Department of Cardiology, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Department of Geriatrics, West China Hospital, Sichuan University, Chengdu, Sichuan, China.
Can J Cardiol. 2019 Dec;35(12):1791-1799. doi: 10.1016/j.cjca.2019.05.004. Epub 2019 May 10.
We performed a systematic review and meta-analysis to assess the discrimination performance of the 2014 European Society of Cardiology (ESC) sudden cardiac death (SCD) risk-prediction model for hypertrophic cardiomyopathy (HCM).
We searched the PubMed, Embase and Web of Science, CNKI, WanFang, and China Biology Medicine disc (CBMdisc) databases for English and Chinese articles validating the risk model. The model's discrimination performance with cutoff points of 4% and 6% based on extracted information was calculated. The extracted C statistic and calculated area under the curve (AUC) with 95% confidence intervals (CIs) of all studies were weighted and summarized. Heterogeneity was quantified through I statistics; sensitivity analysis and publication bias were assessed with Egger's test.
We included 13 studies validating the model's usefulness. We concluded that the model has excellent specificity, although it has poor sensitivity when setting a recommended cutoff value of 6% for identifying high-risk patients with HCM. In addition, there was moderate discrimination value (global C statistic = 0.75, 95% CI, 0.67-0.83; cutoff point of ≥ 4%; AUC = 0.69, 95% CI, 0.62-0.75; cutoff point of ≥ 6%; AUC = 0.65, 95% CI, 0.59-0.72). Subgroup analysis by region showed slightly weaker predictive ability for North America. There was no significant publication bias (all P > 0.05); sensitivity analysis did not change the results significantly.
The 2014 ESC HCM SCD risk-prediction model has excellent specificity and poor sensitivity and has moderate discrimination performance. In addition, it may have lower prediction value for North America compared with other regions.
我们进行了一项系统评价和荟萃分析，以评估 2014 年欧洲心脏病学会（ESC）用于肥厚型心肌病（HCM）的心脏性猝死（SCD）风险预测模型的区分性能。
我们在 PubMed、Embase 和 Web of Science、CNKI、万方和中国生物医学文献数据库（CBMdisc）中搜索了验证风险模型的英文和中文文章。根据提取的信息计算了基于 4%和 6%截断点的模型区分性能。对所有研究的提取 C 统计量和计算的曲线下面积（AUC）及其 95%置信区间（CI）进行加权汇总。通过 I ²统计量量化异质性；Egger 检验评估敏感性分析和发表偏倚。
我们纳入了 13 项验证模型有用性的研究。我们得出结论，该模型具有出色的特异性，尽管当设定 6%的推荐截断值来识别 HCM 高危患者时，其敏感性较差。此外，该模型具有中等的区分价值（总体 C 统计量=0.75，95%CI，0.67-0.83；截断值≥4%；AUC=0.69，95%CI，0.62-0.75；截断值≥6%；AUC=0.65，95%CI，0.59-0.72）。按地区进行的亚组分析显示，北美地区的预测能力略弱。不存在显著的发表偏倚（均 P＞0.05）；敏感性分析并未显著改变结果。
2014 年 ESC HCM SCD 风险预测模型具有出色的特异性和较差的敏感性，具有中等的区分性能。此外，与其他地区相比，它可能对北美地区的预测价值较低。
