Clinical features and outcomes in carriers of pathogenic desmoplakin variants.

BACKGROUND AND AIMS: Pathogenic variants in the desmoplakin (DSP) gene are associated with the development of a distinct arrhythmogenic cardiomyopathy phenotype not fully captured by either dilated cardiomyopathy (DCM), non-dilated left ventricular cardiomyopathy (NDLVC), or arrhythmogenic right ventricular cardiomyopathy (ARVC). Prior studies have described baseline DSP cardiomyopathy genetic, inflammatory, and structural characteristics. However, cohort sizes have limited full clinical characterization and identification of clinical and demographic predictors of sustained ventricular arrhythmias (VAs), heart failure (HF) hospitalizations, and transplant/death. In particular, the relevance of acute myocarditis-like episodes for subsequent disease course is largely unknown. METHODS: All patients with pathogenic/likely pathogenic (P/LP) DSP variants in the worldwide DSP-ERADOS Network (26 academic institutions across nine countries) were included. The primary outcomes were the development of sustained VA and HF hospitalizations during follow-up. Fine-Gray regressions were used to test association between clinical and instrumental parameters and the development of outcomes. RESULTS: Eight hundred patients [40.3 ± 17.5 years, 47.5% probands, left ventricular ejection fraction (LVEF) 49.5 ± 13.9%] were included. Over 3.7 [1.4-7.1] years, 139 (17.4%, 3.9%/year) and 72 (9.0%, 1.8%/year) patients experienced sustained VA and HF episodes, respectively. A total of 32.5% of individuals did not fulfil diagnostic criteria for ARVC, DCM, or NDLVC; their VA incidence was 0.5%/year. In multivariable regression, risk features associated with the development of VA were female sex [adjusted hazard ratio (aHR) 1.547; P = .025], prior non-sustained ventricular tachycardia (aHR 1.721; P = .009), prior sustained VA (aHR 1.923; P = .006), and LVEF ≤ 50% (aHR: 1.645; P = .032), while for HF, they were the presence of T-wave inversion in 3+ electrocardiogram leads (aHR 2.036, P = .007) and LVEF ≤ 50% (aHR 3.879; P < .001). Additionally, 70 (8.8%) patients experienced a myocardial injury episode at presentation or during follow-up. These episodes were associated with an increased risk of VA and HF thereafter (HR 2.394; P < .001, and HR 5.064, P < .001, respectively). CONCLUSIONS: Patients with P/LP DSP variants experience high rates of sustained VA and HF hospitalizations. These patients demonstrate a distinct clinical phenotype (DSP cardiomyopathy), whose most prominent risk features associated with adverse clinical outcomes are the presence of prior non-sustained ventricular tachycardia or sustained VA, T-wave inversion in 3+ leads on electrocardiogram, LVEF ≤ 50%, and myocardial injury events.

Natural History, Phenotype Spectrum and Clinical Outcomes of Desmin (DES)-Associated Cardiomyopathy.

Background: Pathogenic/likely pathogenic (P/LP) desmin (DES) variants cause heterogeneous cardiomyopathy and/or skeletal myopathy phenotypes. Limited data suggest a high incidence of major adverse cardiac events (MACE), including cardiac conduction disease (CCD), sustained ventricular arrhythmias (VA), and heart failure (HF) events (HF hospitalization, LVAD/cardiac transplant, HF-related death), in patients with P/LP DES variants. However, pleiotropic presentation and small cohort sizes have limited clinical phenotype and outcome characterization. Objectives: We aimed to describe the natural history, phenotype spectrum, familial penetrance and outcomes in patients with P/LP DES variants through a systematic review and individual patient data meta-analysis using published reports. Methods: We searched Medline (PubMed) and Embase for studies that evaluated cardiac phenotypes in patients with P/LP DES variants. Cardiomyopathy diagnosis or occurrence of MACE were considered evidence of cardiac involvement/penetrance. Lifetime event-free survival from CCD, sustained VA, HF events, and composite MACE was assessed. Results: Out of 4,212 screened publications, 71 met the inclusion criteria. A total of 230 patients were included (52.6% male, 52.2% probands, median age: 31 years [22.0; 42.8] at first evaluation, median follow-up: 3 years [0; 11.0]). Overall, 124 (53.9%) patients were diagnosed with cardiomyopathy, predominantly dilated cardiomyopathy (14.8%), followed by restrictive cardiomyopathy (13.5%), whereas other forms were less common: arrhythmogenic cardiomyopathy (7.0%), hypertrophic cardiomyopathy (6.1%), arrhythmogenic right ventricular cardiomyopathy (5.2%), and other forms (7.4%). Overall, 132 (57.4%) patients developed MACE, with 96 [41.7%] having CCD, 36 [15.7%] sustained VA, and 43 [18.7%] HF events. Familial penetrance of cardiac disease was 63.6% among relatives with P/LP DES variants. Male sex was associated with increased risk of sustained VA (HR 2.28, p=0.02) and HF events (HR 2.45, p=0.008). Conclusions: DES cardiomyopathy exhibits heterogeneous phenotypes and distinct natural history, characterized by high familial penetrance and substantial MACE burden. Male patients face higher risk of sustained VA events.

