from a 12-lead ECG in sinus rhythm taken in hospital or community predicting subsequent development of new AF. For each identified ECG predictor, we then identify absolute event rates and pooled risk ratios (RR) using an aggregate level random effects meta-analysis. We identified 609,496 patients from 22 studies. ECG patterns included P wave terminal force V1 (PTFV1), interatrial block (IAB) and advanced interatrial block (aIAB), abnormal P wave axis (aPWA), PR prolongation and atrial premature complexes (APCs). Pooled risk ratios reached significance for each of these; PTFV1 RR 1.48 (95% CI 1.04-2.10), IAB 2.54 (95% CI 1.64-3.93), aIAB 4.05 (95% CI 2.64-6.22), aPWA 1.89 (95% CI 1.25-2.85), PR prolongation 2.22 (95% CI 1.27-3.87) and APCs 3.71 (95% CI 2.23-6.16). Diabetes reduced the predictive value of PR prolongation. APC and aIAB were most predictive of AF, while IAB, PR prolongation, PTFV1 and aPWA were also significantly associated with development of AF. These support their use in a screening tool to identify at risk cohorts who may benefit from further investigation, or following stroke, with empirical anticoagulation.

Utility of an Artificial Intelligence Enabled Electrocardiogram for Risk Assessment in Liver Transplant Candidates.

Post-operative cardiac complications occur infrequently but contribute to mortality after liver transplantation (LT). Artificial intelligence-based algorithms based on electrocardiogram (AI-ECG) are attractive for use during pre-operative evaluation to screen for risk of post-operative cardiac complications, but their use for this purpose is unknown. The aim of this study was to evaluate the performance of an AI-ECG algorithm in predicting cardiac factors such as asymptomatic left ventricular systolic dysfunction or potential for developing post-operative atrial fibrillation (AF) in cohorts of patients with end-stage liver disease either undergoing evaluation for transplant or receiving a liver transplant. A retrospective study was performed in two consecutive adult cohorts of patients who were either evaluated for LT or underwent LT at a single center between 2017 and 2019. ECG were analyzed using an AI-ECG trained to recognize patterns from a standard 12-lead ECG which could identify the presence of left ventricular systolic dysfunction (LVEF < 50%) or subsequent atrial fibrillation. The performance of AI-ECG in patients undergoing LT evaluation is similar to that in a general population but was lower in the presence of prolonged QTc. AI-ECG analysis on ECG in sinus rhythm had an AUROC of 0.69 for prediction of de novo post-transplant AF. Although post-transplant cardiac dysfunction occurred in only 2.3% of patients in the study cohorts, AI-ECG had an AUROC of 0.69 for prediction of subsequent low left ventricular ejection fraction. A positive screen for low EF or AF on AI-ECG can alert to risk of post-operative cardiac dysfunction or predict new onset atrial fibrillation after LT. The use of an AI-ECG can be a useful adjunct in persons undergoing transplant evaluation that can be readily implemented in clinical practice.

The masquerade: What is the wide complex tachycardia?

A 19-year-old boy with rheumatic heart disease, post mitral valve replacement three years earlier, presented with recurrent episodes of palpitations. Sinus rhythm ECG showed left bundle branch block (LBBB). During symptoms, ECG showed regular wide complex tachycardia (WCT) at a rate of 140 beats per min (bpm) with QRS identical to sinus rhythm. Echocardiography revealed a dilated left ventricle with severely depressed systolic function. An electrophysiology study was performed. Four catheters - high right atrium, coronary sinus, right ventricle (RV), and multipolar catheter with close electrode spacing in the His region - were placed. The baseline His-Ventricle (HV) interval was 70 msec. Ventricular pacing showed central and decremental retrograde conduction. Clinical tachycardia at a rate of 140 bpm was initiated during atrial extra stimulus protocol with isoprenaline. The initiation and continuation of the tachycardia are shown (Figures1 and2). What is the best explanation for the arrhythmia seen? This article is protected by copyright. All rights reserved.

A Single-lead ECG algorithm to differentiate right from left manifest accessory pathways: A reappraisal of the P-Delta interval.

