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

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BACKGROUND: Electrocardiographic abnormalities are common in arrhythmogenic right ventricular cardiomyopathy and are included in the 2010 Task Force Criteria. Their time course, however, remains uncertain. In this retrospective observational study, we aimed to assess the long-term evolution of electrocardiographic characteristics and their relation to ventricular arrhythmias. METHODS AND RESULTS: Three hundred fifty-three patients with arrhythmogenic right ventricular cardiomyopathy as per the 2010 Task Force Criteria with 6871 automatically processed 12-lead digital ECGs were included. The relationship between the electrocardiographic parameters and the risk of ventricular arrhythmias was assessed at 10 years from the first ECG. Electrocardiographic parameters were compared between the first contact ECG, the ECG at diagnosis, and the most recent ECG. Median time between the first and the latest ECG was 6 [interquartile range, 1-14] years. Reductions of QRS voltage, R- and T-wave amplitudes between the first, diagnostic, and the latest ECGs were observed across precordial and extremity leads. Mean QRS duration increased from 96 to 102 ms (P<0.001), terminal activation duration (V1) from 47 to 52 ms (P<0.001), and QTc from 419 to 432 ms (P<0.001). T-wave inversions in leads V3 to V6 and aVF at first ECG were associated with ventricular arrhythmias (adjusted hazard ratio [HRadj][V3], 2.03 [95% CI, 1.23-3.34] and HRadj[aVF], 1.87 [95% CI, 1.13-3.08]). CONCLUSIONS: Depolarization and repolarization parameters evolved over time in patients with arrhythmogenic right ventricular cardiomyopathy, supporting the progressive nature of arrhythmogenic right ventricular cardiomyopathy. Electrocardiographic abnormalities may be detected before diagnosis and might, although not fulfilling the 2010 Task Force Criteria, be markers of early disease. T-wave inversion in leads V3 or aVF before diagnosis was associated with ventricular arrhythmias during follow-up.

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Atrial fibrillation (AF) prediction and screening are of important clinical interest because of the potential to prevent serious adverse events. Devices capable of detecting short episodes of arrhythmia are now widely available. Although it has recently been suggested that some high-risk patients with AF detected on implantable devices may benefit from anticoagulation, long-term management remains challenging in lower-risk patients and in those with AF detected on monitors or wearable devices as the development of clinically meaningful arrhythmia burden in this group remains unknown. Identification and prediction of clinically relevant AF is therefore of unprecedented importance to the cardiologic community. Family history and underlying genetic markers are important risk factors for AF. Recent studies suggest a good predictive ability of polygenic risk scores, with a possible additive value to clinical AF prediction scores. Artificial intelligence, enabled by the exponentially increasing computing power and digital data sets, has gained traction in the past decade and is of increasing interest in AF prediction using a single or multiple lead sinus rhythm electrocardiogram. Integrating these novel approaches could help predict AF substrate severity, thereby potentially improving the effectiveness of AF screening and personalizing the management of patients presenting with conditions such as embolic stroke of undetermined source or subclinical AF. This review presents current evidence surrounding deep learning and polygenic risk scores in the prediction of incident AF and provides a futuristic outlook on possible ways of implementing these modalities into clinical practice, while considering current limitations and required areas of improvement.

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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.

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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.

