Data on exercise and cardiac imaging in a patient cohort with hypertrophic cardiomyopathy

Data presented in this paper are supplementary material to our study âVigorous exercise in patients with hypertrophic cardiomyopathyâ [1]. The current article presents supplementary data on collection and analyses of exercise parameters and genetic data in the original research article. Keywords: Hypertrophic cardiomyopathy, Exercise, Genetics, Arrhythmi

Increased levels of sST2 in patients with mitral annulus disjunction and ventricular arrhythmias

Objective Displacement of the mitral valve, mitral annulus disjunction (MAD), is described as a possible aetiology of sudden cardiac death. Stress-induced fibrosis in the mitral valve apparatus has been suggested as the underlying mechanism. We aimed to explore the association between stretch-related and fibrosis-related biomarkers and ventricular arrhythmias in MAD. We hypothesised that soluble suppression of tumourigenicity-2 (sST2) and transforming growth factor-β1 (TGFβ1) are markers of ventricular arrhythmias in patients with MAD. Methods We included patients with ≥1 mm MAD on cardiac MRI. We assessed left ventricular ejection fraction (LVEF) and fibrosis by late gadolinium enhancement (LGE). The occurrence of ventricular arrhythmia, defined as aborted cardiac arrest, sustained or non-sustained ventricular tachycardia, was retrospectively assessed. We assessed circulating sST2 and TGFβ1 levels. Results We included 72 patients with MAD, of which 22 (31%) had ventricular arrhythmias. Patients with ventricular arrhythmias had lower LVEF (60 % (±6) vs 63% (±6), p = 0.04), more frequently papillary muscle fibrosis (14 (64%) vs 10 (20%), p < 0.001) and higher sST2 levels (31.6 ± 10.1 ng/mL vs 25.3 ± 9.2 ng/mL, p = 0.01) compared with those without, while TGFβ1 levels did not differ (p = 0.29). Combining sST2 level, LVEF and papillary muscle fibrosis optimally detected individuals with arrhythmia (area under the curve 0.82, 95% CI 0.73 to 0.92) and improved the risk model (p < 0.05) compared with single parameters. Conclusion Circulating sST2 levels were higher in patients with MAD and ventricular arrhythmias compared with arrhythmia-free patients. Combining sST2, LVEF and LGE assessment improved risk stratification in patients with MAD

A new prediction model for ventricular arrhythmias in arrhythmogenic right ventricular cardiomyopathy

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 20.3% reduction of ICD placements with the same proportion of protected patients (P &amp;lt; 0.001). Conclusion Using the Largest cohort of patients with ARVC and no prior VA, a prediction model using readily available clinical parameters was devised to estimate VA risk and guide decisions regarding primary prevention ICDs (www.arvcrisk.com).Funding Agencies|Canadian Heart Rhythm Society George Mines Traveling Fellowship; Montreal Heart Institute Foundation; Fondation LeducqLeducq Foundation [16 CVD 02]; Dutch Heart FoundationNetherlands Heart Foundation [2015T058, CVON2015-12 eDETECT, 2012-10 PREDICT]; Netherlands Organisation for Scientific ResearchNetherlands Organization for Scientific Research (NWO) [040.11.586]; Netherlands Heart Institute [06901]; Swiss National Science FoundationSwiss National Science Foundation (SNSF)European Commission [320030_160327]; UMC Utrecht 2017 Alexandre Suerman Stipend; UMC Utrecht Fellowship Clinical Research Talent; European Unions Horizon 2020 research and innovation program under the ERA-NET Co-fund action [680969]; Dr Francis P. Chiaramonte Private Foundation; Leyla Erkan Family Fund for ARVD Research; Dr Satish, Rupal, and Robin Shah ARVD Fund at Johns Hopkins; Bogle Foundation; Healing Hearts Foundation; Campanella family; Patrick J. Harrison Family; Peter French Memorial Foundation; Wilmerding Endowments; Georg und Bertha Schwyzer-Winiker Foundation; Baugarten Foundation; Swiss Heart Foundation; Leonie-Wild Foundation; Marvin and Philippa Carsley Chair of Medicine; UCL Hospitals NIHR Biomedical Research Centre</p