Standard 12-lead ECG Research Articles

Spatio-Temporal Analysis of Acute Myocardial Ischaemia Based on Entropy-Complexity Plane.

Myocardial ischaemia is a decompensation of the oxygen supply and demand ratio, often caused by coronary atherosclerosis. During the initial stage of ischaemia, the electrical activity of the heart is disrupted, increasing the risk of malignant arrhythmias. The aim of this study is to understand the differential behaviour of the ECG during occlusion of both the left anterior descending (LAD) and right anterior coronary artery (RCA), respectively, using spatio-temporal quantifiers from information theory. A standard 12-lead ECG was recorded for each patient in the database. The control condition was obtained initially. Then, a percutaneous transluminal coronary angioplasty procedure (PTCA), which encompassed the occlusion/reperfusion period, was performed. To evaluate information quantifiers, the Bandt and Pompe permutation method was used to estimate the probability distribution associated with the electrocardiographic vector modulus. Subsequently, we analysed the positioning in the H×C causal plane for the control and ischaemia. In LAD occlusion, decreased entropy and increased complexity can be seen, i.e., the behaviour is more predictable with an increase in the degree of complexity of the system. RCA occlusion had the opposite effects, i.e., the phenomenon is less predictable and exhibits a lower degree of organisation. Finally, both entropy and complexity decrease during the reperfusion phase in LAD and RCA cases.

Computerized QT and QTc Measurements From Bedside ICU Monitors Are Similar to Those Derived From a Standard 12-Lead ECG.

Evaluate the agreement between computerized QT/QTc measurements from the bedside monitor (four leads) and a time-matched standard 12-lead ECG. Prospective observational study in three adult ICUs. QT/QTc measurements were obtained from a convenience sample, and the two ECG types were ≤ 30 min apart. Agreement was evaluated using Bland-Altman analysis. A total of 120 patients were evaluated for inclusion, and 60 (50%) had a 12-lead ECG for comparison. The mean bias difference for QT measurements was not statistically different (β = -2.47, 95% CI = 5.50 to -11.05; p = 0.44; limits of agreement (LOA) = -64.37 to 59.44). Similar non-statistical differences were observed for QTc (β = -3.20, 95% CI = 5.50 to -11.05; p = 0.44; LOA = -67.43 to 61.03). There was good agreement for both QT and QTc measurements between the two methods. These pilot data are promising and suggest QT/QTc measurements from bedside monitors (four leads) may be an acceptable alternative to obtaining additional standard 12-lead ECGs. Given that half of the ICU patients screened did not have a 12-lead ECG recorded, bedside monitor QT/QTc's could identify at-risk patients. However, an evaluation in a larger sample and non-ICU patients is warranted.

A study on QT dispersion and thrombolytic therapy in acute myocardial infarction

Background: Myocardial infarction is a major manifestation of ischemic heart disease, a leading cause of death in developed nations and the third globally. QTc dispersion is an important marker of ventricular repolarization variations and arrhythmogenic risk. This study examines the effects of thrombolytic therapy on QTc dispersion in acute myocardial infarction Materials and methods: 88 patients admitted to Al Ameen Medical College Hospital, Vijayapur, for acute MI were included in the study. Over a period of 8±2 days, all patients underwent monitoring, with standard 12-lead ECGs conducted upon admission and before discharge. QT interval, QTc interval, and QT and QTc dispersion parameters were calculated from these ECGs. Results: Analysis revealed significant variations in QT parameters between patients treated and not treated with thrombolytic therapy. Patients receiving thrombolytic therapy exhibited greater reductions in QT parameters by day 8±2 compared to those without treatment. Additionally, anterior wall infarctions demonstrated significantly higher QT and QTc dispersions compared to inferior wall infarctions, with these differences being statistically significant. Conclusion: In the early stages of acute myocardial infarction, patients, especially those diagnosed with anterior myocardial infarction, exhibited significantly elevated QT and QTc dispersions. Thrombolytic treatment resulted in substantial reductions in these dispersions compared to untreated individuals. Typically peaking within the initial hours of the condition, these dispersions subsequently decline following successful thrombolysis, underscoring their significance in risk assessment for malignant ventricular tachyarrhythmias and reinforcing the efficacy of thrombolytic therapy

Prognostic value of synthetized right-side precordial leads in arrhythmogenic right ventricular cardiomyopathy: data from nordic, dutch, and canadian ARVC registers

