Gomes Daniel A, Lambiase Pier D, Schilling Richard J, Cappato Riccardo, Adragão Pedro, Providência Rui
Department of Cardiology, Hospital de Santa Cruz, Lisbon, Portugal.
Institute of Cardiovascular Science, University College London, London, UK.
Europace. 2025 May 7;27(5). doi: 10.1093/europace/euaf091.
Despite several risk models to predict major arrhythmic events (MAE) in Brugada syndrome (BrS) having been developed, reproducibility and methodology remain a concern. Our aim was to assess the quality of model development and validation, and determine the discriminative performance of available models.
Electronic databases (Medline, Embase, and Central) were searched through September/2024 for studies developing or validating multivariable prediction models for MAE in BrS. Methodological quality and risk of bias (RoB) were assessed using the Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS) checklist and the Prediction Model Risk of Bias Assessment (PROBAST) Tool. Pooled random-effects c-statistics were obtained for each model. A total of 16 studies, including 11 unique multivariable scores, were included. All models had domains classified as high RoB. Common sources of bias were inappropriate inclusion/exclusion criteria, predictor selection, low number of events and underreporting of performance measures. Pooled c-statistics among patients without previous MAE showed good performance for Brugada-Risk [AUC 0.81, 95% confidence interval (CI) 0.71-0.91; I2 64%; three studies], fair for PAT (AUC 0.79, 95% CI 0.45-1.12; I2 95%; two studies), Delise (AUC 0.77, 95% CI 0.72-0.81, I2 39%, three studies), and Sieira (AUC 0.73, 95% CI 0.64-0.82; I2 64%; five studies), and moderate for Shanghai (AUC 0.69, 95% CI 0.61-0,76; I2 13%; three studies).
Currently available multiparametric models for prediction of MAE in BrS have important shortcomings in model development and inadequate evaluation. Further validation of current models in external cohorts is required before safe transition to clinical practice.
尽管已经开发了几种预测 Brugada 综合征（BrS）主要心律失常事件（MAE）的风险模型，但可重复性和方法学仍然是一个问题。我们的目的是评估模型开发和验证的质量，并确定现有模型的判别性能。
检索截至 2024 年 9 月的电子数据库（Medline、Embase 和 Central），以查找开发或验证 BrS 中 MAE 的多变量预测模型的研究。使用预测模型研究系统评价的关键评估和数据提取（CHARMS）清单和预测模型偏倚风险评估（PROBAST）工具评估方法学质量和偏倚风险（RoB）。为每个模型获得合并的随机效应 c 统计量。共纳入 16 项研究，包括 11 个独特的多变量评分。所有模型的领域均被归类为高 RoB。常见的偏倚来源包括不适当的纳入/排除标准、预测变量选择、事件数量少和性能指标报告不足。在没有既往 MAE 的患者中，合并的 c 统计量显示 Brugada-Risk 表现良好 [AUC 0.81，95%置信区间（CI）0.71-0.91；I2 64%；三项研究]，PAT 表现一般（AUC 0.79，95%CI 0.45-1.12；I2 95%；两项研究），Delise 表现一般（AUC 0.77，95%CI 0.72-0.81，I2 39%，三项研究），Sieira 表现一般（AUC 0.73，95%CI 0.64-0.82；I2 64%；五项研究），上海模型表现中等（AUC 0.69，95%CI 0.61-0.76；I2 13%；三项研究）。
目前可用的用于预测 BrS 中 MAE 的多参数模型在模型开发方面存在重要缺陷，评估不足。在安全过渡到临床实践之前，需要在外部队列中对当前模型进行进一步验证。
