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.
Cohort (n=516)
Sí
Do machine learning techniques (NMF and RSF) improve risk stratification for spontaneous VT/VF in patients with Brugada syndrome compared to traditional Cox regression?
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.
Estimación del efecto: HR 24.0 (95% CI 1.21-479)
Tasa de eventos absoluta: 1.7% vs 0.01%
valor p: p=0.037
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.
Lee et al. (Mon,) 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.