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Sudden cardiac death (SCD) in young people is predominantly caused by genetic causes as cardiomyopathies. Hypertrophic cardiomyopathy is the most common genetic cardiovascular disease and is responsible for the major proportion of SCD in the young. The purpose of this study was to identify the genetic variants present in young SCD victims with HCM characteristics. From the Portuguese records of autopsies performed at the National Institute of Legal Medicine and Forensic Sciences, North Delegation, 16 young (16-50 years) SCD victims whose death was suspected to be a manifestation of HCM were selected. Using next-generation sequencing, the coding regions of 40 genes associated with HCM, candidates, or strongly related to HCM-phenocopies were investigated. The victims included in this study were all males, with a mean age of 33.4 ± 11.7 years, left ventricle mean thickness of 21.5 ± 6.28 mm, and the majority of deaths occurred during sleep (36%). A pathogenic or likely pathogenic variant was identified in six out of 16 (37.5%) victims, in the most common HCM genes (MYBPC3 and MYH7). Our results indicate that molecular autopsy of SCD victims contributes to a more precise identification of a cause of death, and this can be used in the prevention of SCD cases through family screening of first relatives who may carry the same pathogenic variant.

The spectrum of inherited arrhythmogenic diseases (IADs) includes disorders without overt structural abnormalities (i.e. primary inherited arrhythmia syndromes) and structural heart diseases (i.e. arrhythmogenic ventricular cardiomyopathy, hypertrophic cardiomyopathy). The aim of this European Heart Rhythm Association (EHRA) survey was to evaluate current clinical practice and adherence to 2015 European Society of Cardiology Guidelines regarding the management of patients with IADs. A 24-item centre-based online questionnaire was presented to the EHRA Research Network Centres and the European Cardiac Arrhythmia Genetics Focus Group members. There were 46 responses from 20 different countries. The survey revealed that 37% of centres did not have any dedicated unit focusing on patients with IADs. Provocative drug challenges were widely used to rule-out Brugada syndrome (BrS) (91% of centres), while they were used in a minority of centres during the diagnostic assessment of long-QT syndrome (11%), early repolarization syndrome (12%), or catecholaminergic polymorphic ventricular tachycardia (18%). While all centres advised family clinical screening with electrocardiograms for all first-degree family members of patients with IADs, genetic testing was advised in family members of probands with positive genetic testing by 33% of centres. Sudden cardiac death risk stratification was straightforward and in line with current guidelines for hypertrophic cardiomyopathy, while it was controversial for other diseases (i.e. BrS). Finally, indications for ventricular mapping and ablation procedures in BrS were variable and not in agreement with current guidelines in up to 54% of centres.

Background Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. Machine learning is a powerful tool used in ECG analysis and has shown to outperform cardiologists. Objectives We aimed to develop machine learning and deep learning models to diagnose PLN p.Arg14del cardiomyopathy using ECGs and evaluate their accuracy compared to an expert cardiologist. Methods We included 155 adult PLN mutation carriers and 155 age- and sex-matched control subjects. Twenty-one PLN mutation carriers (13.4%) were classified as symptomatic (symptoms of heart failure or malignant ventricular arrhythmias). The data set was split into training and testing sets using 4-fold cross-validation. Multiple models were developed to discriminate between PLN mutation carriers and control subjects. For comparison, expert cardiologists classified the same data set. The best performing models were validated using an external PLN p.Arg14del mutation carrier data set from Murcia, Spain (n = 50). We applied occlusion maps to visualize the most contributing ECG regions. Results In terms of specificity, expert cardiologists (0.99) outperformed all models (range 0.53–0.81). In terms of accuracy and sensitivity, experts (0.28 and 0.64) were outperformed by all models (sensitivity range 0.65–0.81). T-wave morphology was most important for classification of PLN p.Arg14del carriers. External validation showed comparable results, with the best model outperforming experts. Conclusion This study shows that machine learning can outperform experienced cardiologists in the diagnosis of PLN p.Arg14del cardiomyopathy and suggests that the shape of the T wave is of added importance to this diagnosis.

While physical activity is beneficial across all age groups for both healthy people and those predisposed to chronic medical conditions such as coronary artery disease, the role of physical activity in the setting of patients with genetic heart diseases is more complex. A well described association between high intensity physical activity and sudden cardiac death (SCD) has been established and this has historically led to blanket sport and physical activity restrictions for any patient meeting diagnostic criteria for a genetic heart disease such as hypertrophic cardiomyopathy (HCM). The increased understanding of the beneficial effects of even light intensity physical activity has called in to question whether we are doing enough to actively encourage patients to undertake sufficient light to moderate intensity physical activity. Negotiating a balance between avoiding high intensity physical activity and at the same time encouraging light to moderate intensity physical activity for the purpose of general health is a real challenge for clinicians when discussing lifestyle advice with patients. This PhD aimed to determine the physical activity behaviours and identify major barriers to physical activity in individuals with HCM, and develop an intervention to increase physical activity levels in those with this condition. Additionally, the physical activity behaviours of individuals with an implantable cardioverter defibrillator and attitudes towards physical activity were investigated. Finally, this PhD aimed to examine the incidence of sudden death events in a major Australian fun-run and discuss the potential for genetic heart diseases to be the underlying cause in these and other sudden death cases during physical activity.

The 22q11.2 deletion syndrome is characterized by multiple congenital anomalies including conotruncal cardiac defects. Identifying the patient with a 22q11.2 deletion (22q11del) can be challenging because many extracardiac features become apparent later in life. We sought to better define the cardiac phenotype associated with a 22q11del to help direct genetic testing. 1,610 patients with conotruncal defects were sequentially tested for a 22q11del. The counts and frequencies of primary lesions and cardiac features were tabulated for those with and those without a 22q11del. Logistic regression models investigated cardiac features that predicted deletion status in tetralogy of Fallot (TOF). Deletion frequency varied by primary anatomic phenotype. Regardless of the cardiac diagnosis, a concurrent aortic arch anomaly (AAA) was strongly associated with deletion status [odds ratio (OR), 5.07; 95 % confidence interval (CI), 3.66-7.04]. In the TOF subset, the strongest predictor of deletion status was an AAA (OR, 3.14; 95 % CI 1.87-5.27; p < 0.001), followed by pulmonary valve atresia (OR, 2.03; 95 % CI 1.02-4.02; p = 0.04). Among those with double-outlet right ventricle and transposition of the great arteries, only those with an AAA had a 22q11del. However, 5 % of the patients with an isolated conoventricular ventricular septal defect and normal aortic arch anatomy had a 22q11del, whereas no one with an interrupted aortic arch type A had a 22q11del. A subset of patients with conotruncal defects are at risk for a 22q11del. A concurrent AAA increases the risk regardless of the intracardiac anatomy. These findings help to direct genetic screening for the 22q11.2 deletion syndrome in the cardiac patient.