Department of Cardiology, The Second People's Hospital of Lianyungang, Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China.
Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
BMJ Open. 2023 Jul 24;13(7):e069273. doi: 10.1136/bmjopen-2022-069273.
Several ECG-based algorithms have been proposed to enhance the effectiveness of distinguishing Wide QRS complex tachycardia (WCT), but a comprehensive comparison of their accuracy is still lacking. This meta-analysis aimed to assess the diagnostic precision of various non-artificial intelligence ECG-based algorithms for WCT.
Systematic review with meta-analysis.
Electronic databases (PubMed, MEDLINE, the Cochrane Library, and Web of Science) are searched up to May 2022.
All studies reporting the diagnostic accuracy of different ECG-based algorithms for WCT are included. The risk of bias in included studies is assessed using the Cochrane Collaboration's risk of bias tools.
Two independent reviewers extracted data and assessed risk of bias. Data were pooled using random-effects model and expressed as mean differences with 95% CIs. Heterogeneity was calculated by the I method. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied to assess the internal validity of the diagnostic studies.
In total, 467 studies were identified, and 14 studies comprising 3966 patients were included, involving four assessable ECG-based algorithms: the Brugada algorithm, Vereckei-pre algorithm, Vereckei-aVR algorithm and R wave peak time of lead II (RWPT-II) algorithm. The overall sensitivity was 88.89% (95% CI: 85.03 to 91.86), with a specificity of 70.55% (95% CI: 62.10 to 77.79) and a diagnostic OR (DOR) of 19.17 (95% CI: 11.45 to 32.10). Heterogeneity of the DOR was 89.1%. The summary sensitivity of each algorithm was Brugada 90.25%, Vereckei-pre 94.80%, Vereckei-aVR 90.35% and RWPT-II 78.15%; the summary specificity was Brugada 64.02%, Vereckei-pre 75.40%, Vereckei-aVR 60.88% and RWPT-II 88.30% and the summary DOR was Brugada 16.48, Vereckei-pre 60.70, Vereckei-aVR 14.57 and RWPT-II 27.00.
ECG-based algorithms exhibit high sensitivity and moderate specificity in diagnosing WCT. A combination of Brugada or Vereckei-aVR algorithm with RWPT-II could be considered to diagnose WCT.
CRD42022344996.
已经提出了几种基于心电图的算法来提高鉴别宽 QRS 心动过速(WCT)的有效性,但它们的准确性仍缺乏全面比较。本荟萃分析旨在评估各种基于非人工智能的心电图算法在 WCT 中的诊断精度。
系统评价与荟萃分析。
截至 2022 年 5 月,检索电子数据库(PubMed、MEDLINE、Cochrane 图书馆和 Web of Science)。
纳入报告不同基于心电图的算法用于 WCT 的诊断准确性的所有研究。使用 Cochrane 协作风险偏倚工具评估纳入研究的风险偏倚。
两名独立评审员提取数据并评估风险偏倚。使用随机效应模型汇总数据,并以 95%置信区间(CI)表示均值差异。采用 I ² 方法计算异质性。应用诊断准确性研究的质量评估工具(QUADAS-2)评估诊断研究的内部有效性。
共确定了 467 项研究,纳入了 14 项研究,共 3966 例患者,涉及四种可评估的基于心电图的算法:Brugada 算法、Vereckei-pre 算法、Vereckei-aVR 算法和 II 导联 R 波峰时间(RWPT-II)算法。总体敏感性为 88.89%(95%CI:85.03 至 91.86),特异性为 70.55%(95%CI:62.10 至 77.79),诊断比值比(DOR)为 19.17(95%CI:11.45 至 32.10)。DOR 的异质性为 89.1%。每个算法的汇总敏感性分别为 Brugada 90.25%、Vereckei-pre 94.80%、Vereckei-aVR 90.35%和 RWPT-II 78.15%;汇总特异性分别为 Brugada 64.02%、Vereckei-pre 75.40%、Vereckei-aVR 60.88%和 RWPT-II 88.30%;汇总 DOR 分别为 Brugada 16.48、Vereckei-pre 60.70、Vereckei-aVR 14.57 和 RWPT-II 27.00。
基于心电图的算法在诊断 WCT 方面具有较高的敏感性和中等特异性。Brugada 或 Vereckei-aVR 算法与 RWPT-II 的结合可用于诊断 WCT。
CRD42022344996。