Abstract Background T-wave inversion (TWI) in standard ECG leads is a proven risk marker for life-threatening ventricular arrhythmias (VA) in arrhythmogenic right ventricular cardiomyopathy (ARVC). Right-sided precordial leads (RSPL), which may be more sensitive for detection of the arrhythmogenic substrate, are rarely recorded and understudied in this context. Computerized ECG signal-processing allows synthesis of right-sided precordial ECG leads derived from standard 12-lead ECG. Objective We aimed to assess the value of TWI in RSPL, synthetized from the standard 12-lead ECG, for prediction of VA risk in ARVC patients with subtle ECG phenotypes. Methods Patients with definite ARVC by TFC2010 and no history of sustained VA prior to diagnosis from the Nordic, Canadian and Dutch ARVC registries were included. Patients with major repolarization criterion or complete right bundle branch block (RBBB) were excluded. RSPL (V3R-V6R) were derived from digitally recorded eight linearly independent leads (I, II and precordial V1 to V6) of the standard ECG using a linear ECG transformation matrix. The matrix was previously developed based on a library of body surface potential maps from a mixed group of healthy subjects and patients with cardiac diseases using multivariate linear regression and QRS-T data obtained from averaged beats of the right-sided-precordial leads and the basis leads. TWI exceeding 0.1 mV in any of the synthetized leads was tested for its ability to predict sustained VA, defined as either sustained ventricular tachycardia, appropriate ICD shock, aborted cardiac arrest, or sudden cardiac death. In a similar manner, the value of TWI in any of the inferior leads III, aVF or II was assessed. Hazard ratio was calculated using Cox regression analysis adjusted for age, sex, and proband status. Results In total, 144 patients without prior VA history were identified. After exclusion of patients with major repolarization criterion (n=37) and RBBB (n=11), 96 patients comprised the study group (mean age 40±16, 42% female, 41% probands, 48% ICD carriers). Fifty-nine patients (62%) had TWI in one or more synthetized RSPL and 33 patients (34%) had TWI in any of the inferior leads. During 10 years of follow-up, 11 patients (12%) developed VA. Kaplan-Meier curve analysis showed significant association between the presence of TWI in at least one of the RSPL and the risk of VA, which remained an independent predictor in the adjusted Cox regression analysis (HR 10.6, 95%CI 1.3-84.4, p=0.026). TWI in any of the inferior leads was not associated with the risk of VA in this cohort (HR 1.2 95%CI 0.26-5.42). Conclusions Synthetized right-sided precordial leads appear to contain information potentially useful for refining risk stratification of primary prevention ARVC patients with a subtle ECG phenotype, in whom traditional repolarization markers, such as major repolarization criterion or TWI in inferior leads, are not informative.

A smartphone based AI model to detect left ventricular systolic dysfunction on 12-lead ECG

Abstract Background Despite echocardiography being essential for cardiac assessment, a point-of-care tool for rapid left ventricular ejection fraction (LVEF) evaluation is lacking in current clinical practice, highlighting the need for a widescale, easily deployable method to assess LV function. Purpose To develop and validate two artificial intelligence (AI) models that accurately detect reduced LVEF on a single 12-lead ECG using a smartphone, facilitating early identification of patients at risk for heart failure who may benefit from more detailed echocardiographic evaluation. Methods A large collection of ECGs and transthoracic echocardiograms (TTEs) was sourced from our hospital data vault from 2011 to 2021. ECGs were paired to TTEs within a 24h time window and the resulting pairs were randomly assigned to the model development dataset (50%) and validation dataset (50%). Two distinct AI-ECG models were developed: one to detect LVEF≤40% and another for LVEF<50%, derived from the ESC definition of heart failure. Both AI-ECG models were paired with smartphone based ECG digitization technology. Key performance metrics include area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and F1-score. Results A total of 1,205,370 ECGs (198,274 patients) and 291,433 TTEs (105,849 patients) were collected and paired, resulting in 109,809 ECG-TTE pairs (56,236 patients). The validation dataset consisted of 25,510 distinct TTE-ECG pairs (25,510 patients). Prevalence of LVEF≤40% and LVEF<50% was 5.4% and 7.9% respectively. The LVEF≤40% model demonstrated an AUC of 0.963 (95% CI: 0.959-0.966), sensitivity 0.924 (95% CI: 0.91-0.937), specificity 0.887 (95% CI: 0.883-0.891), and F1-score of 0.474 (95% CI: 0.457-0.490). PPV and NPV were 0.318 (95% CI: 0.304-0.333) and 0.995 (95% CI: 0.994-0.996) respectively. Performance of the LVEF <50% model shows an AUC of 0.952 (95% CI: 0.947-0.956), with a slightly lower sensitivity of 0.899 (95% CI: 0.886-0.912), specificity of 0.875 (95% CI: 0.871-0.879), PPV of 0.382 (95% CI: 0.368-0.395), NPV of 0.99 (95% CI: 0.989-0.992), and F1-score of 0.536 (0.521-0.55). Conclusion The AI models exhibit the ability to identify reduced LVEF from standard 12-lead ECGs using a smartphone. Results suggest potential clinical utility as a preliminary screening tool to select patients for confirmatory echocardiography. This AI application could potentially offer a timely and cost-effective method for identifying patients at risk for future development of heart failure.

