Arrhythmic Risk Stratification Research Articles

Abstract 4137272: The Impact of Genotype in Dilated and Arrhythmogenic Cardiomyopathy: Insights from the SHaRE Registry

Background: The impact of genotype on the risk of arrhythmia and heart failure in dilated and arrhythmogenic cardiomyopathy (DCM/ACM) is not well defined. Better understanding of genetic risk factors is needed to improve patient risk stratification, management, and outcomes. Methods: In this observational study, we leveraged the international Sarcomeric Human Cardiomyopathy Registry (SHaRe) to analyze the effect of arrhythmic gene variants on clinical events in patients with DCM/ACM (excluding arrhythmogenic right ventricular cardiomyopathy). Patients were stratified by the presence of pathogenic variants in 8 genes most commonly associated with ACM (AR(+): LMNA , DSP , FLNC , RBM20 , PLN , SCN5A , DES , and TMEM43 ). We assessed for risk of clinical events including ventricular arrhythmia (VA), atrial fibrillation (AF), heart failure (HF), death, and the overall composite of any of these events. Analyses were adjusted for age at diagnosis, sex, and baseline left ventricular ejection fraction (EF). Patients were also stratified by EF ≥50% in subgroup analyses. Results: A total of 1373 genotyped adults with DCM/ACM from 6 centers were included with median follow-up of 5.5 (IQR 2.4-10.4) years. Mean diagnosis age was 48 (± 15) years, and 39.5% were female. The cohort included 185 (13.5%) AR(+) patients. AR(+) compared to genotype-negative patients (N=486, 35.4%) had increased risk of VA (HR 5.02, 95% CI 3.46-7.29) and AF (HR 3.02, 95% CI 2.20-4.10). Risk of HF (HR 2.32, 95% CI 1.55-3.49) and the overall composite (HR 2.12, 95% CI 1.68-2.67) were also increased ( Figure ). Risk of each event remained significant after adjusting for age, sex, and baseline EF ( Table ). AR(+) patients with EF ≥50% also had increased risk of VA (HR 4.56, 95% CI 2.16-9.63), AF (HR 2.94, 95% CI 1.72-5.03), and the overall composite (HR 2.15, 95% CI 1.41-3.28). Conclusions: Genetic testing enables powerful risk stratification for both arrhythmias and heart failure in DCM/ACM independent of age, sex, or EF.

Machine Learning-Based Clustering Using a 12-Lead Electrocardiogram in Patients With a Implantable Cardioverter Defibrillator to Identify Future Ventricular Arrhythmia.

Implantable cardioverter defibrillators (ICDs) reduce mortality associated with ventricular arrhythmia in high-risk patients with cardiovascular disease. Machine learning (ML) approaches are promising tools in arrhythmia research; however, their application in predicting ventricular arrhythmias in patients with ICDs remains unexplored. We aimed to predict and stratify ventricular arrhythmias requiring ICD therapy using 12-lead electrocardiograms (ECGs) in patients with an ICD. This retrospective analysis included 200 adult patients who underwent ICD implantation at a single center. Patient demographics, clinical features, and 12-lead ECG data were collected. Unsupervised learning techniques, including K-means and hierarchical clustering, were used to stratify patients based on 12-lead ECG features. Dimensionality reduction methods were also used to optimize clustering accuracy. The silhouette coefficient was used to determine the optimal method and number of clusters. Of the 200 patients, 59 (29.5%) received appropriate therapy. The mean age of patients was 62.3 years, and 81.0% were male. The mean follow-up period was 2,953 days, with no significant intergroup differences. Hierarchical clustering into 3 clusters proved to be the most accurate (silhouette coefficient=0.585). Kaplan-Meier curves for these 3 clusters revealed significant differences (P=0.026). We highlight the potential of ML-based clustering using 12-lead ECGs to help in the risk stratification of ventricular arrhythmia. Future research in a larger multicenter setting may provide further insights and refine ICD indications.

Relationship between activity and sleep, as measured through a wearable accelerometer, and appropriate cardioverter defibrillator interventions: a prospective SafeHeart substudy.