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Importance: Congenital long QT syndrome (LQTS) is associated with syncope, ventricular arrhythmias, and sudden death. Half of patients with LQTS have a normal or borderline-normal QT interval despite LQTS often being detected by QT prolongation on resting electrocardiography (ECG). Objective: To develop a deep learning-based neural network for identification of LQTS and differentiation of genotypes (LQTS1 and LQTS2) using 12-lead ECG. Design, Setting, and Participants: This diagnostic accuracy study used ECGs from patients with suspected inherited arrhythmia enrolled in the Hearts in Rhythm Organization Registry (HiRO) from August 2012 to December 2021. The internal dataset was derived at 2 sites and an external validation dataset at 4 sites within the HiRO Registry; an additional cross-sectional validation dataset was from the Montreal Heart Institute. The cohort with LQTS included probands and relatives with pathogenic or likely pathogenic variants in KCNQ1 or KCNH2 genes with normal or prolonged corrected QT (QTc) intervals. Exposures: Convolutional neural network (CNN) discrimination between LQTS1, LQTS2, and negative genetic test results. Main Outcomes and Measures: The main outcomes were area under the curve (AUC), F1 scores, and sensitivity for detecting LQTS and differentiating genotypes using a CNN method compared with QTc-based detection. Results: A total of 4521 ECGs from 990 patients (mean [SD] age, 42 [18] years; 589 [59.5%] female) were analyzed. External validation within the national registry (101 patients) demonstrated the CNN's high diagnostic capacity for LQTS detection (AUC, 0.93; 95% CI, 0.89-0.96) and genotype differentiation (AUC, 0.91; 95% CI, 0.86-0.96). This surpassed expert-measured QTc intervals in detecting LQTS (F1 score, 0.84 [95% CI, 0.78-0.90] vs 0.22 [95% CI, 0.13-0.31]; sensitivity, 0.90 [95% CI, 0.86-0.94] vs 0.36 [95% CI, 0.23-0.47]), including in patients with normal or borderline QTc intervals (F1 score, 0.70 [95% CI, 0.40-1.00]; sensitivity, 0.78 [95% CI, 0.53-0.95]). In further validation in a cross-sectional cohort (406 patients) of high-risk patients and genotype-negative controls, the CNN detected LQTS with an AUC of 0.81 (95% CI, 0.80-0.85), which was better than QTc interval-based detection (AUC, 0.74; 95% CI, 0.69-0.78). Conclusions and Relevance: The deep learning model improved detection of congenital LQTS from resting ECGs and allowed for differentiation between the 2 most common genetic subtypes. Broader validation over an unselected general population may support application of this model to patients with suspected LQTS.

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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.

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Cardiovascular conditions are among the most frequent causes of impairment to drive, because they might induce unpredictable mental state alterations via diverse mechanisms like myocardial ischemia, cardiac arrhythmias, and vascular dysfunction. Accordingly, health professionals are often asked to assess patients' fitness to drive (FTD). The Canadian Cardiovascular Society previously published FTD guidelines in 2003-2004; herein, we present updated FTD guidelines. Because there are no randomized trials on FTD, observational studies were used to estimate the risk of driving impairment in each situation, and recommendations made on the basis of Canadian Cardiovascular Society Risk of Harm formula. More restrictive recommendations were made for commercial drivers, who spend longer average times behind the wheel, use larger vehicles, and might transport a larger number of passengers. We provide guidance for individuals with: (1) active coronary artery disease; (2) various forms of valvular heart disease; (3) heart failure, heart transplant, and left ventricular assist device situations; (4) arrhythmia syndromes; (5) implantable devices; (6) syncope history; and (7) congenital heart disease. We suggest appropriate waiting times after cardiac interventions or acute illnesses before driving resumption. When short-term driving cessation is recommended, recommendations are on the basis of expert consensus rather than the Risk of Harm formula because risk elevation is expected to be transient. These recommendations, although not a substitute for clinical judgement or governmental regulations, provide specialists, primary care providers, and allied health professionals with a comprehensive list of a wide range of cardiac conditions, with guidance provided on the basis of the level of risk of impairment, along with recommendations about ability to drive and the suggested duration of restrictions.

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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.

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Atrial cardiomyopathy is a condition that causes electrical and contractile dysfunction of the atria, often along with structural and functional changes. Atrial cardiomyopathy most commonly occurs in conjunction with ventricular dysfunction, in which case it is difficult to discern the atrial features that are secondary to ventricular dysfunction from those that arise as a result of primary atrial abnormalities. Isolated atrial cardiomyopathy (atrial-selective cardiomyopathy [ASCM], with minimal or no ventricular function disturbance) is relatively uncommon and has most frequently been reported in association with deleterious rare genetic variants. The genes involved can affect proteins responsible for various biological functions, not necessarily limited to the heart but also involving extracardiac tissues. Atrial enlargement and atrial fibrillation are common complications of ASCM and are often the predominant clinical features. Despite progress in identifying disease-causing rare variants, an overarching understanding and approach to the molecular pathogenesis, phenotypic spectrum, and treatment of genetic ASCM is still lacking. In this review, we aim to analyze the literature relevant to genetic ASCM to understand the key features of this rather rare condition, as well as to identify distinct characteristics of ASCM and its arrhythmic complications that are related to specific genotypes. We outline the insights that have been gained using basic research models of genetic ASCM in vitro and in vivo and correlate these with patient outcomes. Finally, we provide suggestions for the future investigation of patients with genetic ASCM and improvements to basic scientific models and systems. Overall, a better understanding of the genetic underpinnings of ASCM will not only provide a better understanding of this condition but also promises to clarify our appreciation of the more commonly occurring forms of atrial cardiomyopathy associated with ventricular dysfunction.