Improved diagnosis of arrhythmogenic right ventricular cardiomyopathy using electrocardiographic deep learning.

BACKGROUND: Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a rare genetic heart disease associated with life-threatening ventricular arrhythmias. Diagnosis of ARVC is based on the 2010 Task Force Criteria (TFC), application of which often requires clinical expertise at specialized centers. OBJECTIVE: The purpose of this study was to develop and validate an electrocardiogram (ECG) deep learning (DL) tool for ARVC diagnosis. METHODS: ECGs of patients referred for ARVC evaluation were used to develop (n = 551 [80.1%]) and test (n = 137 [19.9%]) an ECG-DL model for prediction of TFC-defined ARVC diagnosis. The ARVC ECG-DL model was externally validated in a cohort of patients with pathogenic or likely pathogenic (P/LP) ARVC gene variants identified through the Geisinger MyCode Community Health Initiative (N = 167). RESULTS: Of 688 patients evaluated at Johns Hopkins Hospital (JHH) (57.3% male, mean age 40.2 years), 329 (47.8%) were diagnosed with ARVC. Although ARVC diagnosis made by referring cardiologist ECG interpretation was unreliable (c-statistic 0.53; confidence interval [CI] 0.52-0.53), ECG-DL discrimination in the hold-out testing cohort was excellent (0.87; 0.86-0.89) and compared favorably to that of ECG interpretation by an ARVC expert (0.85; 0.84-0.86). In the Geisinger cohort, prevalence of ARVC was lower (n = 17 [10.2%]), but ECG-DL-based identification of ARVC phenotype remained reliable (0.80; 0.77-0.83). Discrimination was further increased when ECG-DL predictions were combined with non-ECG-derived TFC in the JHH testing (c-statistic 0.940; 95% CI 0.933-0.948) and Geisinger validation (0.897; 95% CI 0.883-0.912) cohorts. CONCLUSION: ECG-DL augments diagnosis of ARVC to the level of an ARVC expert and can differentiate true ARVC diagnosis from phenotype-mimics and at-risk family members/genotype-positive individuals.

Mavacamten in Right Ventricular Outflow Tract Obstruction.

Right ventricular outflow tract (RVOT) obstruction is a rare complication of ventricular hypertrophy in patients with hypertrophic cardiomyopathy (HCM). This study presents an unusual case of a patient with HCM with severe RVOT obstruction that was relieved successfully through the use of mavacamten.

Eighteen-Month Real-World Experience Using Mavacamten for Treatment of Obstructive Hypertrophic Cardiomyopathy in a Racially Diverse Population.

