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

Abstract

BackgroundMultiparametric models have shown better risk stratification in Brugada syndrome. Recently, these models have been validated in different populations.AimsTo perform a systematic review and meta-analysis of the predictive performance of three validated multiparametric models (Delise model, Sieria model, and Shanghai score).MethodsWe 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.ResultsSeven 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.ConclusionsThe 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.