Physical activity has shown association with ventricular arrhythmia, however, the role of specific behavioral patterns over a 24 h cycle remains unknown. Therefore, we aimed to explore associations between physical behavior and appropriate implantable cardioverter defibrillator (ICD) therapy. We included patients with an ICD at two European sites, who wore wrist-based accelerometers capturing 24 h movement and sleep behaviours for 28 days. Behavioural measures included activity volume, duration and intensity, sleep duration, and efficiency. Participants were followed for 12 months for the outcome of appropriate ICD therapy. Cox proportional hazard models with restricted cubic splines were used for the analysis. Lastly, the predictive capacity was tested. A total of 253 ICD patients were included (mean age 63.5 (±10.2), 48 (19.0%) female). During follow-up, 40 participants (15.8%) received appropriate ICD therapy; 32 anti-tachycardia pacing (ATP) only (12.6%), 5 shock only (2.0%), and 3 combined ATP and shock (1.2%). In the adjusted model, high inactive duration (HR 1.40 (95% 1.10-1.78)), peak walking cadence (HR 1.07 (95% 1.03-1.12)), and total sleep duration (HR 1.50 (1.02-2.22)) were associated with the outcome. The dose-response relationship was U-shaped for inactive duration with a cut-off at 16 h, and linear for peak cadence and sleep. The prediction model reached an area under the receiver operating characteristic curve of 0.70 ± 0.03, with highest accuracy in the first months. Wearable-derived 24 h movement and sleep behaviours collected over 28 days were associated with later appropriate ICD therapy risk. Testing of the predictive value of digital biomarkers for enhanced risk stratification of ventricular arrhythmia warrants larger prospective studies. National Trial Registration (NL9218, http://onderzoekmetmensen.nl/).

Abstract Mo082: Polygenic Assessment for Atrial Fibrillation Liability Predicts Ventricular Arrhythmia Risk

Background: The risks for atrial and ventricular arrhythmias are increased with cardiomyopathy. Currently, left ventricular dysfunction is the major determinate of implantable cardioverter-defibrillator (ICD) placement despite left ventricular ejection fraction only having an odds ratio of 2.9 for subjects with an LVEF <35% with sensitivity and specificity that suggests many patients are inappropriately receiving or not receiving a device. Additional testable variables would aid in arrhythmia risk stratification. There is growing evidence that atrial dysfunction is both a cause and consequence of ventricular disease. Atrial fibrillation (AFib) is the most common atrial arrhythmia and is highly heritable suggesting a strong genetic basis, and AFib occurs at increased frequency with cardiomyopathies. Genome-wide association studies (GWAS) identify >100 loci that contribute to AFib, and signals from GWAS can be used to develop polygenic risk scores (PRS). Subjects in the top 3% of one AFib PRS were found to have ~2 fold greater risk for AFib (OR 1.7-4.6). Hypothesis: The genetic pathways represented in an AFib PRS account for a portion of arrhythmia risk associated with cardiomyopathy and can be exploited to predict ventricular arrhythmia risk. Approach: As part of an AHA Arrhythmia and Sudden Death Strategically Funded Research Network, we assembled a cohort with varying risk for sudden cardiac death ranging from low-risk individuals with non-ischemic cardiomyopathy without arrhythmia to higher risk individuals who underwent ICD placement for aborted sudden death and sudden death. Whole genome sequencing was completed 1043 individuals across the arrhythmia risk spectrum. Results: We examined an AFib PRS to define ventricular outcomes in the cohort. Of the 440 participants with disease, 81% had cardiomyopathy, 8% channelopathy and 7% idiopathic arrest. We found the AFib PRS stratified subjects without significant ventricular arrhythmia risk from subjects with known ventricular arrhythmia (p=0.03), indicating that the AFib PRS includes components that are important markers of ventricular dysfunction. Conclusions: The AFib PRS is a useful tool to stratify ventricular arrhythmia risk.