BACKGROUND: Patients with obstructive hypertrophic cardiomyopathy have increased symptomatic burden. Mavacamten was recently approved for treatment of obstructive hypertrophic cardiomyopathy based on 2 randomized controlled trials. However, its use under real-world conditions and in diverse populations is under-studied. METHODS AND RESULTS: This was a prospective observational cohort study of patients seen at the Johns Hopkins HCM center and prescribed mavacamten for obstructive hypertrophic cardiomyopathy between July 7, 2022 and January 6, 2024. Patients were followed longitudinally, with serial echocardiography and clinical evaluation as mandated by the risk evaluation and mitigation strategy program. Sixty-six patients received mavacamten (mean age 59 years, 47% male, 29% non-White [Black, Hispanic/Latino, Asian, Native Hawaiian or Pacific Islander], 47% obese). Before treatment, all patients had New York Heart Association class II (51.5%) or III (48.5%) heart failure symptoms. Initial maximum peak left ventricular outflow tract gradient was 107±46 mm Hg. Median treatment duration was 9 months. For patients on mavacamten after ≥6 months (n=43), symptoms improved by ≥1 New York Heart Association class in 72% of patients, and peak left ventricular outflow tract gradient decreased by 80±46 mm Hg, eliminating hemodynamically significant left ventricular outflow tract obstruction in 79.1% of patients. Mavacamten was temporarily discontinued in 3 patients due to left ventricular ejection fraction decrease <50%. There were no medication-related adverse events. Effectiveness and safety were similar between White and non-White patients, but symptomatic relief was attenuated in patients with body-mass index ≥35 kg/m2. CONCLUSIONS: Mavacamten was effective and safe when used under real-world conditions in a racially diverse population of symptomatic patients with obstructive hypertrophic cardiomyopathy. Patients with comorbid obesity were less likely to experience symptomatic improvement while on mavacamten.

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

BACKGROUND AND AIMS: 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. METHODS: 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). RESULTS: 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%. CONCLUSIONS: 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.

Long-Term Arrhythmic Follow-Up and Risk Stratification of Patients With Desmoplakin-Associated Arrhythmogenic Right Ventricular Cardiomyopathy.

Background: Patients with likely pathogenic/pathogenic desmoplakin (DSP) variants are poorly characterized. Some of them meet diagnostic criteria for arrhythmogenic right ventricular cardiomyopathy (ARVC), but it is unclear how risk stratification strategies for ARVC perform in this setting. Objectives: The purpose of this study was to characterize arrhythmic outcomes and to test the performance of the recently validated ARVC risk calculator in patients with DSP likely pathogenic/pathogenic variants fulfilling definite 2010 ARVC Task Force Criteria (DSP-TFC+). Methods: DSP-TFC+ patients were enrolled from 20 institutions across 3 continents. Ventricular arrhythmias (VA), defined as a composite of sustained ventricular tachycardia (VT), appropriate implantable cardioverter defibrillator therapies, and ventricular fibrillation/sudden cardiac death events in follow-up, were reported as the primary outcome. We tested the performance of the ARVC risk calculator for VA prediction, reporting c-statistics. Results: Among 252 DSP-TFC+ patients (age 39.6 ± 16.9 years, 35.3% male), 94 (37.3%) experienced VA over 44.5 [IQR: 19.6-78.3] months. Patients with left ventricle involvement (n = 194) were at higher VA risk (log-rank P = 0.0239). History of nonsustained VT (aHR 2.097; P = 0.004) showed the strongest association with VA occurrence during the first 5-year follow-up. Neither age (P = 0.723) nor male sex (P = 0.200) was associated with VAs at follow-up. In 204 patients without VA at diagnosis, incident VA rate was high (32.8%; 7.37%/y). The ARVC risk calculator performed poorly overall (c-statistic 0.604 [0.594-0.614]) and very poorly in patients with left ventricular disease (c-statistic 0.558 [0.556-0.560]). Conclusions: DSP-TFC+ patients are at substantial risk for VAs. The ARVC risk calculator performs poorly in DSP-TFC+ patients suggesting need for a gene-specific risk algorithm. Meanwhile, DSP-TFC+ patients with nonsustained VT should be considered as high-risk.

Implantable cardioverter defibrillator use in arrhythmogenic right ventricular cardiomyopathy in North America and Europe.

