Predictive risk models for forecasting arrhythmic outcomes in Brugada syndrome: A focused review

Cheuk To Chung, George Bazoukis, Danny Radford, Emma Coakley-Youngs, Rajesh Rajan, Paweł T. Matusik, Tong Liu, Konstantinos P. Letsas, Sharen Lee, Gary Tse

Research output: Contribution to journalReview articlepeer-review

10 Citations (Scopus)

Abstract

Brugada syndrome (BrS) is a rare disorder characterized by coved or saddle-shaped ST-segment elevation in the right precordial leads on the electrocardiogram. Risk stratification in BrS remains challenging. A number of clinical, electrocardiographic, programmed ventricular stimulation and genetic risk factors have been identified as important predictors of future major arrhythmic events. There is a positive association between the number of risk factors and arrhythmic events. Hence, a multi-parametric approach would provide comprehensive risk assessment and more accurate risk stratification, assisting in therapeutic decisions making, including implantable cardioverter-defibrillator placement or identification of low-risk individuals. However, the extent to which each variable influences the risk and non-linear interactions between the different risk variables make risk stratification challenging. This paper aims to provide a focused review of the multi-parametric risk models for BrS risk stratification published in the literature.

Original languageEnglish
Pages (from-to)28-34
Number of pages7
JournalJournal of Electrocardiology
Volume72
DOIs
Publication statusPublished - 1 May 2022
Externally publishedYes

Keywords

  • Brugada syndrome
  • Depolarization
  • Repolarization
  • Risk stratification

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