Arrhythmic Risk Stratification by Cardiovascular Magnetic Resonance Imaging in Patients With Nonischemic Cardiomyopathy

BackgroundMyocardial fibrosis (MF) forms part of the arrhythmic substrate for ventricular arrhythmias (VAs). ObjectivesThis study sought to determine whether total myocardial fibrosis (TF) and gray zone fibrosis (GZF), assessed using cardiovascular magnetic resonance, are better than left ventricular ejection fraction (LVEF) in predicting ventricular arrhythmias in patients with nonischemic cardiomyopathy (NICM). MethodsPatients with NICM in a derivation cohort (n = 866) and a validation cohort (n = 848) underwent quantification of TF and GZF. The primary composite endpoint was sudden cardiac death or VAs (ventricular fibrillation or ventricular tachycardia). ResultsThe primary endpoint was met by 52 of 866 (6.0%) patients in the derivation cohort (median follow-up: 7.5 years; Q1-Q3: 5.2-9.3 years). In competing-risks analyses, MF on visual assessment (MFVA) predicted the primary endpoint (HR: 5.83; 95% CI: 3.15-10.8). Quantified MF measures permitted categorization into 3 risk groups: a TF of >0 g and ≤10 g was associated with an intermediate risk (HR: 4.03; 95% CI: 1.99-8.16), and a TF of >10 g was associated with the highest risk (HR: 9.17; 95% CI: 4.64-18.1) compared to patients with no MFVA (lowest risk). Similar trends were observed in the validation cohort. Categorization into these 3 risk groups was achievable using TF or GZF in combination or in isolation. In contrast, LVEF of <35% was a poor predictor of the primary endpoint (validation cohort HR: 1.99; 95% CI: 0.99-4.01). ConclusionsMFVA is a strong predictor of sudden cardiac death and VAs in NICM. TF and GZF mass added incremental value to MFVA. In contrast, LVEF was a poor discriminator of arrhythmic risk.

Molecular genetics in 1991 arrhythmia probands and 2782 relatives in Norway: Results from 17 years of genetic testing in a national laboratory.

The aim of this study was to explore the prevalence of likely pathogenic or pathogenic variants and assess the diagnostic yield from genetic testing for cardiac arrhythmias in Norway since 2003. Data from 1991 probands and 2782 relatives were retrospectively collected from the laboratory information management system at Unit for Cardiac and Cardiovascular Genetics, Oslo University hospital. Of 1991 probands, 57.4% were females, age at genetic testing was 33.1 (±22.7) years, and 32.5% were under the age of 18. A likely pathogenic or pathogenic variant (including 14 novel) was detected in 15.4% in total. Of the 2782 relatives, 53.7% were females, age at genetic testing was 35.6 (±22.5) years, 27.3% were under the age of 18, and 45.3% carried the family variant. Probands and relatives combined, 1/3356 persons in the Norwegian population were heterozygous for an arrhythmia-causing variant. The founder variant p.Q530X (NM_000218.2: c.1588C>T) in KCNQ1 accounted for 34% of all variants in Norway. In conclusion, genetic testing provided a genetic basis of the arrhythmia in 15.4% of the probands. Familial cascade screening identified four times as many variant-positive relatives, allowing early detection and prompt stratification of arrhythmic risk of those variant carriers.

A novel tool for arrhythmic risk stratification in desmoplakin gene variant carriers.

Pathogenic desmoplakin (DSP) gene variants are associated with the development of a distinct form of arrhythmogenic cardiomyopathy known as DSP cardiomyopathy. Patients harbouring these variants are at high risk for sustained ventricular arrhythmia (VA), but existing tools for individualized arrhythmic risk assessment have proven unreliable in this population. Patients from the multi-national DSP-ERADOS (Desmoplakin SPecific Effort for a RAre Disease Outcome Study) Network patient registry who had pathogenic or likely pathogenic DSP variants and no sustained VA prior to enrolment were followed longitudinally for the development of first sustained VA event. Clinically guided, step-wise Cox regression analysis was used to develop a novel clinical tool predicting the development of incident VA. Model performance was assessed by c-statistic in both the model development cohort (n = 385) and in an external validation cohort (n = 86). In total, 471 DSP patients [mean age 37.8 years, 65.6% women, 38.6% probands, 26% with left ventricular ejection fraction (LVEF) < 50%] were followed for a median of 4.0 (interquartile range: 1.6-7.3) years; 71 experienced first sustained VA events {2.6% [95% confidence interval (CI): 2.0, 3.5] events/year}. Within the development cohort, five readily available clinical parameters were identified as independent predictors of VA and included in a novel DSP risk score: female sex [hazard ratio (HR) 1.9 (95% CI: 1.1-3.4)], history of non-sustained ventricular tachycardia [HR 1.7 (95% CI: 1.1-2.8)], natural logarithm of 24-h premature ventricular contraction burden [HR 1.3 (95% CI: 1.1-1.4)], LVEF < 50% [HR 1.5 (95% CI: .95-2.5)], and presence of moderate to severe right ventricular systolic dysfunction [HR 6.0 (95% CI: 2.9-12.5)]. The model demonstrated good risk discrimination within both the development [c-statistic .782 (95% CI: .77-.80)] and external validation [c-statistic .791 (95% CI: .75-.83)] cohorts. The negative predictive value for DSP patients in the external validation cohort deemed to be at low risk for VA (<5% at 5 years; n = 26) was 100%. The DSP risk score is a novel model that leverages readily available clinical parameters to provide individualized VA risk assessment for DSP patients. This tool may help guide decision-making for primary prevention implantable cardioverter-defibrillator placement in this high-risk population and supports a gene-first risk stratification approach.

