Constantinos O'Mahony1, Fatima Jichi2, Menelaos Pavlou3, Lorenzo Monserrat4, Aristides Anastasakis5, Claudio Rapezzi6, Elena Biagini6, Juan Ramon Gimeno7, Giuseppe Limongelli8, William J McKenna1, Rumana Z Omar9, Perry M Elliott10. 1. The Inherited Cardiac Diseases Unit, The Heart Hospital/University College London, 16-18 Westmoreland St., London W1H 8PH, UK. 2. Biostatistics Group, University College London Hospitals/University College London Research Support Centre, University College London, Gower St., London WC1E 6BT, UK. 3. Department of Statistical Science, University College London, Gower St, London WC1E 6BT, UK. 4. Cardiology Department and Research Unit, A Coruña University Hospital, Galician Health Service, Spain. 5. Unit of Inherited Cardiovascular Diseases, 1st Department of Cardiology, University of Athens, 99 Michalakopoulou St, Athens 11527, Greece. 6. Institute of Cardiology, Department of Specialised, Experimental and Diagnostic Medicine, University of Bologna, Via Massarenti 9, Bologna 40138, Italy. 7. Cardiac Department, University Hospital Virgen Arrixaca, Murcia-Cartagena s/n. El Palmar, Murcia 30120, Spain. 8. Monaldi Hospital, Second University of Naples, Via Leonardo Bianchi 1, Naples 80131, Italy. 9. Biostatistics Group, University College London Hospitals/University College London Research Support Centre, University College London, Gower St., London WC1E 6BT, UK Department of Statistical Science, University College London, Gower St, London WC1E 6BT, UK. 10. The Inherited Cardiac Diseases Unit, The Heart Hospital/University College London, 16-18 Westmoreland St., London W1H 8PH, UK perry.elliott@ucl.ac.uk.
Abstract
AIMS: Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death (SCD) in young adults. Current risk algorithms provide only a crude estimate of risk and fail to account for the different effect size of individual risk factors. The aim of this study was to develop and validate a new SCD risk prediction model that provides individualized risk estimates. METHODS AND RESULTS: The prognostic model was derived from a retrospective, multi-centre longitudinal cohort study. The model was developed from the entire data set using the Cox proportional hazards model and internally validated using bootstrapping. The cohort consisted of 3675 consecutive patients from six centres. During a follow-up period of 24 313 patient-years (median 5.7 years), 198 patients (5%) died suddenly or had an appropriate implantable cardioverter defibrillator (ICD) shock. Of eight pre-specified predictors, age, maximal left ventricular wall thickness, left atrial diameter, left ventricular outflow tract gradient, family history of SCD, non-sustained ventricular tachycardia, and unexplained syncope were associated with SCD/appropriate ICD shock at the 15% significance level. These predictors were included in the final model to estimate individual probabilities of SCD at 5 years. The calibration slope was 0.91 (95% CI: 0.74, 1.08), C-index was 0.70 (95% CI: 0.68, 0.72), and D-statistic was 1.07 (95% CI: 0.81, 1.32). For every 16 ICDs implanted in patients with ≥4% 5-year SCD risk, potentially 1 patient will be saved from SCD at 5 years. A second model with the data set split into independent development and validation cohorts had very similar estimates of coefficients and performance when externally validated. CONCLUSION: This is the first validated SCD risk prediction model for patients with HCM and provides accurate individualized estimates for the probability of SCD using readily collected clinical parameters. Published on behalf of the European Society of Cardiology. All rights reserved.
AIMS: Hypertrophic cardiomyopathy (HCM) is a leading cause of sudden cardiac death (SCD) in young adults. Current risk algorithms provide only a crude estimate of risk and fail to account for the different effect size of individual risk factors. The aim of this study was to develop and validate a new SCD risk prediction model that provides individualized risk estimates. METHODS AND RESULTS: The prognostic model was derived from a retrospective, multi-centre longitudinal cohort study. The model was developed from the entire data set using the Cox proportional hazards model and internally validated using bootstrapping. The cohort consisted of 3675 consecutive patients from six centres. During a follow-up period of 24 313 patient-years (median 5.7 years), 198 patients (5%) died suddenly or had an appropriate implantable cardioverter defibrillator (ICD) shock. Of eight pre-specified predictors, age, maximal left ventricular wall thickness, left atrial diameter, left ventricular outflow tract gradient, family history of SCD, non-sustained ventricular tachycardia, and unexplained syncope were associated with SCD/appropriate ICD shock at the 15% significance level. These predictors were included in the final model to estimate individual probabilities of SCD at 5 years. The calibration slope was 0.91 (95% CI: 0.74, 1.08), C-index was 0.70 (95% CI: 0.68, 0.72), and D-statistic was 1.07 (95% CI: 0.81, 1.32). For every 16 ICDs implanted in patients with ≥4% 5-year SCD risk, potentially 1 patient will be saved from SCD at 5 years. A second model with the data set split into independent development and validation cohorts had very similar estimates of coefficients and performance when externally validated. CONCLUSION: This is the first validated SCD risk prediction model for patients with HCM and provides accurate individualized estimates for the probability of SCD using readily collected clinical parameters. Published on behalf of the European Society of Cardiology. All rights reserved.
Authors: Maxim Avanesov; Julia Münch; Julius Weinrich; Lennart Well; Dennis Säring; Christian Stehning; Enver Tahir; Sebastian Bohnen; Ulf K Radunski; Kai Muellerleile; Gerhard Adam; Monica Patten; Gunnar Lund Journal: Eur Radiol Date: 2017-06-14 Impact factor: 5.315
Authors: Rocio Hinojar; José Luis Zamorano; Ariana Gonzalez Gómez; Maria Plaza Martin; Amparo Esteban; Luis Miguel Rincón; Juan Carlos Portugal; José Julio Jimenez Nácher; Covadonga Fernández-Golfín Journal: Clin Cardiol Date: 2017-06-14 Impact factor: 2.882