BACKGROUND AND AIMS: Implantable cardioverter-defibrillators (ICDs) are critical for preventing sudden cardiac death (SCD) in arrhythmogenic right ventricular cardiomyopathy (ARVC). This study aims to identify cross-continental differences in utilization of primary prevention ICDs and survival free from sustained ventricular arrhythmia (VA) in ARVC. METHODS: This was a retrospective analysis of ARVC patients without prior VA enrolled in clinical registries from 11 countries throughout Europe and North America. Patients were classified according to whether they received treatment in North America or Europe and were further stratified by baseline predicted VA risk into low- (<10%/5 years), intermediate- (10%-25%/5 years), and high-risk (>25%/5 years) groups. Differences in ICD implantation and survival free from sustained VA events (including appropriate ICD therapy) were assessed. RESULTS: One thousand ninety-eight patients were followed for a median of 5.1 years; 554 (50.5%) received a primary prevention ICD, and 286 (26.0%) experienced a first VA event. After adjusting for baseline risk factors, North Americans were more than three times as likely to receive ICDs {hazard ratio (HR) 3.1 [95% confidence interval (CI) 2.5, 3.8]} but had only mildly increased risk for incident sustained VA [HR 1.4 (95% CI 1.1, 1.8)]. North Americans without ICDs were at higher risk for incident sustained VA [HR 2.1 (95% CI 1.3, 3.4)] than Europeans. CONCLUSIONS: North American ARVC patients were substantially more likely than Europeans to receive primary prevention ICDs across all arrhythmic risk strata. A lower rate of ICD implantation in Europe was not associated with a higher rate of VA events in those without ICDs.

Catheter Ablation for Ventricular Tachycardia in Patients With Desmoplakin Cardiomyopathy.

BACKGROUND: Desmoplakin (DSP) pathogenic/likely pathogenic (P/LP) variants are associated with malignant phenotypes of arrhythmogenic cardiomyopathy (DSP-ACM). Reports of outcomes after ventricular tachycardia (VT) ablation in DSP-ACM are scarce. OBJECTIVES: In this study, the authors sought to report on long-term outcomes of VT ablation in DSP-ACM. METHODS: Patients with P/LP DSP variants at 9 institutions undergoing VT ablation were included. Demographic, clinical, and instrumental data as well as all ventricular arrhythmia (VA) events were collected. Sustained VAs after the index procedure were the primary outcome. A per-patient before and after ablation comparison of rates of VA episodes per year was performed as well. RESULTS: Twenty-four DSP-ACM patients (39.3 ± 12.1 years of age, 62.5% male, median 6,116 [Q1-Q3: 3,362-7,760] premature ventricular complexes [PVCs] per 24 hours, median 4 [Q1-Q3: 2-11] previous VA episodes per patient at ablation) were included. Index procedure was most commonly endocardial/epicardial (19/24) The endocardium of the right ventricle (RV), the left ventricle (LV), or both ventricles were mapped in 8 (33.3%), 9 (37.5%), and 7 (29.2%) cases, respectively. Low voltage potentials were found in 10 of 15 patients in the RV and 11 of 16 in the LV. Endocardial ablation was performed in 18 patients (75.0%). Epicardial mapping in 19 patients (79.2%) identified low voltage potentials in 17, and 16 received epicardial ablation. Over the following 2.9 years (Q1-Q3: 1.8-5.5 years), 13 patients (54.2%) experienced VA recurrences. A significant reduction in per-patient event/year before and after ablation was observed (1.4 [Q1-Q3: 0.5-2.4] to 0.1 [Q1-Q3: 0.0-0.4]; P = 0.009). Two patients needed heart transplantation, and 4 died (3 of heart failure and 1 noncardiac death). CONCLUSIONS: VT ablation in DSP-ACM is effective in reducing the VA burden of the disease, but recurrences are common. Most VT circuits are epicardial, with both LV and RV low voltage abnormalities. Heart failure complicates clinical course and is an important cause of mortality.

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach.

BACKGROUND: Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide. OBJECTIVE: The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features. METHODS: Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India). RESULTS: ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%. CONCLUSION: In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.

Arrhythmic risk stratification in arrhythmogenic right ventricular cardiomyopathy.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a heritable cardiomyopathy characterized by a predominantly arrhythmic presentation. It represents the leading cause of sudden cardiac death (SCD) among athletes and poses a significant morbidity threat in the general population. As a causative treatment for ARVC is still not available, the placement of an implantable cardioverter defibrillator represents the current cornerstone for SCD prevention in this setting. Thanks to international ARVC-dedicated efforts, significant steps have been achieved in recent years towards an individualized, patient-centred risk stratification approach. A novel risk calculator algorithm estimating the 5-year risk of arrhythmias of patients with ARVC has been introduced in clinical practice and subsequently validated. The purpose of this article is to summarize the body of evidence that has allowed the development of this tool and to discuss the best way to implement its use in the care of an individual patient.