Arrhythmic Risk Stratification in Cardiac Amyloidosis: A Review of the Current Literature.

Cardiac amyloidosis is the most frequent infiltrative disease caused by the deposition of misfolded proteins in the cardiac tissue, leading to heart failure, brady- and tachyarrhythmia and death. Conduction disorders, atrial fibrillation (AF) and ventricular arrhythmia (VA) significantly impact patient outcomes and demand recognition. However, several issues remain unresolved regarding early diagnosis and optimal management. Extreme bradycardia is the most common cause of arrhythmic death, while fast and sustained VAs can be found even in the early phases of the disease. Risk stratification and the prevention of sudden cardiac death are therefore to be considered in these patients, although the time for defibrillator implantation is still a subject of debate. Moreover, atrial impairment due to amyloid fibrils is associated with an increased risk of AF resistant to antiarrhythmic therapy, as well as recurrent thromboembolic events despite adequate anticoagulation. In the last few years, the aging of the population and progressive improvements in imaging methods have led to increases in the diagnosis of cardiac amyloidosis. Novel therapies have been developed to improve patients' functional status, quality of life and mortality, without data regarding their effect on arrhythmia prevention. In this review, we consider the latest evidence regarding the arrhythmic risk stratification of cardiac amyloidosis, as well as the available therapeutic strategies.

Missense and Non-Missense Lamin A/C Gene Mutations Are Similarly Associated with Major Arrhythmic Cardiac Events: A 20-Year Single-Centre Experience.

Arrhythmic risk stratification in patients with Lamin A/C gene (LMNA)-related cardiomyopathy influences clinical decisions. An implantable cardioverter defibrillator (ICD) should be considered in patients with an estimated 5-year risk of malignant ventricular arrhythmia (MVA) of ≥10%. The risk prediction score for MVA includes non-missense LMNA mutations, despite their role as an established risk factor for sudden cardiac death (SCD) has been questioned in several studies. The purpose of this study is to investigate cardiac features and find gene-phenotype correlations that would contribute to the evidence on the prognostic implications of non-missense vs. missense mutations in a cohort of LMNA mutant patients. An observational, prospective study was conducted in which 54 patients positive for a Lamin A/C mutation were enrolled, and 20 probands (37%) were included. The median age at first clinical manifestation was 41 (IQR 19) years. The median follow-up was 8 years (IQR 8). The type of LMNA gene mutation was distributed as follows: missense in 26 patients (48%), non-frameshift insertions in 16 (30%), frameshift deletions in 5 (9%), and nonsense in 7 (13%). Among the missense mutation carriers, two (8%) died and four (15%) were admitted onto the heart transplant list or underwent transplantation, with a major adverse cardiovascular event (MACE) rate of 35%. No statistically significant differences in MACE prevalence were identified according to the missense and non-missense mutation groups (p value = 0.847). Our data shift the spotlight on this considerable topic and could suggest that some missense mutations may deserve attention regarding SCD risk stratification in real-world clinical settings.