How does lead selection impact diagnosis through AI? Implications for continuous monitoring systems using convolutional neural networks

Abstract Background and Objectives Recently, there has been a surge in non-invasive diagnosing methods leveraging the dipolar 12-lead ECG information using Convolutional Neural Networks (CNNs). However, the traditional ECG contains 12 projections of the cardiac dipole in a three-dimensional space can result in redundancy. Furthermore, in continuous monitoring systems such as Holter and wearables, not all 12 leads are consistently available. This study aims to quantify redundancy and determine the best lead configuration for improved classification results using a CNN. It evaluates the level of redundancy in ECG leads and its impact on CNN classification. Methods The degree of redundancy was quantified by means of lead entropies (H) as depicted in Figure 1.A. The standard 12-lead ECG was chosen as the baseline input. From it, various subsets of leads were selected (6, 3, 1 leads) with the objective of attaining maximal orthogonality between leads while minimizing redundancy (Figure 1.B). A 2D-CNN model was trained for multiclass classification: Hypertension (HYP), ST-T interval Changes (STTC), Conduction Disturbances (CD), Myocardial Infarction (MI) and Normal (NORM). The designed inputs were employed to evaluate how redundancy impacts the performance of the CNN (see Figure 1.C). Results Reducing redundancy in input leads led to notable improvements in model prediction for all pathological conditions in terms of Area Under the Curve (AUC). Reducing to 6 and 8 leads were the most suitable input for the CNN (Table 1) increasing by 1.18% in HYP, 0.23% in STTC, 0.52% in CD, and 0.33% in MI. However, a significant reduction in redundancy may result in the loss of essential clinical information for classification, as observed in scenarios involving only 3 or 1 leads. Conclusions When diagnosing pathologies with systems featuring fewer leads, its combinations should aim for maximum orthogonality to reduce redundancy while preserving unique information. By strategically selecting a set of leads with reduced input redundancy, the effectiveness of the standard 12-lead ECG can be improved, thereby enhancing the utility of such continuous monitoring systems for automated diagnosis and also decreasing training and prediction times.

Orthostatic blood pressure changes and incident atrial fibrillation in patients with hypertension: Insights from the SPRINT trial

Abstract Background Exaggerated orthostatic changes in systolic blood pressure (SBP) were associated with adverse cardiovascular events. However, the predictive role of SBP hyperactivity to standing on incident atrial fibrillation (AF) is unclear. Purpose We aim to assess the association between orthostatic SBP changes and incident AF. Methods We performed a post hoc analysis of SPRINT (Systolic Blood Pressure Intervention Trial), a randomized controlled trial comparing intensive (<120 mm Hg) and standard (<140 mm Hg) SBP interventions in participants with hypertension. Participants with standing SBP <110 mm Hg were not eligible for randomization according to the SPRINT protocol. Participants without AF who had seated and standing SBP measurements at baseline and available baseline or follow-up ECG were included in this analysis. Orthostatic SBP changes were defined as standing SBP minus seated SBP. Patients were grouped into tertiles of orthostatic SBP changes. AF was ascertained from the standard 12-lead ECG obtained at baseline, year 2, year 4, a close-out visit, and any visit when suspected of serious adverse events. We used Cox proportional regression models and restricted spline curves to assess the association of orthostatic SBP changes with incident AF. Results Among 8,455 participants included in this analysis, 327 incident AF cases occurred during follow-up. The restricted spline curve depicted a U-shaped relationship between orthostatic SBP changes and incident AF in the unadjusted model (P for nonlinearity = 0.012) (Figure 1). After adjusting for age, female, race, smoking, alcohol use, history of cardiovascular disease, history of chronic kidney disease, and body mass index, a SBP increase ≥ 6 mm Hg to standing was independently associated with a 43% higher risk of incident AF (HR: 1.43; 95% CI: 1.07-1.90; P = 0.014) compared to nonsignificant orthostatic SBP changes (>-4 mm Hg to <6 mm Hg). A SBP decrease ≥ 4 mm Hg to standing showed a nonsignificant higher risk of developing AF compared to SBP changes of >-4 mm Hg to <6 mm Hg (Figure 2). In subgroup analysis, the results presented a similar tendency to the main result. Sensitivity analyses also generated consistent results while additionally adjusting for seated and standing blood pressure or heart rate. Conclusion In this post hoc analysis of the SPRINT trial, exaggerated SBP increase to standing independently predicts incident AF.Figure 1Figure 2

A novel 12-lead standard ECG derived scoring system for risk stratification in the emergency department

Abstract Background/Aims The ECG represents a routinely used diagnostic tool in the assessment of patients in the emergency department. Beside specific alterations within the ECG indicative of underlying pathologies, such as ST-elevation in myocardial infarction, several other parameters have been developed over the past decades that proofed themselves as strong and independent predictors of worsened outcome independent of a specific underlying pathology. These include basic metrics such as heart rate and QRS duration, but also more advanced biomarkers such as QRS microfragmentation (QRSµf) [1] and total cosine R to T (TCRT) [2] . In this work, we aimed to summarize all these markers derived from 12-lead standard ECGs into one novel risk score and evaluate its performance as mortality predictor in the emergency department. Methods We retrospectively collected 10- second 12- lead ECG recordings unselected patients presenting to the emergency department of a large university hospital between September 2019 and June 2022. For each patient a risk score was calculated from the first retrieved ECG using the criteria displayed in Figure 1. Calculation of all parameters was done as previously published. Patients with a total score of 0-1 points, 2-3 points, 4-6 points and 7 points were considered as low, intermediate, high and very high risk, respectively. The performance of the risk score was evaluated using Cox regression analysis and Kaplan-Meier curves with all-cause mortality within one year as primary endpoint. Results In total, 10 781 patients were included in our analyses. Median age was 69 (IQR 54-80) years, 5 588 (52%) were male and 5 193 (48%) were female. 6 966 (65%) patients were considered as low risk, 2 656 (25%) patients as intermediate risk, 1 098 (10%) patients as high risk and 61 (1%) patients as very high risk according to the ECG risk score with one year mortality rates of 8%, 17%, 26% and 43%, respectively (p < 0.001 for difference). In cox regression analysis the novel ECG risk score was a strong and independent predictor of one-year all-cause mortality with a hazard ratio of 1.35 (95% CI 1.31-1.39, p < 0.001). Kaplan-Meier survival curves for different risk categories are displayed in Figure 2. Conclusion In an unselected population of patients presenting to the emergency department of a large university hospital a novel risk score calculated from standard 12-lead ECGs recorded during clinical routine was a strong and independent mortality predictor. Further studies are needed to evaluate how incorporation of the novel score into existing triage systems can improve patient care.Figure 1:Calculation of ECG risk scoreFigure 2:Kaplan-Meier survival curves