Despite numerous ECG algorithms being developed to localize the site of manifest accessory pathways (AP), they often require stepwise multiple-lead analysis with variable accuracy, limitations, and reproducibility. The study aimed to develop a single-lead ECG algorithm incorporating the P-Delta interval (PDI) as an adjunct criterion to discriminate between right and left manifest AP. Consecutive WPW patients undergoing electrophysiological study (EPS) were retrospectively recruited and split into a derivation and validation group (1:1 ratio). Sinus rhythm ECG analysis in lead V1 was performed by three independent investigators blinded to the EPS results. Conventional ECG parameters and PDI were assessed through the global cohort. A total of 140 WPW patients were included (70 for each group). A score-based, single-lead ECG algorithm was developed through derivation analysis incorporating the PDI, R/S ratio, and QRS onset polarity in lead V1. The validation group analysis confirmed the proposed algorithm's high accuracy (95%), which was superior to the previous ones in predicting the AP side (p < 0.05). A score of ≤+1 was 96.5% accurate in predicting right AP while a score of ≥+2 was 92.5% accurate in predicting left AP. The new algorithm maintained optimal performance in specific subgroups of the global cohort showing an accuracy rate of 90%, 92%, and 96% in minimal pre-excitation, posteroseptal AP, and pediatric patients, respectively. A novel single-lead ECG algorithm incorporating the PDI interval with previous conventional criteria showed high accuracy in differentiating right from left manifest AP comprising pediatric and minimal pre-excitation subgroups in the current study.

Incidence and Outcome of Newly Diagnosed Atrial Fibrillation in Medical ICU Patients: An Observational-Descriptive Study

Abstract Introduction: Cardiac arrhythmias frequently occur in critically ill patients in the intensive care unit (ICU), significantly impacting their health outcomes. Among these arrhythmias, supraventricular cardiac arrhythmic tachycardia, particularly atrial fibrillation (AF), is increasing in incidence and spans various medical and surgical contexts. In medical patients, the incidence ranges from 1%–46%. In patients with sepsis, the incidence of acute AF varies with the severity of sepsis, with an incidence of 8–10% in sepsis, 6–22% in severe sepsis, and 23–44% in septic shock. The development of cardiac arrhythmias in critically ill patients poses a substantial risk to both morbidity and mortality. Aim: To study the incidence and outcome of new-onset AF (NOAF) in medical ICU patients. Materials and Methods: NOAF was identified in patients with documented sinus rhythm ECG upon admission to the medical ICU who later developed AF. Results: Four hundred and twenty patients underwent 24-h continuous ECG monitoring, with 68 developing new-onset arrhythmias. Among them, 21 developed AF (5%). AF predominated in patients with preexisting cardiac illness (76.1%), with pneumonia being the primary non-cardiac cause. Notably, among 21 patients with AF, 12 individuals (57.1%) experienced mortality, while 9 patients (42.8%) were discharged from medical care. Notably, among the discharged patients, 5 individuals (45.4%) continued to exhibit persistent AF. Conclusion: It has been observed that there is an increased incidence of AF among critically ill patients, correlating with heightened rates of mortality and morbidity. Early detection and treatment hold promise in altering disease trajectories. Hence, the implementation of 24-h ECG monitoring is advocated for critically ill patients. Furthermore, a significant fraction of acute illness survivors experience new-onset AF after being discharged, which raises their long-term risks of heart failure, ischemic stroke, and death. However, the lack of expert consensus documents or evidence-based guidelines for managing NOAF highlights the need for carefully planned multicenter, prospective randomized trials to address these clinical uncertainties.

484 PAPILLARY FIBROELASTOMA OF THE AORTIC VALVE ASSOCIATED WITH MYOCARDIAL INFARCTION, COMPLICATED BY ELECTRICAL STORM AND ENDOVENTRICULAR THROMBOSIS

Abstract Introduction Papillary fibroelastomas (PFE) are reported to be the most common tumors of the heart valves and are a possible source of systemic embolism, including myocardial infarction. Case Report We report the case of a 67-year-old female with no background of cardiovascular disease, admitted to our hospital with worsening dyspnea and chest tightness over the prior 10 days. The ECG showed a sustained hemodynamically tolerated monomorphic ventricular tachycardia, successfully treated with antiarrhythmic therapy, and ECG in sinus rhythm revealed signs of previous anterior myocardial infarction. Transthoracic echocardiogram showed a severely depressed systolic function (LVEF 19%), akinesia and thinning of the anterior wall and of all middle-apical segments, complicated by left ventricular aneurysm and endoventricular thrombosis. In the ventricular side of right aortic coronary cusp a mobile, pedunculated mass of 12×8 mm was visualized. A subsequent TEE corroborated the suspicion of PFE. She underwent cardiac MRI which showed areas of transmural late enhancement in all akinetic segments. A coronary angiography revealed sub-occlusion of the mid-anterior descending artery with no angiographic evidence of coronary atherosclerosis elsewhere. In view of her reduced LVEF and history of VT, an ICD was implanted. She started oral anticoagulant therapy and complete resolution of the endoventricular thrombosis was evidenced. After several days of electrical instability, characterized by multiple episodes of ventricular tachycardia, the clinical course was characterized by progressive clinical and electrical improvement and she was discharged in good clinical conditions after four weeks. Due to the severe impairment of the systolic function, not susceptible to surgical remodeling and the high operative risk, no indication for surgical removal of the mass was made. Conclusion Aortic PFE carry a not negligible risk of acute coronary syndrome, electrical storm and sudden death. It should be considered as the cause of myocardial infarction, and not an incidental finding, especially in patients with no angiographic evidence of coronary atherosclerosis.

