Wide complex tachycardia discrimination tool improves physicians' diagnostic accuracy

Kashou, Anthony H.; Noseworthy, Peter A.; Jentzer, Jacob C.; Rafie, Nikita; Roy, Alexandria R.; Abraham, Helayna; Sang, Philip D.; Kronzer, Ellen K.; Inglis, Sara S.; Rezkalla, Joshua; Julakanti, Raghav R.; Saric, Petar; Asirvatham, Samuel J.; Deshmukh, Abhishek; DeSimone, Christopher V.; May, Adam M.

doi:10.1016/j.jelectrocard.2022.07.070

Cited by 6 publications

(2 citation statements)

References 25 publications

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“…Several studies (Kashou et al, 2021; Kashou, DeSimone, Deshmukh, et al, 2020; May et al, 2019a, 2020) have introduced novel automated approaches to differentiate WCTs using computerized ECG interpretation software, each offering a compelling option that avoids the practical and diagnostic limitations inextricably associated with manual ECG interpretation methods. Recently, a separate analysis has confirmed that one automated WCT differentiation model (i.e., VT Prediction Model) improved users' diagnostic performance (Kashou et al, 2022). By design, automated methods transform computerized ECG data, which is routinely processed by ECG interpretation software programs, into novel parameters (e.g., frontal and horizontal PTVAC) that can be integrated into binary classification modeling techniques (i.e., LR).…”

Section: Discussionmentioning

confidence: 97%

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wideQRScomplex tachycardias

Kashou

LoCoco

Shaikh

et al. 2022

Noninvasive Electrocardiol

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Background Accurate automated wide QRS complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) can be accomplished using calculations derived from computerized electrocardiogram (ECG) data of paired WCT and baseline ECGs. Objective Develop and trial novel WCT differentiation approaches for patients with and without a corresponding baseline ECG. Methods We developed and trialed WCT differentiation models comprised of novel and previously described parameters derived from WCT and baseline ECG data. In Part 1, a derivation cohort was used to evaluate five different classification models: logistic regression (LR), artificial neural network (ANN), Random Forests [RF], support vector machine (SVM), and ensemble learning (EL). In Part 2, a separate validation cohort was used to prospectively evaluate the performance of two LR models using parameters generated from the WCT ECG alone (Solo Model) and paired WCT and baseline ECGs (Paired Model). Results Of the 421 patients of the derivation cohort (Part 1), a favorable area under the receiver operating characteristic curve (AUC) by all modeling subtypes: LR (0.96), ANN (0.96), RF (0.96), SVM (0.96), and EL (0.97). Of the 235 patients of the validation cohort (Part 2), the Solo Model and Paired Model achieved a favorable AUC for 103 patients with (Solo Model 0.87; Paired Model 0.95) and 132 patients without (Solo Model 0.84; Paired Model 0.95) a corroborating electrophysiology procedure or intracardiac device recording. Conclusion Accurate WCT differentiation may be accomplished using computerized data of (i) the WCT ECG alone and (ii) paired WCT and baseline ECGs.

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Section: Discussionmentioning

confidence: 97%

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wideQRScomplex tachycardias

Kashou

LoCoco

Shaikh

et al. 2022

Noninvasive Electrocardiol

Self Cite

View full text Add to dashboard Cite

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“…In addition, the VT Prediction Model successfully generated a continuum of VT probabilities with favorable diagnostic performance indices across various VT probability thresholds. More recently, a subsequent prospective analysis (Kashou, Noseworthy, Jentzer, et al, 2022), which evaluated the VT Prediction Model's application by physician trainees, showed that it favorably improved ECG interpretation accuracy and interpreter confidence for discriminating WCTs.…”

Section: Figurementioning

confidence: 99%

Mayo Clinic VT calculator: A practical tool for accurate wide complex tachycardia differentiation

Kashou,

LoCoco,

Gardner

et al. 2023

Noninvasive Electrocardiol

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The discrimination of ventricular tachycardia (VT) versus supraventricular wide complex tachycardia (SWCT) via 12‐lead electrocardiogram (ECG) is crucial for achieving appropriate, high‐quality, and cost‐effective care in patients presenting with wide QRS complex tachycardia (WCT). Decades of rigorous research have brought forth an expanding arsenal of applicable manual algorithm methods for differentiating WCTs. However, these algorithms are limited by their heavy reliance on the ECG interpreter for their proper execution. Herein, we introduce the Mayo Clinic ventricular tachycardia calculator (MC‐VTcalc) as a novel generalizable, accurate, and easy‐to‐use means to estimate VT probability independent of ECG interpreter competency. The MC‐VTcalc, through the use of web‐based and mobile device platforms, only requires the entry of computerized measurements (i.e., QRS duration, QRS axis, and T‐wave axis) that are routinely displayed on standard 12‐lead ECG recordings.

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Interpreting Wide-Complex Tachycardia With the Use of Artificial Intelligence

Chow,

Fayyazifar,

Balamane

et al. 2024

Canadian Journal of Cardiology

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Wide complex tachycardia discrimination tool improves physicians' diagnostic accuracy

Cited by 6 publications

References 25 publications

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wideQRScomplex tachycardias

Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wideQRScomplex tachycardias

Mayo Clinic VT calculator: A practical tool for accurate wide complex tachycardia differentiation

Interpreting Wide-Complex Tachycardia With the Use of Artificial Intelligence

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