Use of artificial intelligence-powered ECG to differentiate between cardiac and pulmonary pathologies in patients with acute dyspnoea in the emergency department

BackgroundAcute dyspnoea is common in acute care settings. However, identifying the origin of dyspnoea in the emergency department (ED) is often challenging. We aimed to investigate whether our artificial intelligence...

Machine Learning Algorithm to Predict Atrial Fibrillation Using Serial 12-Lead ECGs Based on Left Atrial Remodeling.

We hypothesized that analysis of serial ECGs could predict new-onset atrial fibrillation (AF) more accurately than analysis of a single ECG by detecting the subtle cardiac remodeling that occurs immediately before AF occurrence. Our aim in this study was to compare the performance of 2 types of machine learning (ML) algorithms. Standard 12-lead ECGs of patients selected by cardiologists between January 2010 and May 2021 were used for ML model development. Two ML models (single ECG and serial ECG) were developed using a light gradient boosting machine-learning algorithm. Model performance was evaluated based on the area under the receiver operating characteristic curve, sensitivity, specificity, accuracy, and F1 score. We trained the ML models on 415 964 ECGs from 176 090 patients. When testing the 2 ML models using external validation data sets, the performance of the serial-ML model was significantly better than that of the single-ML model for predicting new-onset AF (single- versus serial-ML model: sensitivity 0.744 versus 0.810; specificity 0.742 versus 0.822; accuracy 0.743 versus 0.816; F1 score 0.743 versus 0.815; area under the receiver operating characteristic curve 0.812 versus 0.880; P<0.001). The Shapley Additive Explanations analysis ranked P-wave duration and amplitude among the top 10 ECG parameters. An ML model based on serial ECGs from an individual had greater ability to predict new-onset AF than the ML model based on a single ECG. P-wave morphologies were associated with future AF prediction.

Clinical Utility of Synthesized 18-Lead Electrocardiography.

Eighteen-lead electrocardiography (18-ECG) includes, in addition to those in standard 12-lead ECG (12-ECG), six additional chest leads: V7-V9 and V3RV5R. Leads V7-V9 require the patient to be in a lateral decubitus position for the electrodes to be attached to the back. Synthesized 18-ECG (syn18-ECG) is a method that only records 12-ECG and uses computational logic to record the posterior wall (V7-V9) and right-sided (V3R-V5R) leads. We review the clinical utility of syn18-ECG in conditions including acute coronary syndromes, arrhythmias, acute pulmonary embolism, and Duchenne muscular dystrophy. The syn18-ECG waveform correlates well with the actual 18-ECG waveform, indicating that syn18-ECG is an excellent substitute for 18-ECG, excluding negative T waves. ST elevation in leads V7-V9 has the effect of reducing missed acute coronary syndromes in the posterior wall. In cases of arrhythmia, syn18-ECG can accurately estimate the target site of radiofrequency catheter ablation using a simple algorithm. The use of additional leads in Duchenne muscular dystrophy is expected to provide new insights. To facilitate gaining more knowledge regarding diseases that have not yet been investigated, it is imperative that the cost of syn18-ECG is reduced in the future.

The Significance of Right-Sided Precordial ECG Leads (V3R and V4R) in Assessing Right Ventricular Dysfunction: ASingle Center Cross-Sectional Study.

Right ventricular systolic dysfunction is associated with poor prognosis and increased mortality rates. Our objective was to investigate ECG changes in patients with this condition, focusing on the right-sided precordial leads. In this cross-sectional study, 60 patients with right ventricular dysfunction were included from April 2020 to April 2021. Cardiac structure and function were assessed using 2D transthoracic echocardiography. Standard 12-lead electrocardiograms and right-sided precordial ECGs (V3R-V4R) were obtained and analyzed for QRS complex configuration, ST-segment elevation, and T-wave morphology. In our study, the majority were male (70.0%) with a mean age of 58.76 years. The most common initial diagnoses were pulmonary thromboembolism (43.3%), chronic obstructive pulmonary disease (26.7%), and pulmonary hypertension (25.0%). The predominant ECG finding in the right-sided precordial leads (V3R, V4R) was a deep negative T wave (90.0%). Patients with severe right ventricular systolic dysfunction often exhibited a qR pattern (41.2%), whereas those with nonsevere dysfunction showed rS and QS patterns (55.8%). Approximately 41.0% of severe RV dysfunction cases had ST segment depression in the right-sided precordial leads, and 28.0% of patients displayed signs of right atrial abnormality. The study found that qR, rS, and QS patterns were more prevalent in V3R and V4R leads among patients with severe and nonsevere right ventricular systolic dysfunction. The most common ECG feature observed was deep T-wave inversion in these leads. The study recommends using right-sided precordial leads in all patients with RV systolic dysfunction for early detection and risk stratification.

