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Kashou AH, LoCoco S, Gardner MR, Webb J, Jentzer JC, Noseworthy PA, DeSimone CV, Deshmukh AJ, Asirvatham SJ, May AM. Mayo Clinic VT calculator: A practical tool for accurate wide complex tachycardia differentiation. Ann Noninvasive Electrocardiol 2023; 28:e13085. [PMID: 37670480 PMCID: PMC10646384 DOI: 10.1111/anec.13085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Revised: 07/25/2023] [Accepted: 08/14/2023] [Indexed: 09/07/2023] Open
Abstract
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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Affiliation(s)
- Anthony H. Kashou
- Department of Cardiovascular MedicineMayo ClinicRochesterMinnesotaUSA
| | - Sarah LoCoco
- Department of MedicineWashington University School of MedicineSt. LouisMissouriUSA
| | | | - Jocelyn Webb
- Mayo Clinic Center for Digital HealthMayo ClinicRochesterMinnesotaUSA
| | - Jacob C. Jentzer
- Department of Cardiovascular MedicineMayo ClinicRochesterMinnesotaUSA
| | | | | | | | | | - Adam M. May
- Cardiovascular DivisionWashington University School of MedicineSt. LouisMissouriUSA
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Kashou AH, LoCoco S, Shaikh PA, Katbamna BB, Sehrawat O, Cooper DH, Sodhi SS, Cuculich PS, Gleva MJ, Deych E, Zhou R, Liu L, Deshmukh AJ, Asirvatham SJ, Noseworthy PA, DeSimone CV, May AM. Computerized electrocardiogram data transformation enables effective algorithmic differentiation of wide QRS complex tachycardias. Ann Noninvasive Electrocardiol 2022; 28:e13018. [PMID: 36409204 PMCID: PMC9833371 DOI: 10.1111/anec.13018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2022] [Revised: 10/16/2022] [Accepted: 10/19/2022] [Indexed: 11/23/2022] Open
Abstract
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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Affiliation(s)
- Anthony H. Kashou
- Department of Cardiovascular MedicineMayo ClinicMinnesotaRochesterUSA
| | - Sarah LoCoco
- Department of MedicineWashington University School of MedicineMissouriSt. LouisUSA
| | - Preet A. Shaikh
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Bhavesh B. Katbamna
- Department of MedicineWashington University School of MedicineMissouriSt. LouisUSA
| | - Ojasav Sehrawat
- Department of Cardiovascular MedicineMayo ClinicMinnesotaRochesterUSA
| | - Daniel H. Cooper
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Sandeep S. Sodhi
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Phillip S. Cuculich
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Marye J. Gleva
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
| | - Elena Deych
- Division of BiostatisticsWashington University School of MedicineMissouriSt. LouisUSA
| | - Ruiwen Zhou
- Division of BiostatisticsWashington University School of MedicineMissouriSt. LouisUSA
| | - Lei Liu
- Division of BiostatisticsWashington University School of MedicineMissouriSt. LouisUSA
| | | | | | | | | | - Adam M. May
- Department of Medicine, Division of Cardiovascular DiseasesWashington University School of MedicineMissouriSt. LouisUSA
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Kashou AH, Noseworthy PA, Jentzer JC, Rafie N, Roy AR, Abraham HM, Sang PD, Kronzer EK, Inglis SS, Rezkalla JA, Julakanti RR, Saric P, Asirvatham SJ, Deshmukh AJ, DeSimone CV, May AM. Wide complex tachycardia discrimination tool improves physicians' diagnostic accuracy. J Electrocardiol 2022; 74:32-39. [PMID: 35933848 PMCID: PMC9799284 DOI: 10.1016/j.jelectrocard.2022.07.070] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 07/07/2022] [Accepted: 07/23/2022] [Indexed: 12/31/2022]
Abstract
BACKGROUND Timely and accurate discrimination of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular WCT (SWCT) is critically important. Previously we developed and validated an automated VT Prediction Model that provides a VT probability estimate using the paired WCT and baseline 12-lead ECGs. Whether this model improves physicians' diagnostic accuracy has not been evaluated. OBJECTIVE We sought to determine whether the VT Prediction Model improves physicians' WCT differentiation accuracy. METHODS Over four consecutive days, nine physicians independently interpreted fifty WCT ECGs (25 VTs and 25 SWCTs confirmed by electrophysiological study) as either VT or SWCT. Day 1 used the WCT ECG only, Day 2 used the WCT and baseline ECG, Day 3 used the WCT ECG and the VT Prediction Model's estimation of VT probability, and Day 4 used the WCT ECG, baseline ECG, and the VT Prediction Model's estimation of VT probability. RESULTS Inclusion of the VT Prediction Model data increased diagnostic accuracy versus the WCT ECG alone (Day 3: 84.2% vs. Day 1: 68.7%, p 0.009) and WCT and baseline ECGs together (Day 3: 84.2% vs. Day 2: 76.4%, p 0.003). There was no further improvement of accuracy with addition of the baseline ECG comparison to the VT Prediction Model (Day 3: 84.2% vs. Day 4: 84.0%, p 0.928). Overall sensitivity (Day 3: 78.2% vs. Day 1: 67.6%, p 0.005) and specificity (Day 3: 90.2% vs. Day 1: 69.8%, p 0.016) for VT were superior after the addition of the VT Prediction Model. CONCLUSION The VT Prediction Model improves physician ECG diagnostic accuracy for discriminating WCTs.
