A novel prediction model for risk stratification in patients with a type 1 Brugada ECG pattern

Background: Risk stratification in Brugada syndrome remains a controversial and unresolved clinical problem, especially in asymptomatic patients with a type 1 ECG pattern. The purpose of this study is to derive and validate a prediction model based on clinical and ECG parameters to effectively identify patients with a type 1 ECG pattern who are at high risk of major arrhythmic events (MAE)during follow-up. Methods: This study analysed data from 103 consecutive patients with Brugada Type 1 ECG pattern and no history of previous cardiac arrest. The prediction model was derived using logistic regression with MAE as the primary outcome, and patient demographic and electrocardiographic parameters as potential predictor variables. The model was externally validated in an independent cohort of 42 patients. Results: The final model (Brugada Risk Stratification [BRS]score)consisted of 4 independent predictors (1 point each)of MAE during follow-up (median 85.3 months): spontaneous type 1 pattern, QRS fragments in inferior leads≥3,S wave upslope duration ratio ≥ 0.8, and T peak – T end ≥ 100 ms. The BRS score (AUC = 0.95,95% CI 0.0.92–0.98)stratifies patients with a type 1 ECG pattern into low (BRS score ≤ 2)and high (BRS score ≥ 3)risk classes, with a class specific risk of MAE of 0–1.1% and 92.3–100% across the derivation and validation cohorts, respectively. Conclusions: The BRS score is a simple bed-side tool with high predictive accuracy, for risk stratification of patients with a Brugada Type 1 ECG pattern. Prospective validation of the prediction model is necessary before this score can be implemented in clinical practice.