Observational studyOpen heart2021
Territory-wide cohort study of Brugada syndrome in Hong Kong: predictors of long-term outcomes using random survival forests and non-negative matrix factorisation.
Observational study in Open heart, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Risk Prediction for Sudden Cardiac Death in the General Population: A Systematic Review and Meta-Analysis.International journal of public health · 2024Pooled it
- A pipeline for developing deep learning prognostic prediction models in cardiac magnetic resonance image analysis.European heart journal. Digital health · 2026Article
- Risk stratification of major arrhythmia events in Japanese patients with Brugada syndrome using machine learning models.Heart rhythm O2 · 2025Article
- Epicardial Ablation for Arrhythmogenic Disorders in Patients with Brugada Syndrome.Biomedicines · 2024Review
- Secular trends of health care resource utilization and costs between Brugada syndrome and congenital long QT syndrome: A territory-wide study.Clinical cardiology · 2023Article
- Unmasking a Silent Killer and Understanding Sudden Cardiac Death in Brugada Syndrome: A Traditional Review.Cureus · 2023Review
- Ethnic and racial differences in Asian populations with ion channelopathies associated with sudden cardiac death.Frontiers in cardiovascular medicine · 2023Review
- Artificial Intelligence in Ventricular Arrhythmias and Sudden Death.Arrhythmia & electrophysiology review · 2023Review
- Clinical Characteristics, Genetic Basis and Healthcare Resource Utilisation and Costs in Patients with Catecholaminergic Polymorphic Ventricular Tachycardia: A Retrospective Cohort Study.Reviews in cardiovascular medicine · 2022Article
- A Territory-Wide Study of Arrhythmogenic Right Ventricular Cardiomyopathy Patients from Hong Kong.Reviews in cardiovascular medicine · 2022Article
- Pathogenesis and Management of Brugada Syndrome: Recent Advances and Protocol for Umbrella Reviews of Meta-Analyses in Major Arrhythmic Events Risk Stratification.Journal of clinical medicine · 2022Review
- Machine learning techniques for arrhythmic risk stratification: a review of the literature.International journal of arrhythmia · 2022Article
- Development of an Electronic Frailty Index for Predicting Mortality and Complications Analysis in Pulmonary Hypertension Using Random Survival Forest Model.Frontiers in cardiovascular medicine · 2022Article
- Syncope Prediction Scores in the Emergency Department.Current cardiology reviews · 2022Review
- Paediatric/young versus adult patients with long QT syndrome.Open heart · 2021Article
- Predictive scores for identifying patients with type 2 diabetes mellitus at risk of acute myocardial infarction and sudden cardiac death.Endocrinology, diabetes & metabolism · 2021Article
Corrections and comments
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesBrugada syndrome (BrS) is an ion channelopathy that predisposes affected patients to spontaneous ventricular tachycardia/fibrillation (VT/VF) and sudden cardiac death. The aim of this study is to examine the predictive factors of spontaneous VT/VF.
methodsThis was a territory-wide retrospective cohort study of patients diagnosed with BrS between 1997 and 2019. The primary outcome was spontaneous VT/VF. Cox regression was used to identify significant risk predictors. Non-linear interactions between variables (latent patterns) were extracted using non-negative matrix factorisation (NMF) and used as inputs into the random survival forest (RSF) model.
resultsThis study included 516 consecutive BrS patients (mean age of initial presentation=50±16 years, male=92%) with a median follow-up of 86 (IQR: 45-118) months. The cohort was divided into subgroups based on initial disease manifestation: asymptomatic (n=314), syncope (n=159) or VT/VF (n=41). Annualised event rates per person-year were 1.70%, 0.05% and 0.01% for the VT/VF, syncope and asymptomatic subgroups, respectively. Multivariate Cox regression analysis revealed initial presentation of VT/VF (HR=24.0, 95% CI=1.21 to 479, p=0.037) and SD of P-wave duration (HR=1.07, 95% CI=1.00 to 1.13, p=0.044) were significant predictors. The NMF-RSF showed the best predictive performance compared with RSF and Cox regression models (precision: 0.87 vs 0.83 vs. 0.76, recall: 0.89 vs. 0.85 vs 0.73, F1-score: 0.88 vs 0.84 vs 0.74).
conclusionsClinical history, electrocardiographic markers and investigation results provide important information for risk stratification. Machine learning techniques using NMF and RSF significantly improves overall risk stratification performance.
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