Arrhythmic risk stratification in non-ischemic cardiomyopathy using cardiovascular magnetic resonance

Abstract Background Myocardial fibrosis (MF) is an essential component of the arrhythmic substrate for ventricular arrhythmias (VAs). Aims To determine whether total myocardial fibrosis (TF) and gray zone fibrosis (GZF), assessed using cardiovascular magnetic resonance, are better than LVEF in predicting ventricular arrhythmias in patients with non-ischemic cardiomyopathy (NICM). Methods and results Patients with NICM in a derivation cohort (n=866) and a validation cohort (n=848) underwent quantification of TF and GZF. The primary composite endpoint was sudden cardiac death (SCD) or VAs (ventricular fibrillation or sustained ventricular tachycardia). The primary endpoint was met by 52/866 (6.0%) patients in the derivation cohort (follow-up of 7.5 years [interquartile range, IQR] 5.2-9.3). In competing risk analyses, MF on visual assessment (MFVA) predicted the primary endpoint (hazard ratio [HR] 5.83, 95% confidence intervals [CI] 3.15-10.8). Quantified MF measures permitted categorization into 3 risk groups: a TF &amp;gt;0 ≤ 10 g was associated with an intermediate risk (HR: 4.03 [95% CI 1.99-8.16]) and a TF &amp;gt; 10g with the highest risk (HR: 9.17 [95% CI 4.64-18.1]) compared to no MFVA (lowest risk) (Figure). Similar trends were observed in the validation cohort. Categorization into 3 risk groups was achievable using TF or GZF in combination or in isolation. In contrast, LVEF&amp;lt;35% was a poor predictor of the primary endpoint (validation cohort HR:1.99, 95% CI 0.99-0.41). Conclusions MFVA is a strong predictor of SCD and VAs in NICM. TF and GZF mass added incremental value to MFVA. In contrast, LVEF was not useful in arrhythmic risk stratification.

BRUGADA SYNDROME: NEWEST DATA ON RISK STRATIFICATION

Abstract Background Brugada Syndrome (BrS) is a rare genetically based channelopathy responsible for malignant arrhythmias and sudden cardiac death (SCD) in young adults. Objectives The aims was to evaluate prognostic indicators useful for accurate arrhythmic risk stratification in BrS by studying genetic, clinical, anamnestic, instrumental parameters, and those derived from electrophysiological study (EPS) and intracavitary mapping. Materials and Methods 209 patients with Brugada type 1 were recruited between 01/01/2018 and 01/10/2023. Results In the first study, predictive factors related to high arrhythmic risk from the data obtained for the total population of 209 Brugada patients enrolled in our case series include a positive history of cardiogenic syncope (p value 0.000041), having had a resuscitated cardiac arrest (p value 0.00025), presenting positive EPS for inducibility of ventricular tachycardia (p value 0.00032), having a family history of sudden cardiac death (p value &amp;lt; 0.00001), and presenting positive cardiac mapping and MRI for pathological alterations (both with p value &amp;gt; 0.00001). From the data obtained for the subset of 80 Brugada patients with ICD in our case series, predictive factors related to high arrhythmic risk are different from the previously analyzed population: presenting spontaneous type 1 ECG (p value 0.0015), having had a resuscitated cardiac arrest (p value 0.014028), having a family history of sudden cardiac death (p value &amp;lt; 0.00001), and presenting positive mapping, genetics, and cardiac MRI for pathological alterations. Positive EPS and syncope in this subset do not appear to be risk factors, unlike the population of all 209 enrolled patients. Conclusions Multiparametric scores, including the mentioned markers, have been introduced in clinical practice for a better approach in selection. However, new markers are needed to improve risk stratification in BrS patients, especially those in the so–called "gray zone" with intermediate risk. In the future, it will be evaluated through experimental models and genotype–phenotype integration whether such electrical heterogeneity is also associated with specific genetic or structural abnormalities and may represent a new therapeutic target for etiological treatment in BrS patients. In conclusion, there is an urgent need to develop new methods for risk stratification in Brugada syndrome that are sufficiently sensitive, specific, reproducible, and easy to apply.