Is the association of QTc with atrial fibrillation and stroke in cohort studies a matter of time?

ObjectivesTo investigate the association of the heart rate-corrected QT interval (QTc) with the risk of atrial fibrillation (AF) and ischaemic stroke.MethodsWe estimated the risk of AF and ischaemic stroke associated...

Awareness of a Rare Diagnosis: CIA of the Coronary Sinus (A Case Report)

Interatrial communication is a common congenital heart disease in adulthood. It is subdivided into four distinct groups based on location. Coronary sinus type is the rarest one and represents less than 1% of interatrial septal defects. Its echocardiographic diagnosis can be difficult. We report the case of a 27-year-old female patient, admitted to the emergency department for management of a poorly tolerated supraventricular tachycardia, referred to cardiology after cardioversion. Clinical examination revealed a SaO2 of 95%, a B2 burst at the pulmonary site. The ECG in sinus rhythm showed right bundle branch block and right atrial hypertrophy. Echocardiography showed dilated right chambers, coronary sinus atrial septal defect with a left-to-right shunt, dysplastic mitral valve, and pulmonary pression was estimated at 85mmhg on tricuspid insufficiency flow; right ventricular function was normal. The patient was treated with B-blocker and VKA with a good clinical course. She is waiting a right catheterization to decide on surgical closure of the atrial septal defect. Through this case, we wanted to underline the importance of not omitting the echocardiographic search for a defect of the roof of the interatrial septum in front of a volumetric overload of the right cavities.

Improvement of a convolutional neural network using synthetic ECGs for the diagnosis of different atrial rhythms

Abstract Funding Acknowledgements Type of funding sources: None. Introduction The diagnostic capacity of convolutional neural networks has demonstrated its potential for the diagnosis of different ECG alterations. An important limitation of these networks is that there are classes that are underrepresented in the population, such as the Brugada pattern compared to right bundle branch block or Atrial Flutter (AFL) compared to atrial fibrillation (AF). This asymmetry that exists in any population ECG database is one of the limitations (F1-Score) that could be improved, either with even larger databases (impractical) or by creating synthetic representations of the ECG. Purpose Using a tool that allows the drawing of specific electrocardiographic patterns first and subsequently, the random creation of different ECGs based on said pattern, the diagnostic capacity of any unbalanced convolutional network could be improved. Methods Initially, a database with 95,000 ECGs from our health area was used. From this data set, 5,810 ECGs were taken that were labeled in three classes (1) Sinus Rhythm (SR), 2) AF and 3) AFL by two arrythmologists, 4,637 for training and 1,173 for validation. It was observed that the network was unbalanced (SR: 81.3% of the total, FA: 16.4% of the total and AFL: 2.2% of the total). After having demonstrated the good capacity of the network for the diagnosis of these three atrial rhythms, it was observed that the diagnostic dispersion matrix erred precisely between the ECGs of AF and AFL (figure 1). Based on this idea, it was decided to create a random pattern-based ECG creation tool. Different atrial flutter patterns were created and 2000 ECGs were synthetically created with variations of the ECG frequency, voltages and morphology. A new neural network was subsequently trained with the addition of these ECGs to the original database and its diagnostic capacity was evaluated on the same initial validation set. Results The new network showed a 88,3% sensitivity (vs 82,6%), a 100% specificity (vs 98,5%) and an 92% (vs 87,8%) accuracy. It specially had an improvement in AFL ECGs (70,2% vs 61%) and minor improvements in AF ECGs (90,8% vs 89,4%) and SRs ECGs (100% vs 98,6%) accuracy. Conclusions The creation of synthetic ECGs to improve the diagnostic capacity of neural networks for infrequent events is feasible. This application allows the development of networks applicable in real life, in this case, for the diagnosis of atrial arrhythmias.