Handheld 6-Lead ECG for Early Detection of Acute Inferior Wall ST-T Segment Elevation Myocardial Infarction: HINT-MI Study Design and Rationale.

Background: ST-T segment elevation myocardial infarction (STEMI) is a critical condition that requires rapid diagnosis and treatment. Recently, various ECG recording devices have been developed. In this study, we aim to determine the utility of a 6-lead handheld ECG recording device to shorten the time taken for the diagnosis of inferior wall STEMI. Methods and Design: HINT-MI is an investigator-derived, observational, prospective study that will evaluate the ability of a handheld 6-lead ECG device to diagnose acute inferior wall STEMI. Patients who have undergone coronary angiography for STEMI or for other reasons will be enrolled in the study. This study aims to evaluate sensitivity and specificity of a handheld 6-lead ECG device by the level of agreement with a standard 12-lead ECG for diagnosing inferior wall STEMI. Further, we will determine whether the use of the handheld device can reduce the time needed for reperfusion treatment through faster diagnosis. Conclusions: This study aims to investigate the feasibility of a handheld 6-lead ECG device for diagnosing inferior wall STEMI to reduce the time required to diagnose inferior wall STEMI and to allow timely treatment.

Performance of a vectorcardiographic deep learning algorithm compared to single-lead and 12-lead ECG for atrial flutter detection: implications for wearable devices

Abstract Background Wearable devices have developed the capacity to offer single-lead ECG analysis and are already widespread in use, particularly for detection of atrial fibrillation (1,2). Vectorcardiography entails the transformation of electrical vectors into X, Y, and Z axes to constitute a vectorcardiogram (VCG) (3). The VCG has diagnostic promise given the capacity to generate it without requiring clinical presentation for standard 12-lead ECG acquisition. Furthermore, a novel credit-card sized device capable of acquiring a VCG has recently been developed – raising the potential for arrhythmia detection and discrimination beyond wearable single-lead ECG systems. Objective To assess the performance of a deep learning algorithm trained on single lead and 12-lead ECGs and VCGs for detecting atrial flutter. Methods A convolutional neural network (AFL-CNN) was developed on a dataset of 4,000 ECGs and VCGs from a single US academic medical institution (80% training and 20% for testing). Validation was performed on a separate publicly available dataset (2223 ECGs). Model performance was assessed on single lead ECG analysis (lead I, obtained by removing the other leads) and standard 12-lead ECG. The gold standard for AFL diagnosis was physician-confirmed 12-lead ECG diagnoses in the dataset. The VCG was generated from the 12-lead ECG via Kors transformation matrix to which AFL-CNN was also applied. Model performance was assessed with sensitivity, specificity, positive and negative predictive values, accuracy, and F1 statistic. Results On single lead ECG analysis, AFL-CNN achieved 71.2% sensitivity and 96.9% specificity with PPV 84.0% and NPV 93.7% and 92.2% accuracy with F1 83.2%. When AFL-CNN was applied to the VCG, performance improved: 91.0% sensitivity and 98.7% specificity, PPV 94.24%, NPV 98.0%, 97.3% accuracy, and F1 95.31%. Lastly, the performance remained high when AFL-CNN was applied to the 12-lead ECG: 95.4% sensitivity, 98.63% specificity, PPV 94.0%, NPV 99.0%, and accuracy of 98.0%, with F1 97.2%. Conclusion A deep learning algorithm applied to vectorcardiography for the detection of atrial flutter showed improved performance over single lead analysis and comparable performance to standard 12-lead applications. The finding of improved model performance beyond single lead ECG tracings underscores both the limitations of current wearable technologies in arrhythmia detection beyond atrial fibrillation and the potential for VCG-based algorithm prediction to fill gaps in healthcare inequality when standard 12-lead acquisition is challenging to obtain.

