Key result
Initial VT/VF presentation predicts ~24-fold higher risk of spontaneous VT/VF in Brugada syndrome.
Why the study?
Brugada syndrome predisposes patients to spontaneous VT/VF and sudden cardiac death, but the predictive factors of spontaneous VT/VF required further examination.
Do machine learning techniques (NMF and RSF) improve risk stratification for spontaneous VT/VF in patients with Brugada syndrome compared to traditional Cox regression?
Cohort (n=516)
Yes
Do machine learning techniques (NMF and RSF) improve risk stratification for spontaneous VT/VF in patients with Brugada syndrome compared to traditional Cox regression?
Effect estimate: HR 24.0 (95% CI 1.21-479)
Absolute Event Rate: 1.7% vs 0.01%
p-value: p=0.037
Machine learning techniques using non-negative matrix factorisation and random survival forests significantly improve risk stratification for spontaneous VT/VF in patients with Brugada syndrome.
May aid VT/VF risk stratification in Brugada syndrome; leaves open whether machine learning outperforms Cox models prospectively.
OBJECTIVES: Brugada 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. METHODS: This 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. RESULTS: This 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). CONCLUSIONS: Clinical 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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Lee et al. (2021) conducted a cohort in Brugada syndrome (n=516). Initial presentation of VT/VF vs. Asymptomatic or syncope presentation was evaluated on Spontaneous ventricular tachycardia/fibrillation (VT/VF) (HR 24.0, 95% CI 1.21-479, p=0.037). Initial presentation of VT/VF strongly predicted spontaneous VT/VF in Brugada syndrome patients (HR 24.0; 95% CI 1.21-479; p=0.037), with machine learning models improving risk stratification.
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