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Patients with new-onset left bundle branch block (LBBB) after transcatheter aortic valve implantation (TAVI) are at risk of developing delayed high-degree atrioventricular block. Management of new-onset LBBB post-TAVI remains controversial. In the Comparison of a Clinical Monitoring Strategy Versus Electrophysiology-Guided Algorithmic Approach in Patients With a New LBBB After TAVI (COME-TAVI) trial, consenting patients with new-onset LBBB that persists on day 2 after TAVI, meeting exclusion/inclusion criteria, are randomized to an electrophysiological study (EPS)-guided approach or 30-day electrocardiographic monitoring. In the EPS-guided approach, patients with a His to ventricle (HV) interval ≥ 65 ms undergo permanent pacemaker implantation. Patients randomized to noninvasive monitoring receive a wearable continuous electrocardiographic recording and transmitting device for 30 days. Follow-up will be performed at 3, 6, and 12 months. The primary endpoint is a composite outcome designed to capture net clinical benefit. The endpoint incorporates major consequences of both strategies in patients with new-onset LBBB after TAVI, as follows: (i) sudden cardiac death; (ii) syncope; (iii) atrioventricular conduction disorder requiring a pacemaker (for a class I or IIa indication); and (iv) complications related to the pacemaker or EPS. The trial incorporates a Bayesian design with a noninformative prior, outcome-adaptive randomization (initially 1:1), and 2 prespecified interim analyses once 25% and 50% of the anticipated number of primary endpoints are reached. The trial is event-driven, with an anticipated upper limit of 452 patients required to reach 77 primary outcome events over 12 months of follow-up. In summary, the aim of this Bayesian multicentre randomized trial is to compare 2 management strategies in patients with new-onset LBBB post-TAVI-an EPS-guided approach vs noninvasive 30-day monitoring. Trial registration number: NCT03303612.

Les patients chez qui un bloc de branche gauche (BBG) est récemment apparu à la suite de l'implantation valvulaire aortique par cathéter (IVAC) présentent un risque de bloc auriculoventriculaire de haut degré tardif. La prise en charge d'un BBG récemment apparu après une IVAC demeure controversée. Dans le cadre de l'essai COME-TAVI (Comparison of a ClinicalMonitoring Strategy VersusElectrophysiology-Guided Algorithmic Approach in Patients With a New LBBB AfterTAVI, ou comparaison d'une stratégie de surveillance clinique, par rapport à une approche guidée par étude électrophysiologique et fondée sur un algorithme, chez des patients présentant un BBG d'apparition récente à la suite d'une IVAC), des patients qui présentent un BBG d'apparition récente persistant le 2e jour après une IVAC, qui répondent aux critères d'admissibilité et qui ont donné leur consentement sont répartis aléatoirement pour être suivis à l'aide d'une approche guidée par une étude électrophysiologique (EEP) ou faire l'objet d'une surveillance électrocardiographique d'une durée de 30 jours. Un stimulateur cardiaque est implanté chez les patients du groupe de l'EEP dont l'intervalle HV (temps de conduction dans le tronc du faisceau de His jusqu'aux ventricules) est ≥ 65 ms. Les patients du groupe de surveillance non invasive reçoivent un dispositif portable d'enregistrement et de transmission continue de données électrocardiographiques pour une période de 30 jours. Le suivi sera réalisé aux 3e, 6e et 12e mois. Le critère d'évaluation principal est un paramètre composite conçu afin de saisir le bienfait clinique net. Il comprend les conséquences majeures des deux stratégies chez les patients présentant un BBG d'apparition récente après une IVAC, comme suit : (i) mort subite d'origine cardiaque; (ii) syncope; (iii) trouble de la conduction auriculoventriculaire nécessitant la pose d'un stimulateur cardiaque (pour une indication de classe I ou IIa); et (iv) complications relatives au stimulateur cardiaque ou à l'EEP. L'essai intègre une conception bayésienne avec une répartition aléatoire (dans un rapport initial de 1:1) antérieure non informative adaptée aux résultats et deux analyses intermédiaires définies au préalable lorsque 25 % et 50 % du nombre anticipé des critères d'évaluation principaux seront atteints. L'essai est axé sur les événements, et la limite supérieure anticipée pour atteindre 77 événements relatifs aux critères d'évaluation principaux sur 12 mois de suivi est de 452 patients. En résumé, l'objectif de cet essai bayésien multicentrique à répartition aléatoire est de comparer deux stratégies de prise en charge de patients présentant un BBG d'apparition récente après une IVAC, soit une approche guidée par une EEP, par rapport à une surveillance non invasive de 30 jours. Trial registration number: NCT03303612.

