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Diagnostic accuracy of electrocardiogram algorithms for differentiating left from right outflow tract ventricular

Zhuoqiao He1,2, Ming Liu3, Pengxiang Ying1,2

  • 1Department of Cardiology, First Affiliated Hospital of Shantou University Medical College, Shantou, Guangdong, China.

Heart (British Cardiac Society)
|July 3, 2025
PubMed

Insights

The Weighted hybrid score ECG algorithm is the most accurate for distinguishing left (LVOT) from right (RVOT) ventricular outflow tract arrhythmias. This finding aids in selecting the best treatment strategy for patients with these heart rhythm disorders.

Area of Science:

  • Cardiology
  • Electrophysiology
  • Medical Diagnostics

Background:

  • Differentiating left (LVOT) and right (RVOT) ventricular outflow tract arrhythmias (OTVAs) pre-ablation is crucial for treatment planning.
  • Current electrocardiogram (ECG) algorithms for OTVA differentiation lack a clear consensus on reliability.
  • Accurate diagnosis impacts vascular access and therapeutic strategy decisions.

Purpose of the Study:

  • To systematically review and compare the diagnostic accuracy of available ECG algorithms for differentiating LVOT from RVOT arrhythmias.
  • To identify the most reliable ECG algorithm for clinical application in OTVA management.

Main Methods:

  • A systematic review and Bayesian network meta-analysis (NMA) of studies evaluating ECG algorithms for OTVA origin.
  • Searched MEDLINE, EMBASE, and Cochrane databases up to May 7, 2025.
  • Assessed study quality using the QUADAS-2 tool and estimated pooled diagnostic accuracy metrics.

Main Results:

  • Included 22 studies with 3483 patients (2706 RVOT, 777 LVOT) evaluating 21 ECG algorithms.
  • The 'Weighted hybrid score' algorithm demonstrated the highest diagnostic accuracy (S=21.2, DOR=275.8), with pooled sensitivity of 0.83 and specificity of 0.92.
  • The 'Earliest onset or peak/nadir in lead V2' algorithm showed the lowest diagnostic performance.

Conclusions:

  • The 'Weighted hybrid score' algorithm is the most accurate for differentiating LVOT from RVOT arrhythmias.
  • This algorithm is recommended for clinical use to guide treatment decisions in OTVA patients.
  • Improved diagnostic accuracy can optimize patient management and outcomes.
Abstract

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