Potential determinants of nocturnal hypoxemic burden and its association with atrial fibrillation recurrence after catheter ablation

Abstract Background Sleep-disordered breathing (SDB) is present in approximately half of patients with atrial fibrillation (AF) scheduled for catheter ablation (CA) and coincides with an elevated nocturnal hypoxemia. Purpose To evaluate determinants of nocturnal hypoxemic burden metrics and its association with AF recurrence after CA in patients undergoing a systematic SDB management and treatment pathway. Methods Consecutive AF patients scheduled for CA without history of SDB and/or SDB screening were included. Patients were referred to a virtual SDB management pathway including mobile health supported overnight home sleep test. An apnoea-hypopnoea index (AHI) of 5 to &amp;lt;15 and ≥ 15 were interpreted as mild and moderate-to-severe SDB, respectively. Oximetry data from home sleep tests were analyzed by a computer algorithm to evaluate metrics of nocturnal hypoxemic burden: time spent below 90% oxygen desaturation (T90) based on acute desaturations (T90 desat) or non-specific drifts in oxygen desaturation (T90 non-spec). Patients were monitored for AF recurrences through standard 12-lead ECG or Holter ECG and/or smartphone-based photophletysmography (PPG) performed at 3- and 12-month follow up. Multiple regression analysis was employed to (1) evaluate determinants of nocturnal hypoxemic burden metrics and (2) identify predictors of AF recurrence during 12 months after CA (excluding 3 months blanking period). Results The analysis included 210 patients (42% female, mean age 64± 9 years). Median AHI was 14.7 [6.7-24.7], median T90desat was 0.25 [0.03-1.42] minutes, T90non-spec 0.26 [0.08-0.78] minutes. Overall, 101 (48%) patients were diagnosed with moderate-to-severe SDB and 68 (32%) had mild SDB. Of patients with moderate-to- severe SDB and mild SDB, 87% and 46%, respectively, received a SDB therapy recommendation. Multiple linear regression analysis identified diabetes mellitus (B 8.611 95%CI 1.350-15.871, p=0.020) and AHI ≥ 15 (B 5.886, 95%CI 1.793-9.979, p= 0.005) as predictors of T90non-spec. Thyroid dysfunction (B 1.849, 95%CI 0.623-3.075, p=0.003) and AHI ≥ 15 (B 2.161, 95%CI 1.320-3.001, p&amp;lt;0.001) were significant predictors of T90desat. During 12 months after CA, 65 (31%) patients had experienced at least one AF recurrence after the blanking period. After multiple Cox-regression, age (HR 1.032, 95%CI 1.002-1.063, p= 0.035) and BMI ≥ 25 (HR 2.029, 95%CI 1.032-3.989, p=0.040) remained predictors for AF recurrences. Conclusions In AF patients scheduled for CA, SDB (AHI ≥ 15) and thyroid dysfunction were predictors for nocturnal hypoxemic burden with repetitive desaturations (T90desat). SDB and diabetes mellitus were predictors for nocturnal hypoxemic burden without repetitive desaturations (T90non-spec). In patients with AF who followed a virtual SDB management and treatment pathway, SDB presence and nocturnal hypoxemic burden metrics did not emerge as predictors, whereas age and BMI (≥25) remained significant predictors for AF recurrence.

Additional factors associated with sudden cardiac death in HCM patients

Abstract Background Hypertrophic cardiomyopathy (HCM) is a primary myocardial disease characterized by an increase in the thickness of the left ventricular (LV) wall, which is not explained only by abnormal loading conditions. Sudden cardiac death (SCD) is the most devastating complication of HCM occurring in asymptomatic or younger patients without warning. Purpose To evaluate additional SCD risk factors in Ukrainian population of HCM patients. Materials and methods We retrospectively evaluated 350 consecutive HCM patients that were followed from 2006 to 2021 in our Cardiological Department. General clinical examination, standard 12-lead ECG, transthoracic echocardiography and 24 hours Holter ECG monitoring data were estimated. The 5-year SCD risk was calculated according to the HCM Risk-SCD score. The endpoint of the study was SCD and its surrogates, including adequate performance of the implantable cardioverter-defibrillator and successful resuscitation. Results Follow-up time was 5.0 (1.5-9.5) years. During this time, 16 patients (4.6%) reached the end point, which was 0.9%/year. According to the HCM Risk-SCD score, 5 (31.3%) of this group had a high risk of SCD (≥ 6%) and 3 (18.8%) had an intermediate risk (≥ 4% and &amp;lt; 6%). In multivariable Cox regression analysis adjusted for conventional SCD risk factors it was found that independent predictors of SCD were unexplained syncope (HR 3.81, 95% CI 1.11-13.12, p=0.034), systolic blood pressure (SBP) (HR 0.97 , 95% CI 0.94- 0.99, p=0.026), "infarct-like" ST elevation on ECG (HR 6.81, 95% CI 2.09-22.16, p=0.001) and PQ interval duration (HR 1.03, 95% CI 1.01-1.05, p=0.002), Harrell's C-index 0.84, 95% CI 0.73-0.95, p &amp;lt; 0.0001). Conclusions In our study HCM Risk-SCD score predicted SCD or surrogates only in 50% of cases. In order to improve the risk stratification some additional independent predictors of SCD can be proposed. ECG parameters ("infarct-like" ST elevation and PQ interval duration) and level of SBP were found to have SCD predictive value.