Stratification of arrhythmic risk in patients admitted for acute coronary syndrome: the role of the machine learning-derived PRAISE score

Abstract Funding Acknowledgements None. Background Patients with acute coronary syndrome (ACS) have a high arrhythmic burden, and either ventricular and supraventricular arrhythmias are very common during hospitalization[1]. Patients who develop arrhythmias show higher mortality and worse prognosis. The PRAISE (Prediction of Adverse Events Following an Acute Coronary Syndrome) score is a machine-learning based model for predicting 1-year all cause death, recurrent acute myocardial infarction and major bleedings in patients after ACS[2]. To date, its role to predict arrhythmic complications in ACS remains unknown. Purpose We hypothesized that patients with higher PRAISE score have a higher arrhythmic burden. Aim was to evaluate in a prospective protocol the PRAISE score capability for identifying patients with ACS at higher risk of arrhythmic complications during in-hospital stay. Methods A total of 365 consecutive patients admitted to our cardiac intensive care unit for ACS and undergoing percutaneous coronary intervention were enrolled. All patient was monitored by continuous electrocardiogram during hospitalization. The PRAISE score was obtained for each patient &amp;lt;24 hours from admission. Patients were divided into 2 groups based on the results of the PRAISE score for all-cause of death as patients without high PRAISE score (e.g. with low to intermediate risk, n=350) and patients with high PRAISE score (e.g. with high risk, n=15). The occurrence of atrial fibrillation (AF) and ventricular arrhythmias (VAs) up to discharge was recorded. AF was defined by detection of at least one episode of uncoordinated atrial electrical activation and irregular R-R intervals, in absence of distinct repeating P waves and with irregular atrial activations lasting at least 5 minutes. We considered as VAs all episodes of ventricular non-sustained tachycardia, sustained ventricular tachycardia and ventricular fibrillation. Results ROC curve analysis indicated a significant relationship between high PRAISE score and risk of both in-hospital AF (AUC 0.89, 95%CI 0.82-0.94, p=0.0001) and VAs (AUC 0.69, 95%CI 0.64-0.75, p=0.0001). A high PRAISE score had a specificity of 99% for AF and 92% for VAs. By Kaplan-Meier analysis, patients with high PRAISE score more frequently developed AF (30% vs 4% in those without high score; log rank p=0.00001) and VAs (77% vs 35%; log rank p=0.00001). Multivariate analysis showed that a high PRAISE score was an independent predictor of both AF (HR 4.08, 95%CI 1.14-14.58, p=0.030) and VAs (HR 2.36, 95%CI 1.08-5.14, p=0.030). Conclusions In patients hospitalized for ACS, the PRAISE score has a comprehensive capability to identify with high specificity those patients prone to develop arrhythmic events during hospitalization. This may be important in order to stratify patients with higher arrhythmic risk in whom individualized strategies may improve the clinical outcome.Baseline features of the populationKaplan-Meier anaysis

Multiparametric Mapping via Cardiovascular Magnetic Resonance in the Risk Stratification of Ventricular Arrhythmias and Sudden Cardiac Death.

Risk stratification for malignant ventricular arrhythmias and sudden cardiac death is a daunting task for physicians in daily practice. Multiparametric mapping sequences obtained via cardiovascular magnetic resonance imaging can improve the risk stratification for malignant ventricular arrhythmias by unveiling the presence of pathophysiological pro-arrhythmogenic processes. However, their employment in clinical practice is still restricted. The present review explores the current evidence supporting the association between mapping abnormalities and the risk of ventricular arrhythmias in several cardiovascular diseases. The key message is that further clinical studies are needed to test the additional value of mapping techniques beyond conventional cardiovascular magnetic resonance imaging for selecting patients eligible for an implantable cardioverter defibrillator.

Evaluation of Autonomic Nervous System Through Spectral Analysis in Patients with Ischemic Heart Failure: A Systematic Review

Abstract Heart failure is one of the most complex pathologies and most prevalent diseases, with around 64 million people affected globally (2% of the total adult population). In the initial phase of heart failure, studies have shown that the autonomic nervous system is in a state of imbalance, with an increase in the influence of the sympathetic nervous system on the heart. Persistence of long-term activation of the autonomic nervous system produces a decrease in the levels of myocardial catecholamines and also promotes down-regulation of beta-1 receptors. This article is a systematic review of papers from four well-known and important scientific databases on the subject of power spectral analysis of ECG Holter monitoring in patients with heart failure. The PRISMA technique was used to identify the most relevant articles on this subject, and their results were presented based on importance of their use for the diagnosis of heart failure, for risk stratification of sudden death or arrhythmias, for the evaluation of certain treatments used, or for other possible pathophysiological mechanisms involved in the evolution of the disease.