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Hypertrophic cardiomyopathy (HCM) is the most prevalent inherited cardiac disease. Since the modern description of HCM more than seven decades ago, great focus has been placed on preventing its most catastrophic complication: sudden cardiac death (SCD). Implantable cardioverter-defibrillators (ICD) have been recognized to provide effective prophylactic therapy. Over the years, two leading societies, the European Society of Cardiology (ESC) and the American Heart Association/American College of Cardiology (AHA/ACC), have proposed risk stratification models to assess SCD in adults. European guidelines rely on a risk calculator, the HCM Risk-SCD, while American guidelines propose a stand-alone risk factor approach. Recently, risk prediction models were also developed in the pediatric population. This article reviews the latest recommendations on the risk stratification of SCD in HCM and summarises current indications for ICD use.

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Inherited arrhythmia syndromes are rare genetic conditions that predispose seemingly healthy individuals to sudden cardiac arrest and death. The Hearts in Rhythm Organization is a multidisciplinary Canadian network of clinicians, researchers, patients, and families that aims to improve care for patients and families with inherited cardiac conditions, focused on those that confer predisposition to arrhythmia and sudden cardiac arrest and/or death. The field is rapidly evolving as research discoveries increase. A streamlined, practical guide for providers to diagnose and follow pediatric and adult patients with inherited cardiac conditions represents a useful tool to improve health system utilization, clinical management, and research related to these conditions. This review provides consensus care pathways for 7 conditions, including the 4 most common inherited cardiac conditions that confer predisposition to arrhythmia, with scenarios to guide investigation, diagnosis, risk stratification, and management. These conditions include Brugada syndrome, long QT syndrome, arrhythmogenic right ventricular cardiomyopathy and related arrhythmogenic cardiomyopathies, and catecholaminergic polymorphic ventricular tachycardia. In addition, an approach to investigating and managing sudden cardiac arrest, sudden unexpected death, and first-degree family members of affected individuals is provided. Referral to specialized cardiogenetic clinics should be considered in most cases. The intention of this review is to offer a framework for the process of care that is useful for both experts and nonexperts, and related allied disciplines such as hospital management, diagnostic services, coroners, and pathologists, in order to provide high-quality, multidisciplinary, standardized care.

