Why the study?
Accurate differentiation of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia using conventional manual 12-lead ECG interpretation is difficult.
Does the VT Prediction Model using automated computerized ECG measurements accurately differentiate ventricular tachycardia from supraventricular wide complex tachycardia?
Does the VT Prediction Model using automated computerized ECG measurements accurately differentiate ventricular tachycardia from supraventricular wide complex tachycardia?
A simplified logistic regression model using routinely available automated computerized ECG measurements can effectively differentiate ventricular tachycardia from supraventricular wide complex tachycardia.
BACKGROUND: The accurate separation of undifferentiated wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using conventional, manually-applied 12-lead electrocardiogram (ECG) interpretation methods is difficult. PURPOSE: We sought to devise a new WCT differentiation method that operates solely on automated measurements routinely provided by computerized ECG interpretation software. METHODS: In a two-part analysis, we developed and validated a logistic regression model (ie, VT Prediction Model) that utilizes routinely available computerized measurements derived from patients' paired WCT and baseline ECGs. RESULTS: The derivation cohort consisted of 601 paired WCT (273 VT, 328 SWCT) and baseline ECGs from 421 patients. The VT Prediction Model, composed of WCT QRS duration (ms) (P < .0001), QRS duration change (ms) (P < .0001), QRS axis change (°) (P < .0001) and T axis change (°) (P < .0001), yielded effective VT and SWCT differentiation (area under the curve [AUC]: 0.924; confidence interval [CI]: 0.903-0.944) for the derivation cohort. The validation cohort comprised 241 paired WCT (97 VT, 144 SWCT) and baseline ECGs from 177 patients. The VT Prediction Model's implementation on the validation cohort yielded effective WCT differentiation (AUC: 0.900; CI: 0.862-0.939) with overall accuracy, sensitivity, and specificity of 85.0%, 80.4%, and 88.2%, respectively. CONCLUSION: The VT Prediction Model is an example of how readily available ECG measurements may be used to distinguish VT and SWCT effectively. Further study is needed to develop and refine newer WCT differentiation approaches that utilize computerized measurements provided by ECG interpretation software.
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May et al. (2019) studied this question.
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