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Study on the feasibility of distinguishing ventricular and pre-excited arrhythmia rhythms by a new algorithm
Honglin Ni1, Qingcheng Wang2, Zhiyong Wang1
1Jiaxing First Hospital, Affiliated Hospital of Jiaxing University, Jiaxing 314000, Zhejiang, China.
Background:
The differentiation and diagnosis of ventricular tachycardia (VT) and pre-excited tachycardia (PXT) remains a challenging task, especially when typical AV dissociation is not present. The purpose of this article is to study the feasibility of a new theoretical algorithm for identifying ventricular arrhythmias (VA) and pre-excited arrhythmias (PA) rhythms (which can be used to distinguish VT from PXT, etc.).
Method:
This study involved the deduction of a new algorithm by combining knowledge of cardiac anatomy, vectorcardiography, and cardiac electrophysiology. The new algorithm evaluated the diagnostic value through intracardiac electrophysiology in 205 cases of VA and PA. The new algorithm diagnoses VA based on the following 4-step process: If none of these criteria are met, the diagnosis is PA. The diagnostic value of the new algorithm is compared with the Steurer algorithm and the Vereckei algorithm (diagnosed based on the QRS waveform characteristics of the two algorithms during electrophysiological verification, excluding the diagnosis of atrioventricular dissociation).
Results:
The new algorithm showed significant advantages in terms of AUC value (0.83 vs. 0.61 vs. 0.57), sensitivity (83.6 % vs. 23.3 % vs. 24.8 %), and accuracy (82.9 % vs. 48.3 % vs. 46.3 %) compared to the Steurer algorithm and Vereckei algorithm based on QRS waveform characteristics for diagnosing VA (137 cases) and PA (68 cases). This indicates that the new algorithm is more accurate in identifying idiopathic VA. While there was a significant difference in specificity between the New algorithm and Steurer algorithm (82.3 % vs. 98.5 %, p < 0.05), the difference with Vereckei algorithm (82.3 % vs. 89.7 %) was not significant. In the New algorithm, the sensitivity and specificity for each step are as follows: Step 1 had the highest AUC value, indicating the best overall diagnostic performance among all steps. Step 2 and Step 3 also performed well, while Step 4 had relatively poorer diagnostic performance.
Conclusion:
The new algorithm is suitable for identifying the origin of VA and PA rhythms.
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