High-resolution repolarization changes preceding short-coupled ectopy in idiopathic ventricular fibrillation, and effects of quinidine

Abstract Background Idiopathic ventricular fibrillation (VF) is linked to early repolarization, increased repolarization heterogeneity, and (short-coupled) premature ventricular complexes (PVCs). Quinidine, an antiarrhythmic drug with class-IA and -III effects, reduces the occurrence of PVCs and VF, but its effects on repolarization have not yet been studied at the whole-heart level in idiopathic-VF patients. Purpose To study dynamic changes in repolarization preceding short-coupled PVCs using noninvasive electrocardiographic imaging (ECGI) in a patient with idiopathic VF, and the antiarrhythmic effects of quinidine. Methods ECGI was performed during sinus rhythm in a 61-year-old male with an ICD (secondary prevention; 2.5 mg nebivolol daily) two days after recurrent idiopathic VF (Fig. A). This was done 1) at baseline, 2 minutes before short-coupled ectopy ("baseline"); 2) at baseline, 10 seconds before the short-coupled PVC ("pre PVC"); and 3) at 2 hours after start of quinidine 200 mg ("on-quinidine"). The ECGI procedure consisted of a 224-electrode body surface potential map and a contrast-enhanced CT scan to obtain torso and heart geometries. After reconstruction of epicardial unipolar electrograms (UEGs), local activation and repolarization times (ATs and RTs) were derived from UEGs by assessing the steepest downslope of the QRS complex and steepest upslope of the T waves, respectively, of 10-second signal-averaged beats. AT and RT duration (first to last AT or RT) were assessed. Results At baseline, QRS duration and QT interval measured 107 ms and 343 ms, respectively, on the standard 12-lead ECG. ECGI-derived epicardial AT duration was 72 ms, and RT histogram span 92 ms. The coupling interval of the PVC that triggered VF in the ICD readout was 340 ms (Fig. A). During 44 minutes of ECGI, 157 short-coupled PVCs from the moderator-band region were recorded (Fig. B; no tachycardia or VF). Just prior to a PVC, epicardial early repolarization at the PVC origin decreased by 26 ms (star in Figs. C-E), and the RT histogram broadened to 109 ms (Fig. E). While on-quinidine, (25 minutes; QRS duration 107 ms, QT interval 419 ms), epicardial repolarization was prolonged (Fig. C/D), also at the site of abnormal impulse formation. Quinidine had increased the epicardial AT duration to 79 ms, and RT histogram span to 133 ms. PVCs were completely suppressed. The patient remained free of sustained arrhythmias while on-quinidine. Conclusion(s) In this case of short-coupled idiopathic VF, epicardial early repolarization shortened momentarily prior to abnormal impulse formation. We found dynamic substrate-trigger characteristics that likely facilitate reentrant excitation. Quinidine prolonged the duration of epicardial activation and repolarization, suppressing the ectopy and VF recurrence.

CineECG analysis of body surface potential mapping performed in preclinical and clinical plakophilin-2-variant carriers

Abstract Background/introduction Pathogenic variation of the plakophilin-2 (PKP2) gene is associated with arrhythmogenic cardiomyopathy (ACM). Disease manifestation in PKP2-pathogenic variant (PV) carriers differs greatly. Early detection of disease is crucial and could be of benefit in the risk stratification of this population. 67-lead body surface potential mapping (BSPM) could offer more insight into subtle electrical abnormalities. CineECG is a novel tool which can provide an estimation of the average pathway of ventricular activation based on the BSPM-signals, and could be of added value in the detection of electrical abnormalities. Purpose This study aimed to analyze 67-lead BSPM data with CineECG to ascertain the electrical activation pathway in PKP2-PV carriers and to compare these to the normal CineECG. Methods Ten PKP2-PV carriers underwent 67-lead BSPM. Six carriers were without definite ACM diagnosis (preclinical); no Task Force Criteria (TFC) were met apart from the identification of the pathogenic PKP2-variant. The other four carriers were all diagnosed with definite ACM (symptomatic). The average location and direction of ventricular activation was determined with CineECG, resulting in a CineECG trajectory throughout the ventricles. These results were compared to the normal CineECG trajectory, which was established in a previous study with normal ECG’s from the PTB-XL database. A CineECG trajectory that deviated more than 15% from the normal trajectory was considered abnormal. Additionally, to further determine the added diagnostic value of BSPM and CineECG beyond the standard 12-lead ECG, the 12-lead ECG’s were evaluated for abnormalities by three different ECG-experts. Results Expert evaluation of the 12-lead ECG showed that all preclinical PKP2-PV carriers had a normal ECG (no TFC and no other abnormalities such as abnormal electrical heart axis, pathological Q-waves or low voltage). In the symptomatic carriers, all ECG’s showed either presence of TFC or conduction abnormalities. Of the ten PKP2-PV carriers, eight exhibited an abnormal CineECG trajectory. In the preclinical group, four out of six carriers had an abnormal activation trajectory, while all symptomatic carriers had an abnormal CineECG trajectory. Conclusions Through CineECG analysis of 67-lead BSPM, abnormal ventricular activation was identified in the majority of PKP2-PV carriers. Notably, abnormal activation was observed in four out of six preclinical PKP2-PV carriers while the standard 12-lead ECG showed no abnormalities. Future research in a larger group, which also includes follow-up data, is warranted to determine whether these results are an indication of early signs of disease.Figure 1

Multicenter hemodynamic assessment of the LOT-CRT strategy: when is one lead enough for resynchronization?