Ultra-High-Frequency ECG in Cardiac Pacing and Cardiac Resynchronization Therapy: From Technical Concept to Clinical Application.

Identifying electrical dyssynchrony is crucial for cardiac pacing and cardiac resynchronization therapy (CRT). The ultra-high-frequency electrocardiography (UHF-ECG) technique allows instantaneous dyssynchrony analyses with real-time visualization. This review explores the physiological background of higher frequencies in ventricular conduction and the translational evolution of UHF-ECG in cardiac pacing and CRT. Although high-frequency components were studied half a century ago, their exploration in the dyssynchrony context is rare. UHF-ECG records ECG signals from eight precordial leads over multiple beats in time. After initial conceptual studies, the implementation of an instant visualization of ventricular activation led to clinical implementation with minimal patient burden. UHF-ECG aids patient selection in biventricular CRT and evaluates ventricular activation during various forms of conduction system pacing (CSP). UHF-ECG ventricular electrical dyssynchrony has been associated with clinical outcomes in a large retrospective CRT cohort and has been used to study the electrophysiological differences between CSP methods, including His bundle pacing, left bundle branch (area) pacing, left ventricular septal pacing and conventional biventricular pacing. UHF-ECG can potentially be used to determine a tailored resynchronization approach (CRT through biventricular pacing or CSP) based on the electrical substrate (true LBBB vs. non-specified intraventricular conduction delay with more distal left ventricular conduction disease), for the optimization of CRT and holds promise beyond CRT for the risk stratification of ventricular arrhythmias.

ECG-based risk stratification of sudden cardiac death and life-threatening ventricular arrhythmias

Risk stratification of sudden cardiac death and life-threatening ventricular arrhythmias remains an unsolved problem of modern cardiology. Technological progress in the field of electrocardiography and cardiac monitoring enables discovering and researching potential ECG risk predictors based on novel methods of ECG data analysis.

Artificial intelligence for ventricular arrhythmia capability using ambulatory electrocardiograms.

European and American clinical guidelines for implantable cardioverter defibrillators are insufficiently accurate for ventricular arrhythmia (VA) risk stratification, leading to significant morbidity and mortality. Artificial intelligence offers a novel risk stratification lens through which VA capability can be determined from the electrocardiogram (ECG) in normal cardiac rhythm. The aim of this study was to develop and test a deep neural network for VA risk stratification using routinely collected ambulatory ECGs. A multicentre case-control study was undertaken to assess VA-ResNet-50, our open source ResNet-50-based deep neural network. VA-ResNet-50 was designed to read pyramid samples of three-lead 24 h ambulatory ECGs to decide whether a heart is capable of VA based on the ECG alone. Consecutive adults with VA from East Midlands, UK, who had ambulatory ECGs as part of their NHS care between 2014 and 2022 were recruited and compared with all comer ambulatory electrograms without VA. Of 270 patients, 159 heterogeneous patients had a composite VA outcome. The mean time difference between the ECG and VA was 1.6 years (⅓ ambulatory ECG before VA). The deep neural network was able to classify ECGs for VA capability with an accuracy of 0.76 (95% confidence interval 0.66-0.87), F1 score of 0.79 (0.67-0.90), area under the receiver operator curve of 0.8 (0.67-0.91), and relative risk of 2.87 (1.41-5.81). Ambulatory ECGs confer risk signals for VA risk stratification when analysed using VA-ResNet-50. Pyramid sampling from the ambulatory ECGs is hypothesized to capture autonomic activity. We encourage groups to build on this open-source model. Can artificial intelligence (AI) be used to predict whether a person is at risk of a lethal heart rhythm, based solely on an electrocardiogram (an electrical heart tracing)? In a study of 270 adults (of which 159 had lethal arrhythmias), the AI was correct in 4 out of every 5 cases. If the AI said a person was at risk, the risk of lethal event was three times higher than normal adults. In this study, the AI performed better than current medical guidelines. The AI was able to accurately determine the risk of lethal arrhythmia from standard heart tracings for 80% of cases over a year away-a conceptual shift in what an AI model can see and predict. This method shows promise in better allocating implantable shock box pacemakers (implantable cardioverter defibrillators) that save lives.

