Julia Cadrin-Tourigny1,2, Laurens P Bosman3,4, Anna Nozza5, Weijia Wang1, Rafik Tadros2, Aditya Bhonsale1, Mimount Bourfiss4, Annik Fortier5, Øyvind H Lie6,7, Ardan M Saguner8, Anneli Svensson9, Antoine Andorin2, Crystal Tichnell1, Brittney Murray1, Katja Zeppenfeld10, Maarten P van den Berg11, Folkert W Asselbergs3,4,12, Arthur A M Wilde13, Andrew D Krahn14, Mario Talajic2, Lena Rivard2, Stephen Chelko1, Stefan L Zimmerman15, Ihab R Kamel15, Jane E Crosson1, Daniel P Judge1, Sing-Chien Yap16, Jeroen F van der Heijden4, Harikrishna Tandri1, Jan D H Jongbloed17, Marie-Claude Guertin5, J Peter van Tintelen3,18, Pyotr G Platonov19, Firat Duru8, Kristina H Haugaa6,7, Paul Khairy2, Richard N W Hauer3,4, Hugh Calkins1, Anneline S J M Te Riele3,4, Cynthia A James1. 1. Division of Cardiology, Department of Medicine, Johns Hopkins Hospital, Carnegie 568D, 600 N. Wolfe St. Baltimore, MD, USA. 2. Cardiovascular Genetics Center, Montreal Heart Institute, Université de Montréal, 5000 Bélanger St, Montréal, Canada. 3. Netherlands Heart Institute, 3501 DG, Utrecht, The Netherlands. 4. Department of Cardiology, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, CX Utrecht, The Netherlands. 5. Montreal Health Innovations Coordinating Center, Université de Montréal, 4100 Molson St, Suite 400, Montréal, Canada. 6. Department of Cardiology, Center for Cardiological Innovation, Oslo University Hospital, Postboks 4950 Nydalen, Oslo, Norway. 7. University of Oslo, Postboks 1171, Blindern Oslo, Norway. 8. Department of Cardiology, University Heart Center Zurich, Raemistrasse 100, Zurich, Switzerland. 9. Department of Cardiology, University Hosptial of Linköping, S-581 85 Linköping, Sweden. 10. Department of Cardiology, Leiden University Medical Center, Albinusdreef 2, ZA Leiden, The Netherlands. 11. Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands. 12. Faculty of Population Health Sciences, Institute of Cardiovascular Science, Institute of Health Informatics, University College London, 69-75 Chenies Mews, London, UK. 13. Department of Clinical and Experimental Cardiology, Amsterdam UMC, University of Amsterdam, Heart Center, Meibergdreef 9, AZ, Amsterdam, The Netherlands. 14. Division of Cardiology, Department of Medicine, University of British Columbia 211 - 1033 Davie Street, Vancouver, BC, Canada. 15. The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins Hospital, 600 N. Wolfe St., Baltimore, MD, USA. 16. Department of Cardiology, Thoraxcenter, Erasmus Medical Center, Dr. Molewaterplein 40, GD, Rotterdam, The Netherlands. 17. Department of Genetics, University of Groningen, University Medical Center Groningen, Hanzeplein 1, Groningen, The Netherlands. 18. Department of Clinical Genetics, Academic Medical Center, University of Amsterdam, Meibergdreef 9, DD Amsterdam, The Netherlands. 19. Department of Cardiology, Clinical Sciences, Lund University Hosptial, Lund, Sweden.
Abstract
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 < 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).
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 < 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).
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