Hiroshi Kawazoe1, Yukiko Nakano2, Hidenori Ochi3, Masahiko Takagi4, Yusuke Hayashi4, Yuko Uchimura1, Takehito Tokuyama1, Yoshikazu Watanabe1, Hiroya Matsumura1, Shunsuke Tomomori1, Akinori Sairaku1, Kazuyoshi Suenari1, Akinori Awazu5, Yosuke Miwa6, Kyoko Soejima6, Kazuaki Chayama3, Yasuki Kihara1. 1. Department of Cardiovascular Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan. 2. Department of Cardiovascular Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan. Electronic address: nakanoy@hiroshima-u.ac.jp. 3. Department of Gastroenterology and Metabolism, Applied Life Sciences, Institute of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan. 4. Department of Internal Medicine and Cardiology, Osaka City University Graduate School of Medicine, Osaka, Japan. 5. Department of Mathematical and Life Sciences, Hiroshima University, Hiroshima, Japan. 6. Department of Cardiology, Kyorin University Hospital, Tokyo, Japan.
Abstract
BACKGROUND: Risk stratification for ventricular fibrillation (VF) in patients with Brugada syndrome (BrS) remains controversial. OBJECTIVE: The purpose of this study was to construct a novel prediction model for VF risk in BrS patients using noninvasive parameters. METHODS: A total of 143 Japanese BrS patients with VF (n = 35) and without VF (n = 108) were retrospectively enrolled. We built a logistic regression model predicting VF occurrence and evaluated it by cross-validation. RESULTS: Frequencies of history of syncope and spontaneous type 1 ECG, r-J interval in V1, QRS duration in V6, and LAS40, Tpeak-Tend dispersion, and max T-wave alternans were significantly associated with VF occurrence in univariate analyses. The history of syncope, r-J interval in V1, QRS duration in V6, and Tpeak-Tend dispersion were identified as independent predictors by multivariate logistic regression analysis. The predictive model was constructed using all these parameters with good discrimination of VF occurrence (area under the curve 0.869 with 97.1% sensitivity and 65.7% specificity). The area under the curve based on leave-one-out cross-validation was 0.845, with 97.1% sensitivity and 63.0% specificity suggesting good performance of the model. Retrospective survival analysis revealed that the cumulative VF event rate was significantly higher in patients at high risk than in those with low risk using the log rank test (P = 2.97 × 10(-8)). Notably, no BrS patient below the cutoff value developed a subsequent VF event. CONCLUSION: This novel prediction method may effectively assesses VF risk in BrS patients, especially when determining implantable cardioverter-defibrillator placement for asymptomatic BrS patients.
BACKGROUND: Risk stratification for ventricular fibrillation (VF) in patients with Brugada syndrome (BrS) remains controversial. OBJECTIVE: The purpose of this study was to construct a novel prediction model for VF risk in BrS patients using noninvasive parameters. METHODS: A total of 143 Japanese BrS patients with VF (n = 35) and without VF (n = 108) were retrospectively enrolled. We built a logistic regression model predicting VF occurrence and evaluated it by cross-validation. RESULTS: Frequencies of history of syncope and spontaneous type 1 ECG, r-J interval in V1, QRS duration in V6, and LAS40, Tpeak-Tend dispersion, and max T-wave alternans were significantly associated with VF occurrence in univariate analyses. The history of syncope, r-J interval in V1, QRS duration in V6, and Tpeak-Tend dispersion were identified as independent predictors by multivariate logistic regression analysis. The predictive model was constructed using all these parameters with good discrimination of VF occurrence (area under the curve 0.869 with 97.1% sensitivity and 65.7% specificity). The area under the curve based on leave-one-out cross-validation was 0.845, with 97.1% sensitivity and 63.0% specificity suggesting good performance of the model. Retrospective survival analysis revealed that the cumulative VF event rate was significantly higher in patients at high risk than in those with low risk using the log rank test (P = 2.97 × 10(-8)). Notably, no BrS patient below the cutoff value developed a subsequent VF event. CONCLUSION: This novel prediction method may effectively assesses VF risk in BrS patients, especially when determining implantable cardioverter-defibrillator placement for asymptomatic BrS patients.
Authors: Marta Pérez-Hernández; Marcos Matamoros; Silvia Alfayate; Paloma Nieto-Marín; Raquel G Utrilla; David Tinaquero; Raquel de Andrés; Teresa Crespo; Daniela Ponce-Balbuena; B Cicero Willis; Eric N Jiménez-Vazquez; Guadalupe Guerrero-Serna; Andre M da Rocha; Katherine Campbell; Todd J Herron; F Javier Díez-Guerra; Juan Tamargo; José Jalife; Ricardo Caballero; Eva Delpón Journal: JCI Insight Date: 2018-09-20
Authors: Mu Chen; Dong-Zhu Xu; Adonis Z Wu; Shuai Guo; Juyi Wan; Dechun Yin; Shien-Fong Lin; Zhenhui Chen; Michael Rubart-von der Lohe; Thomas H Everett; Zhilin Qu; James N Weiss; Peng-Sheng Chen Journal: JCI Insight Date: 2018-11-15
Authors: Ahmed Bayoumy; Meng-Qi Gong; Ka Hou Christien Li; Sunny Hei Wong; William Kk Wu; Guang-Ping Li; George Bazoukis; Konstantinos P Letsas; Wing Tak Wong; Yun-Long Xia; Tong Liu; Gary Tse Journal: J Geriatr Cardiol Date: 2017-10 Impact factor: 3.327