Performance of Multiparametric Models in Patients With Brugada Syndrome: A Systematic Review and Meta-Analysis - Wei, Hui-ting; Liu, Wei; Ma, Yue-Rong; Chen, Shi (Frontiers in Cardiovascular Medicine, 2022)
Background: Multiparametric models have shown better risk stratification in Brugada syndrome. Recently, these models have been validated within different populations. Aims: This study is a systematic review and meta-analysis of the predictive performance of three multiparametric models (Delise model 2011, Sieria model 2017 and Shanghai score 2019) that have been validated. Methods: We searched PubMed, Embase, Medline, Web of Science, and Ovid for studies validating the risk multiparametric model. A Sieria score of >2 and a Shanghai score of ≥4 were considered higher risk. Performance estimates were summarized using a random effects model. Results: Seven studies were included. Study sample sizes ranged from 111 to 1613. The duration of follow-up ranged from 3.3 to 10.18 years. The pooled area under the area curve (AUC), sensitivity and specificity for the Sieria model were 0.71 (95% CI: 0.67-0.75), 57% (95% CI: 35-76) and 71% (95% CI: 62-79), respectively. The AUC for the Shanghai score ranged from 0.63 to 0.71, the sensitivity and specificity were between 68.97% to 90.67% and between 43.53% to 63.43%, respectively. The AUC for the Delise model ranged from 0.77 to 0.87; however, the optimal cut-off was not given. Conclusions: All three models displayed moderate discrimination performance. The Sieria model has poor sensitivity and moderate specificity, whereas the Shanghai score has poor specificity and moderate sensitivity.