Diagnostic accuracy of different ECG-based algorithms in wide QRS complex tachycardia: a systematic review and meta-analysis.

Abstract

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 I² 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.