Abstract Background Left bundle branch area pacing (LBBAP) has been postulated as an alternative to traditional cardiac resynchronization therapy via biventricular pacing (BiV) in heart failure patients with wide QRS. Limited data is available regarding the acute hemodynamic response of LBBAP and the combination of LBBAP and coronary venous (LVcv) pacing (LOT-CRT). Purpose We sought to compare the acute hemodynamic and ECG effects of traditional CRT (BiV or LV-only) versus LBBAP and LOT-CRT in CRT candidates with advanced conduction disease. Methods In this multi-centre study (5 EU and 7 US centres), 48 subjects (mean age 65.8 years, 67% males, LV ejection fraction 29.4%) with either nonspecific interventricular conduction delay (NIVCD, n = 29) or left bundle branch block (LBBB; n =19; based upon strict criteria AHA/ACC/HRS plus Strauss) underwent an acute hemodynamic study to determine the % change in +LV dP/dtmax from baseline atrial pacing versus BiV, LBBAP, or LOT-CRT. Each therapy pacing configuration was applied 4 times at 5 different atrioventricular (AV) delays. QRS shortening was assessed using standard 12-lead ECG. Results Successful LBBAP lead implantation was achieved in 27 patients (56%) using current EHRA criteria. Twenty-one patients (44%) were categorized as deep septal pacing (DSP), due to suboptimal lead penetration or advanced left conduction system disease with broad paced QRS complexes and absent V1-r-wave. The increase in AV-optimized dP/dtmax for the different pacing configurations is displayed in the Figure, left panel. LOT-CRT dP/dtmax % improvement was 25.8%, significantly higher than LBBAP configurations (p&amp;lt;0.01), either unipolar-LBBAP (19.3%), or bipolar-LBBAP (16.4%), but not different from BiV (26.4%). QRS shortening was greater in LOT-CRT (29.5ms) vs. unipolar-LBBAP (11.9ms), bipolar-LBBAP (11.7ms), or BiV (18.5ms), all p&amp;lt;0.01. Sub-group analysis indicated the hemodynamic benefit of adding LVcv pacing when LBBAP strategy was pursued (Figure, right panel) depended on obtaining LBBAP rather than DSP (p &amp;lt; 0.01) and baseline QRS duration (p = 0.03). The largest benefit was observed among subjects with a baseline QRS wider than the observed cohort mean (171ms) and DSP only, where LOT-CRT provided 14.5% (5.0-24.1%) greater dP/dtmax improvement and 20.8ms (12.8-28.8ms) greater QRS duration shortening than unipolar DSP. Conclusions In a CRT cohort with advanced conduction disease, LOT-CRT provided greater acute hemodynamic benefit than LBBAP strategy (i.e. including also DSP cases). Patients with wider QRS or suboptimal outcome of LBBAP strategy (DSP) are more likely to benefit from addition of a LVcv lead to implement LOT-CRT.Figure 1

Recognition of Brugada type 1 pattern on ECG by Echo State Networks

Abstract Background/Introduction The development of artificial intelligence (AI) and machine learning (ML) techniques, which can identify data patterns in electrocardiography, has led to the development of automated analysis tools for standard 12-lead ECGs. These tools are able to identify specific features associated with different cardiac diseases, which may be helpful in clinical practice [1]. One field of application is represented by Brugada Syndrome (BrS) diagnosis, which, despite characteristic ECG features, can be challenging to diagnose and correctly distinguish from other diseases. Purpose Within the Brugada Syndrome and Artificial Intelligence Applications to Diagnosis system study, we are developing an innovative model for diagnosing Type 1 BrS based on electrocardiographic pattern recognition through the Echo State Networks (ESN), a Recurrent Neural Network (RNN) type of architecture. Methods From patients enrolled in 5 Centers, 306 12-lead ECGs were obtained. The datasets were composed of spontaneous Type 1 pattern (coved) (group A, 98 patients); patients undergoing ajmaline test, classified as positive (group B, 47 patients) or negative (group C, 35 patients) according to test results; 220 controls with no clinical and familial history of arrhythmias (group D). ESN analyzed, as input, data collected from 1 beat of the ECG V2 lead. The datasets were used for the ESN model training and were assessed through a double cross-validation approach. The ESN performance was compared with the evaluation of 4 independent cardiologists, and sensitivity, specificity and accuracy were determined. Results The ESN model correctly classified 280/306 ECGs (91.5%), with a performance of 3.6 points superior to clinicians (269/306, 88.0%). ESN sensitivity (87.0% vs. 86.9%) and specificity (94.5% vs. 89.1%) are described in Table 1; accuracy in each group of the dataset (groups A, B, C, and D), represented in Table 2, were comparable to or greater than those of clinicians. Conclusion(s) In this work, an ESN-based system for diagnosing Type 1 BrS pattern on ECG was developed. Our results show that this ML model can detect ECG patterns associated with Type 1 BrS with accuracy, sensitivity and specificity comparable or superior to clinicians. In the future, we will use a larger dataset to increase the model performance further, with the real possibility of becoming a tool for everyday clinical practice.ESN and clinicians' diagnostic accuracyECG accuracy in each dataset group