Performance of Multiparametric Models in Patients With Brugada Syndrome: A Systematic Review and Meta-Analysis.

ABSTRACT

Background: Multiparametric models have shown better risk stratification in Brugada syndrome. Recently, these models have been validated in different populations. Aims: To perform a systematic review and meta-analysis of the predictive performance of three validated multiparametric models (Delise model, Sieria model, and Shanghai score). Methods: We searched PubMed, Embase, MEDLINE, Web of Science, and Ovid for studies validating the risk multiparametric model. A Sieria score > 2 and Shanghai score ≥ 4 were considered to indicate higher risk. Performance estimates were summarized using a random-effects model. Results: Seven studies were included, with sample sizes of 111-1,613. The follow-up duration was 3.3-10.18 years. The Sieria model had a pooled area under the curve (AUC), sensitivity, and specificity of 0.71 [95% confidence interval (CI) 0.67-0.75], 57% (95% CI 35-76), and 71% (95% CI 62-79), respectively. The Shanghai score had an AUC of 0.63-0.71, 68.97-90.67% sensitivity, and 43.53-63.43% specificity. The AUC of the Delise model was 0.77-0.87; however, the optimal cut-off was not identified. Conclusions: The three models exhibited moderate discriminatory ability for Brugada syndrome. The Sieria model has poor sensitivity and moderate specificity, whereas the Shanghai score has poor specificity and moderate sensitivity.