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Arrhythmogenic right ventricular cardiomyopathy (ARVC) causes ventricular arrhythmias (VAs) and sudden cardiac death (SCD). In 2019, a risk prediction model that estimates the 5-year risk of incident VAs in ARVC was developed (ARVCrisk.com). This study aimed to externally validate this prediction model in a large international multicentre cohort and to compare its performance with the risk factor approach recommended for implantable cardioverter-defibrillator (ICD) use by published guidelines and expert consensus. In a retrospective cohort of 429 individuals from 29 centres in North America and Europe, 103 (24%) experienced sustained VA during a median follow-up of 5.02 (2.05-7.90) years following diagnosis of ARVC. External validation yielded good discrimination [C-index of 0.70 (95% confidence interval-CI 0.65-0.75)] and calibration slope of 1.01 (95% CI 0.99-1.03). Compared with the three published consensus-based decision algorithms for ICD use in ARVC (Heart Rhythm Society consensus on arrhythmogenic cardiomyopathy, International Task Force consensus statement on the treatment of ARVC, and American Heart Association guidelines for VA and SCD), the risk calculator performed better with a superior net clinical benefit below risk threshold of 35%. Using a large independent cohort of patients, this study shows that the ARVC risk model provides good prognostic information and outperforms other published decision algorithms for ICD use. These findings support the use of the model to facilitate shared decision making regarding ICD implantation in the primary prevention of SCD in ARVC. P.J. is supported by the Daniel Bravo Foundation grant and Spanish Society of Cardiology Magda Heras mobility grant, A.G. by the Wilton W. Webster Fellow of Heart Rhythm Society, The Johns Hopkins ARVD/C Programme by the Leonie-Wild Foundation, Leyla Erkan Family Fund for ARVD Research, The Hugh Calkins, Marvin H. Weiner, and Jacqueline J. Bernstein Cardiac Arrhythmia Center, Dr Francis P. Chiramonte Private Foundation, Dr Satish, Rupal, and Robin Shah ARVD Fund at Johns Hopkins, Bogle Foundation, Campanella Family, Patrick J. Harrison Family, Peter French Memorial Foundation, and Wilmerding Endowments, H.K.J. by grants from Novo Nordisk Foundation, Denmark (NNF18OC0031258 and NNF20OC0065151), S.A.L. by NIH grant 1R01HL139731 and American Heart Association 18SFRN34250007, R.T. by the Canada Research Chairs programme, J.C.T. by the Philippa and Marvin Carsley Cardiology Research Chair. S.G.P. receives support from Ricerca Corrente funding scheme of the Italian Ministry of Health and Italian Ministry of Research and University Dipartimenti di Eccellenza 2018 to 2022 grant to the Molecular Medicine Department (University of Pavia). Sí

ImportanceIndividuals with schizophrenia have a reduced life-expectancy compared to the general population, largely due to an increased risk of cardiovascular disease (CVD). Clinical and epidemiological studies have been unable to fully unravel the nature of this relationship.ObjectiveInvestigate genetic correlations and potential bi-directional effects between liability to schizophrenia and CVD.Design, setting, and participantsWe obtained summary-data of genome-wide-association studies of schizophrenia (N=130,644), heart failure (N=977,323), coronary artery disease (N=332,477), systolic and diastolic blood pressure (N=757,601), heart rate variability (N=46,952), QT interval (N=103,331), early repolarization and dilated cardiomyopathy ECG patterns (N=63,700). We computed genetic correlations with linkage disequilibrium score regression and conducted bi-directional Mendelian randomization (MR). With multivariable MR, we investigated whether associations were mediated by smoking, body mass index, physical activity, lipid levels, or type 2 diabetes. To ensure robustness, we applied a range of sensitivity methods.Main outcomes and measuresSchizophrenia, heart failure, coronary artery disease, systolic blood pressure, diastolic blood pressure, heart rate variability, QT interval, early repolarization, dilated cardiomyopathy.ResultsGenetic correlations between liability to schizophrenia and CVD were close to zero (−0.02 to 0.04). With MR, we found robust evidence that liability to schizophrenia increases heart failure risk. This effect remained consistent with multivariable MR. There was also evidence that liability to schizophrenia increases early repolarization risk, largely mediated by BMI and lipid levels. Finally, there was evidence that liability to schizophrenia increases heart rate variability, a direction of effect contrasting previous studies. In the other direction, there was weak evidence that higher systolic, but not diastolic, blood pressure increases schizophrenia risk.Conclusions and relevanceOur findings indicate that liability to schizophrenia increases the risk of heart failure, and that this is not mediated by key health behaviours. This is consistent with the notion that schizophrenia is characterised by a systemic dysregulation of the body (including inflammation and oxidative stress) with detrimental effects on the heart. To decrease cardiovascular mortality among schizophrenia patients, priority should lie with optimal treatment and interventions in early stages of psychoses. We also identified early repolarization, currently understudied, as a potential CVD marker among patients with schizophrenia.