Les syndromes d'arythmie héréditaires sont des troubles génétiques rares qui prédisposent des personnes en apparence en bonne santé à un arrêt cardiaque soudain et à la mort. L'organisation Hearts in Rhythm Organization est un réseau multidisciplinaire canadien qui regroupe des cliniciens, des chercheurs ainsi que des patients et leurs proches dans le but d'améliorer les soins prodigués aux patients atteints de maladies cardiaques héréditaires et à leur famille, en particulier dans le cas des maladies qui entraînent une prédisposition à l'arythmie et à un arrêt cardiaque soudain et/ou à la mort. Puisque ce champ de recherche évolue rapidement, la mise au point d'un guide pratique et simple à l'intention des professionnels de la santé pour le diagnostic et le suivi des patients enfants et adultes présentant une maladie cardiaque héréditaire serait donc un outil intéressant pour améliorer l'utilisation du système de santé et la prise en charge clinique de ces maladies tout en orientant la recherche à ce propos. La présente synthèse expose les trajectoires de soins faisant l'objet d'un consensus pour sept maladies, dont les quatre maladies cardiaques héréditaires les plus courantes qui prédisposent à l'arythmie. Elle présente aussi des scénarios pour orienter les examens, le diagnostic, la stratification du risque et la prise en charge des patients. Ces maladies sont le syndrome de Brugada, le syndrome du QT long, la cardiomyopathie arythmogénique du ventricule droit et les cardiomyopathies arythmogènes associées, et la tachycardie ventriculaire polymorphe catécholaminergique. En outre, une approche pour la prise en charge de l'arrêt cardiaque soudain, de mort subite inattendue et des membres de la famille immédiate de la personne touchée est proposée. L'orientation vers des cliniques spécialisées en cardiogénétique doit être envisagée dans la plupart des cas. L'objectif est d'établir un cadre de soins qui soit utile pour les experts et les non-experts ainsi que pour les professionnels des domaines connexes, par exemple le personnel de l'administration hospitalière et des services diagnostiques, les coroners et les pathologistes, en vue d'offrir des soins multidisciplinaires normalisés de grande qualité.

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BACKGROUNDS: High-degree atrioventricular block (AVB) recovery in CS has been shown to be highly variable despite immunosuppressive treatment, with no reliable tool available to predict odds of reversibility. This study sought to evaluate the potential of combined fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) and resting myocardial perfusion imaging (rMPI) to predict reversibility of newly diagnosed high-grade AVB in cardiac sarcoidosis (CS). METHODS: We performed a single-center, retrospective analysis of patients with CS presenting with high-grade AVB who underwent combined FDG-PET/CT and rMPI. The 2016 JCS and the 2014 HRS diagnostic criteria were used for the diagnosis of CS. Patients with a history of coronary artery disease or prior immunosuppressive treatment were excluded. Patients were divided into AVB recovery and non-recovery subgroups. CS disease staging was based on FDG-PET and rMPI findings: (Stage 0) normal FDG-PET and rMPI (Stage 1) positive FDG-PET and normal rMPI (Stage 2) positive FDG-PET with perfusion deficits on rMPI (Stage 3) normal FDG-PET with perfusion deficits on rMPI. RESULTS: Twenty-seven patients, including 13 demonstrating AVB recovery, were identified. Eleven out of fourteen (78.6%) patients presenting with stage 1 CS demonstrated AVB recovery. Stage 1 CS was significantly more present in the recovery group compared to the non-recovery group (84.6% vs 21.4%, P = .002). Eleven presented with stage 2 CS, with only 2 (18.2%) recovering AV nodal conduction. Stage 2 CS presented more frequently in the non-recovery group (64.3% vs 15.4%, P = .020). CONCLUSIONS: Combined FDG-PET and rMPI employed to stage CS disease presenting with high-degree AVB appears to have good performance for predicting likelihood of recovery.

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Hypertrophic cardiomyopathy (HCM) is an important cause of morbidity and mortality with both monogenic and polygenic components. We here report results from the largest HCM genome-wide association study (GWAS) and multi-trait analysis (MTAG) including 5,900 HCM cases, 68,359 controls, and 36,083 UK Biobank (UKB) participants with cardiac magnetic resonance (CMR) imaging. We identified a total of 70 loci (50 novel) associated with HCM, and 62 loci (32 novel) associated with relevant left ventricular (LV) structural or functional traits. Amongst the common variant HCM loci, we identify a novel HCM disease gene, SVIL, which encodes the actin-binding protein supervillin, showing that rare truncating SVIL variants cause HCM. Mendelian randomization analyses support a causal role of increased LV contractility in both obstructive and non-obstructive forms of HCM, suggesting common disease mechanisms and anticipating shared response to therapy. Taken together, the findings significantly increase our understanding of the genetic basis and molecular mechanisms of HCM, with potential implications for disease management.