Predicting ventricular tachycardia circuits in patients with arrhythmogenic right ventricular cardiomyopathy using genotype-specific heart digital twins.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review.

Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.

Predicting Ventricular Tachycardia Circuits in Patients with Arrhythmogenic Right Ventricular Cardiomyopathy using Genotype-specific Heart Digital Twins.

Arrhythmogenic right ventricular cardiomyopathy (ARVC) is a genetic cardiac disease that leads to ventricular tachycardia (VT), a life-threatening heart rhythm disorder. Treating ARVC remains challenging due to the complex underlying arrhythmogenic mechanisms, which involve structural and electrophysiological (EP) remodeling. Here, we developed a novel genotype-specific heart digital twin (Geno-DT) approach to investigate the role of pathophysiological remodeling in sustaining VT reentrant circuits and to predict the VT circuits in ARVC patients of different genotypes. This approach integrates the patient's disease-induced structural remodeling reconstructed from contrast-enhanced magnetic-resonance imaging and genotype-specific cellular EP properties. In our retrospective study of 16 ARVC patients with two genotypes: plakophilin-2 (PKP2, n = 8) and gene-elusive (GE, n = 8), we found that Geno-DT accurately and non-invasively predicted the VT circuit locations for both genotypes (with 100%, 94%, 96% sensitivity, specificity, and accuracy for GE patient group, and 86%, 90%, 89% sensitivity, specificity, and accuracy for PKP2 patient group), when compared to VT circuit locations identified during clinical EP studies. Moreover, our results revealed that the underlying VT mechanisms differ among ARVC genotypes. We determined that in GE patients, fibrotic remodeling is the primary contributor to VT circuits, while in PKP2 patients, slowed conduction velocity and altered restitution properties of cardiac tissue, in addition to the structural substrate, are directly responsible for the formation of VT circuits. Our novel Geno-DT approach has the potential to augment therapeutic precision in the clinical setting and lead to more personalized treatment strategies in ARVC.

Clinical Applications, Methodology, and Scientific Reporting of Electrocardiogram Deep-Learning Models: A Systematic Review.

BACKGROUND: The electrocardiogram (ECG) is one of the most common diagnostic tools available to assess cardio-vascular health. The advent of advanced computational techniques such as deep learning has dramatically expanded the breadth of clinical problems that can be addressed using ECG data, leading to increasing popularity of ECG deep-learning models aimed at predicting clinical endpoints. OBJECTIVES: The purpose of this study was to define the current landscape of clinically relevant ECG deep-learning models and examine practices in the scientific reporting of these studies. METHODS: We performed a systematic review of PubMed and EMBASE databases to identify clinically relevant ECG deep-learning models published through July 1, 2022. RESULTS: We identified 44 manuscripts including 53 unique, clinically relevant ECG deep-learning models. The rate of publication of ECG deep-learning models is increasing rapidly. The most common clinical applications of ECG deep learning were identification of cardiomyopathy (14/53 [26%]), followed by arrhythmia detection (9/53 [17%]). Methodologic reporting varied; while 33/44 (75%) publications included model architecture diagrams, complete information required to reproduce these models was provided in only 10/44 (23%). Saliency analysis was performed in 20/44 (46%) of publications. Only 18/53 (34%) models were tested within external validation cohorts. Model code or resources allowing for model implementation by external groups were available for only 5/44 (11%) publications. CONCLUSIONS: While ECG deep-learning models are increasingly clinically relevant, their reporting is highly variable, and few publications provide sufficient detail for methodologic reproduction or model validation by external groups. The field of ECG deep learning would benefit from adherence to a set of standardized scientific reporting guidelines.

Longitudinal Prediction of Ventricular Arrhythmic Risk in Patients With Arrhythmogenic Right Ventricular Cardiomyopathy.