Right Ventricular Function in Arrhythmogenic Right Ventricular Cardiomyopathy: Potential Value of Strain Echocardiography

Arrhythmogenic right ventricular cardiomyopathy is an inherited cardiomyopathy, characterized by abnormal cell adhesions, disrupted intercellular signaling, and fibrofatty replacement of the myocardium. These changes serve as a substrate for ventricular arrhythmias, placing patients at risk of sudden cardiac death, even in the early stages of the disease. Current echocardiographic criteria for diagnosing arrhythmogenic right ventricular cardiomyopathy lack sensitivity, but novel markers of cardiac deformation are not subject to the same technical limitations as current guideline-recommended measures. Measuring cardiac deformation using speckle tracking allows for meticulous quantification of global systolic function, regional function, and dyssynchronous contraction. Consequently, speckle tracking to quantify myocardial strain could potentially be useful in the diagnostic process for the determination of disease progression and to assist risk stratification for ventricular arrhythmias and sudden cardiac death. This narrative review provides an overview of the potential use of different myocardial right ventricular strain measures for characterizing right ventricular dysfunction in arrhythmogenic right ventricular cardiomyopathy and its utility in assessing the risk of ventricular arrhythmias.

Arrhythmogenic Cardiomyopathy: Definition, Classification and Arrhythmic Risk Stratification.

Arrhythmogenic cardiomyopathy (ACM) is a heart disease characterized by a fibrotic replacement of myocardial tissue and a consequent predisposition to ventricular arrhythmic events, especially in the young. Post-mortem studies and the subsequent diffusion of cardiac MRI have shown that left ventricular involvement in arrhythmogenic cardiomyopathy is common and often develops early. Regarding the arrhythmic risk stratification, the current scores underestimate the arrhythmic risk of patients with arrhythmogenic cardiomyopathy with left involvement. Indeed, the data on arrhythmic risk stratification in this group of patients are contradictory and not exhaustive, with the consequence of not correctly identifying patients at a high arrhythmic risk who deserve protection from arrhythmic death. We propose a literature review on arrhythmic risk stratification in patients with ACM and left involvement to identify the main features associated with an increased arrhythmic risk in this group of patients.

Enhancing Arrhythmogenic Right Ventricular Cardiomyopathy Detection and Risk Stratification: Insights from Advanced Echocardiographic Techniques.

The echocardiographic diagnosis criteria for arrhythmogenic right ventricular cardiomyopathy (ARVC) are highly specific but sensitivity is low, especially in the early stages of the disease. The role of echocardiographic strain in ARVC has not been fully elucidated, although prior studies suggest that it can improve the detection of subtle functional abnormalities. The purposes of the study were to determine whether these advanced measures of right ventricular (RV) dysfunction on echocardiogram, including RV strain, increase diagnostic value for ARVC disease detection and to evaluate the association of echocardiographic parameters with arrhythmic outcomes. The study included 28 patients from the Heart Institute of São Paulo ARVC cohort with a definite diagnosis of ARVC established according to the 2010 Task Force Criteria. All patients were submitted to ECHO's advanced techniques including RV strain, and the parameters were compared to prior conventional visual ECHO and CMR. In total, 28 patients were enrolled in order to perform ECHO's advanced techniques. A total of 2/28 (7%) patients died due to a cardiovascular cause, 2/28 (7%) underwent heart transplantation, and 14/28 (50%) patients developed sustained ventricular arrhythmic events. Among ECHO's parameters, RV dilatation, measured by RVDd (p = 0.018) and RVOT PSAX (p = 0.044), was significantly associated with arrhythmic outcomes. RV free wall longitudinal strain < 14.35% in absolute value was associated with arrhythmic outcomes (p = 0.033). Our data suggest that ECHO's advanced techniques improve ARVC detection and that abnormal RV strain can be associated with arrhythmic risk stratification. Further studies are necessary to better demonstrate these findings and contribute to risk stratification in ARVC, in addition to other well-known risk markers.