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

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.