BACKGROUND: The arrhythmogenic right ventricular cardiomyopathy (ARVC) risk calculator stratifies risk for incident sustained ventricular arrhythmias (VA) at the time of ARVC diagnosis. However, included risk factors change over time, and how well the ARVC risk calculator performs at follow-up is unknown. METHODS: This was a retrospective analysis of patients with definite ARVC and without prior sustained VA. Risk factors for VA including age, nonsustained ventricular tachycardia, premature ventricular complex burden, T-wave inversions on electrocardiogram, cardiac syncope, right ventricular function, therapeutic medication use, and exercise intensity were assessed at the time of 2010 Task Force Criteria based ARVC diagnosis and upon repeat evaluations. Changes in these risk factors were analyzed over 5-year follow-up. The 5-year risk of VA was predicted longitudinally using (1) the baseline ARVC risk calculator prediction, (2) the ARVC risk prediction calculated using updated risk factors, and (3) time-varying Cox regression. Discrimination and calibration were assessed in comparison to observed VA event rates. RESULTS: Four hundred eight patients with ARVC experiencing 132 primary VA events were included. Matched comparison of risk factors at baseline versus at 5 years of follow-up revealed decreased burdens of premature ventricular complexes (-1200/day) and nonsustained ventricular tachycardia (-14%). Presence of significant right ventricular dysfunction and number of T-wave inversions on electrocardiogram were unchanged. Observed risk for VA decreased by 13% by 5 years follow-up. The baseline ARVC risk calculator's ability to predict 5-year VA risk worsened during follow-up (C statistics, 0.83 at diagnosis versus 0.68 at 5 years). Both the updated ARVC risk calculator (C statistics of 0.77) and time-varying Cox regression model (C statistics, 0.77) had strong discrimination. The updated ARVC risk calculator overestimated 5-year VA risk by an average of +6%. CONCLUSIONS: Risk factors for VA in ARVC are dynamic, and overall risk for incident sustained VA decreases during follow-up. Up-to-date risk factor assessment improves VA risk stratification.

Programmed Ventricular Stimulation as an Additional Primary Prevention Risk Stratification Tool in Arrhythmogenic Right Ventricular Cardiomyopathy: A Multinational Study.

BACKGROUND: A novel risk calculator based on clinical characteristics and noninvasive tests that predicts the onset of clinical sustained ventricular arrhythmias (VA) in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC) has been proposed and validated by recent studies. It remains unknown whether programmed ventricular stimulation (PVS) provides additional prognostic value. METHODS: All patients with a definite ARVC diagnosis, no history of sustained VAs at diagnosis, and PVS performed at baseline were extracted from 6 international ARVC registries. The calculator-predicted risk for sustained VA (sustained or implantable cardioverter defibrillator treated ventricular tachycardia [VT] or fibrillation, [aborted] sudden cardiac arrest) was assessed in all patients. Independent and combined performance of the risk calculator and PVS on sustained VA were assessed during a 5-year follow-up period. RESULTS: Two hundred eighty-eight patients (41.0±14.5 years, 55.9% male, right ventricular ejection fraction 42.5±11.1%) were enrolled. At PVS, 137 (47.6%) patients had inducible ventricular tachycardia. During a median of 5.31 [2.89-10.17] years of follow-up, 83 (60.6%) patients with a positive PVS and 37 (24.5%) with a negative PVS experienced sustained VA (P<0.001). Inducible ventricular tachycardia predicted clinical sustained VA during the 5-year follow-up and remained an independent predictor after accounting for the calculator-predicted risk (HR, 2.52 [1.58-4.02]; P<0.001). Compared with ARVC risk calculator predictions in isolation (C-statistic 0.72), addition of PVS inducibility showed improved prediction of VA events (C-statistic 0.75; log-likelihood ratio for nested models, P<0.001). PVS inducibility had a 76% [67-84] sensitivity and 68% [61-74] specificity, corresponding to log-likelihood ratios of 2.3 and 0.36 for inducible (likelihood ratio+) and noninducible (likelihood ratio-) patients, respectively. In patients with a ARVC risk calculator-predicted risk of clinical VA events <25% during 5 years (ie, low/intermediate subgroup), PVS had a 92.6% negative predictive value. CONCLUSIONS: PVS significantly improved risk stratification above and beyond the calculator-predicted risk of VA in a primary prevention cohort of patients with ARVC, mainly for patients considered to be at low and intermediate risk by the clinical risk calculator.