AIMS: Arrhythmogenic right ventricular dysplasia/cardiomyopathy (ARVC) is characterized by ventricular arrhythmias (VAs) and sudden cardiac death (SCD). We aimed to develop a model for individualized prediction of incident VA/SCD in ARVC patients. METHODS AND RESULTS: Five hundred and twenty-eight patients with a definite diagnosis and no history of sustained VAs/SCD at baseline, aged 38.2 ± 15.5 years, 44.7% male, were enrolled from five registries in North America and Europe. Over 4.83 (interquartile range 2.44-9.33) years of follow-up, 146 (27.7%) experienced sustained VA, defined as SCD, aborted SCD, sustained ventricular tachycardia, or appropriate implantable cardioverter-defibrillator (ICD) therapy. A prediction model estimating annual VA risk was developed using Cox regression with internal validation. Eight potential predictors were pre-specified: age, sex, cardiac syncope in the prior 6 months, non-sustained ventricular tachycardia, number of premature ventricular complexes in 24 h, number of leads with T-wave inversion, and right and left ventricular ejection fractions (LVEFs). All except LVEF were retained in the final model. The model accurately distinguished patients with and without events, with an optimism-corrected C-index of 0.77 [95% confidence interval (CI) 0.73-0.81] and minimal over-optimism [calibration slope of 0.93 (95% CI 0.92-0.95)]. By decision curve analysis, the clinical benefit of the model was superior to a current consensus-based ICD placement algorithm with a 20.6% reduction of ICD placements with the same proportion of protected patients (P

Background/Introduction Guidelines for variant interpretation in Mendelian disease set stringent criteria to report a variant as (likely) pathogenic, prioritising control of false positive rate over test sensitivity and diagnostic yield, and require customisation for the specific genetic characteristics of gene-disease dyads. Inherited arrhythmias like long QT syndrome (LQTS) and Brugada syndrome (BrS) are genetically heterogeneous, with missense variants constituting the preponderance of disease-causing variants. Evidence from family segregation or functional assays to confirm pathogenicity are often unavailable or impractical in clinical genetic testing, leading to high rates of variants of uncertain significance and false negative test results. Methods We compared rare variant frequencies from 1847 LQTS (KCNQ1, KCNH2, SCN5A) and 3335 BrS (SCN5A) cases from the International LQTS/BrS Genetics Consortia to population-specific gnomAD data. We propose disease-specific criteria for ACMG/AMP evidence classes – rarity (PM2/BS1 rules) and enrichment of individual (PS4) and domain-specific (PM1) variants in cases over controls. Results Rare SCN5A variant prevalence differed between BrS cases with spontaneous (28.7%) versus induced (15.8%) type 1 Brugada ECG (p=1.3x10–13) and between European (20.8%) and Japanese (8.9%) patients (p=8.8x10–18). Transmembrane regions and specific N-terminus (KCNH2) and C-terminus (KCNQ1/KCNH2) domains were characterised by high enrichment of case variants and >95% probability of pathogenicity. Applying the customised rules, 17.5% of European BrS cases and 73.7% of European LQTS cases had variants classified as (likely) pathogenic, compared to estimated diagnostic yields (case excess over gnomAD) of 19.3%/82.6%. Conclusions Large case/control datasets enable quantitative implementation of ACMG/AMP guidelines and increased sensitivity for inherited arrhythmia genetic testing. Classification of Brugada/LQTS variants Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): Dutch Heart Foundation, Netherlands Organisation for Scientific Research

Introduction Left ventricle (LV) substrate can be characterized by imaging modalities providing physiological variables (tissue perfusion, ischemia, etc.) grouped in a complex dataset. Principal Component Analysis (PCA) can identify the dataset variables that best explain its variance. Variables linked to substrate arrhythmogenicity will lead PCA to identify LV regions most influenced by the variables on a LV 3D geometry. Purpose To evaluate whether regions identified by PCA correspond to regions targeted by physicians in a ventricular tachycardia (VT) ablation procedure. Methods Ischemic VT subjects underwent SPECT/CT perfusion imaging (rest and stress) prior to LV voltage mapping with the cardiac mapping system. Co-registration allowed projection of ablation sites onto SPECT/CT geometries. PCA retrospectively analyzed the following dataset: tissue perfusion (rest and stress), tissue ischemia and the local (1cm2 sub-regions) stress-rest difference for: 1) standard deviation (STD) and 2) skewness (Skew). PCA components (PCAc) explaining ≥85% of the total variance were plotted on the LV 3D geometry to display regions highly influenced by the dataset variables (see figure below). Results Ten subjects (9 males, 66 ± 8 years old, LVEF 37 ± 11%) underwent co-registration. In 7/10 subjects, tissue perfusion (in PCAc#1) and ischemia (in PCAc#2) were most influential on LV regions variance. Ischemia and low perfusion (≤40%) areas equaled 32 ± 19 % of the LV with 21 ± 23% of these areas highly involved in the arrhythmogenic substrate (≥75% of explained LV variance). The location of 63 ± 18% of ablation sites were Conclusion Preliminary results showed that PCA can synthesize the influence of different perfusion derived variables, like tissue perfusion and ischemia, used in SPECT/CT imaging of the LV. Further analysis is needed to confirm whether this could be used to select VT ablation targets. Abstract Figure.