Literature DB >> 33521066

Automated Electrocardiogram Analysis Identifies Novel Predictors of Ventricular Arrhythmias in Brugada Syndrome.

Gary Tse1, Sharen Lee2, Andrew Li3, Dong Chang4, Guangping Li1, Jiandong Zhou5, Tong Liu1, Qingpeng Zhang5.   

Abstract

Background: Patients suffering from Brugada syndrome (BrS) are at an increased risk of life-threatening ventricular arrhythmias. Whilst electrocardiographic (ECG) variables have been used for risk stratification with varying degrees of success, automated measurements have not been tested for their ability to predict adverse outcomes in BrS.
Methods: BrS patients presenting in a single tertiary center between 2000 and 2018 were analyzed retrospectively. ECG variables on vector magnitude, axis, amplitude and duration from all 12 leads were determined. The primary endpoint was spontaneous ventricular tachycardia/ventricular fibrillation (VT/VF) on follow-up.
Results: This study included 83 patients [93% male, median presenting age: 56 (41-66) years old, 45% type 1 pattern] with 12 developing the primary endpoint (median follow-up: 75 (Q1-Q3: 26-114 months). Cox regression showed that QRS frontal axis > 70.0 degrees, QRS horizontal axis > 57.5 degrees, R-wave amplitude (lead I) <0.67 mV, R-wave duration (lead III) > 50.0 ms, S-wave amplitude (lead I) < -0.144 mV, S-wave duration (lead aVL) > 35.5 ms, QRS duration (lead V3) > 96.5 ms, QRS area in lead I < 0.75 Ashman units, ST slope (lead I) > 31.5 deg, T-wave area (lead V1) < -3.05 Ashman units and PR interval (lead V2) > 157 ms were significant predictors. A weighted score based on dichotomized values provided good predictive performance (hazard ratio: 1.59, 95% confidence interval: 1.27-2.00, P-value<0.0001, area under the curve: 0.84). Conclusions: Automated ECG analysis revealed novel risk markers in BrS. These markers should be validated in larger prospective studies.
Copyright © 2021 Tse, Lee, Li, Chang, Li, Zhou, Liu and Zhang.

Entities:  

Keywords:  Brugada syndrome; automated ECG; depolarization; repolarization; risk stratification

Year:  2021        PMID: 33521066      PMCID: PMC7840575          DOI: 10.3389/fcvm.2020.618254

Source DB:  PubMed          Journal:  Front Cardiovasc Med        ISSN: 2297-055X


  4 in total

1.  Territory-wide cohort study of Brugada syndrome in Hong Kong: predictors of long-term outcomes using random survival forests and non-negative matrix factorisation.

Authors:  Sharen Lee; Jiandong Zhou; Ka Hou Christien Li; Keith Sai Kit Leung; Ishan Lakhani; Tong Liu; Ian Chi Kei Wong; Ngai Shing Mok; Chloe Mak; Kamalan Jeevaratnam; Qingpeng Zhang; Gary Tse
Journal:  Open Heart       Date:  2021-02

2.  Automatic Detection for Multi-Labeled Cardiac Arrhythmia Based on Frame Blocking Preprocessing and Residual Networks.

Authors:  Zicong Li; Henggui Zhang
Journal:  Front Cardiovasc Med       Date:  2021-03-19

3.  Paediatric/young versus adult patients with long QT syndrome.

Authors:  Sharen Lee; Jiandong Zhou; Kamalan Jeevaratnam; Wing Tak Wong; Ian Chi Kei Wong; Chloe Mak; Ngai Shing Mok; Tong Liu; Qingpeng Zhang; Gary Tse
Journal:  Open Heart       Date:  2021-09

Review 4.  Pathogenesis and Management of Brugada Syndrome: Recent Advances and Protocol for Umbrella Reviews of Meta-Analyses in Major Arrhythmic Events Risk Stratification.

Authors:  Hasina Masha Aziz; Michał P Zarzecki; Sebastian Garcia-Zamora; Min Seo Kim; Piotr Bijak; Gary Tse; Hong-Hee Won; Paweł T Matusik
Journal:  J Clin Med       Date:  2022-03-30       Impact factor: 4.241

  4 in total

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