Prognostic parameters predict cardiac events in Brugada syndrome: a meta-analysis

Brugada syndrome (BrS) is recognized as a cause of sudden cardiac death (SCD), ventricular tachycardia or ventricular fibrillation (VT/VF). However, the prognosis varies among the patients with Brugada electrocardiograph (ECG) pattern. Furthermore, there is no prognostic tool to predict the worsening cardiac outcomes in BrS.

Primary outcome was to assess the association between risks including patient history, ECG pattern, electrophysiology study (EPS) findings and cardiac events which is identified as VT/VF or SCD.

We performed a meta-analysis of prospective and retrospective cohort studies which assessed the risks and cardiac events in patients with BrS. Pooled hazard ratios (HRs) was analyzed by using Random-effect model.

A total of 27 studies involving 15,990 BrS patients. Of these, 17 were retrospective studies and 10 were prospective studies. In univariate analysis, the risks including male, aborted SCD, QRS duration≥120 msec, fragmented QRS (multiple spikes within the QRS complex in lead V1-V3), late potential (positive terminal filtered QRS complex of signal-averaged ECG), sinus node dysfunction or inducibility of ventricular tachyarrhythmias during EPS associated with a higher risk of cardiac events in BrS. In multivariate analysis, higher risks of cardiac events were associated in BrS patients with history of syncope (HR 2.65, 95% confidence interval (CI): 1.68-4.19, I2 = 54 %, p-value <0.001), family history of SCD (HR 2.64, 95%CI:1.39-5.01, I2 = 0%, p-value = 0.003), prior VF (HR 14.52, 95%CI: 3.36-62.84, I2 = 62%, p-value <0.001), type 1 Brugada ECG pattern (HR 2.07, 95%CI: 1.13-3.79, I2 = 0%, p-value = 0.018), spontaneous type 1 ECG pattern (HR 1.91, 95%CI: 1.01-3.61, I2 = 58%, p-value = 0.046), early repolarization pattern (HR 2.78, 95%CI: 1.9-4.07, I2 = 0%, p-value < 0.001) and history of atrial fibrillation before BrS diagnosis (HR 1.82, 95%CI: 1.03-3.23, I2 = 49%, p-value = 0.039) (Table 1).

Our findings suggest that patient history, ECG pattern, EPS findings can predict the cardiac events. Introducing a risk prediction model could aid in early management of BrS.