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Affiliation(s)
- Anthony H. Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Jacob C. Jentzer
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota,Division of Pulmonary and Critical Care Medicine, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Nikita Rafie
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | | | | | - Philip D. Sang
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Ellen K. Kronzer
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Sara S. Inglis
- Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota
| | - Joshua A. Rezkalla
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | - Petar Saric
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota
| | | | | | | | - Adam M. May
- Department of Medicine, Washington University School of Medicine, St. Louis, Missouri
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Kashou AH, LoCoco S, McGill TD, Evenson CM, Deshmukh AJ, Hodge DO, Cooper DH, Sodhi SS, Cuculich PS, Asirvatham SJ, Noseworthy PA, DeSimone CV, May AM. Automatic wide complex tachycardia differentiation using mathematically synthesized vectorcardiogram signals. Ann Noninvasive Electrocardiol 2021; 27:e12890. [PMID: 34562325 PMCID: PMC8739609 DOI: 10.1111/anec.12890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 08/21/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND Automated wide complex tachycardia (WCT) differentiation into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) may be accomplished using novel calculations that quantify the extent of mean electrical vector changes between the WCT and baseline electrocardiogram (ECG). At present, it is unknown whether quantifying mean electrical vector changes within three orthogonal vectorcardiogram (VCG) leads (X, Y, and Z leads) can improve automated VT and SWCT classification. METHODS A derivation cohort of paired WCT and baseline ECGs was used to derive five logistic regression models: (i) one novel WCT differentiation model (i.e., VCG Model), (ii) three previously developed WCT differentiation models (i.e., WCT Formula, VT Prediction Model, and WCT Formula II), and (iii) one "all-inclusive" model (i.e., Hybrid Model). A separate validation cohort of paired WCT and baseline ECGs was used to trial and compare each model's performance. RESULTS The VCG Model, composed of WCT QRS duration, baseline QRS duration, absolute change in QRS duration, X-lead QRS amplitude change, Y-lead QRS amplitude change, and Z-lead QRS amplitude change, demonstrated effective WCT differentiation (area under the curve [AUC] 0.94) for the derivation cohort. For the validation cohort, the diagnostic performance of the VCG Model (AUC 0.94) was similar to that achieved by the WCT Formula (AUC 0.95), VT Prediction Model (AUC 0.91), WCT Formula II (AUC 0.94), and Hybrid Model (AUC 0.95). CONCLUSION Custom calculations derived from mathematically synthesized VCG signals may be used to formulate an effective means to differentiate WCTs automatically.
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Affiliation(s)
| | - Sarah LoCoco
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Trevon D McGill
- Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Christopher M Evenson
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Abhishek J Deshmukh
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - David O Hodge
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Daniel H Cooper
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Sandeep S Sodhi
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Phillip S Cuculich
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Samuel J Asirvatham
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
| | - Peter A Noseworthy
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Adam M May
- Division of Cardiovascular Diseases, Department of Medicine, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
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Abualsuod AM, Miller JM. Removing the complexity from wide complex tachycardia. Trends Cardiovasc Med 2021; 32:221-225. [PMID: 33838244 DOI: 10.1016/j.tcm.2021.04.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/03/2021] [Accepted: 04/04/2021] [Indexed: 12/13/2022]
Abstract
Correctly diagnosing the cause of wide QRS tachycardias remain an area of difficulty for many clinicians. The authors provide a concise update to the different ECG algorithms that have been developed as well as caveats in their application.
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Affiliation(s)
- Amjad M Abualsuod
- Department of Medicine, Krannert Institute of Cardiology, Indiana University School of Medicine, E-488 1800 N. Capitol Ave., Indianapolis, IN 46202, United States
| | - John M Miller
- Department of Medicine, Krannert Institute of Cardiology, Indiana University School of Medicine, E-488 1800 N. Capitol Ave., Indianapolis, IN 46202, United States.
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Kashou AH, Evenson CM, Noseworthy PA, Muralidharan TR, DeSimone CV, Deshmukh AJ, Asirvatham SJ, May AM. Differentiating wide complex tachycardias: A historical perspective. Indian Heart J 2020; 73:7-13. [PMID: 33714412 PMCID: PMC7961210 DOI: 10.1016/j.ihj.2020.09.006] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 09/03/2020] [Accepted: 09/10/2020] [Indexed: 11/02/2022] Open
Abstract
One of the most critical and challenging skills is the distinction of wide complex tachycardias into ventricular tachycardia or supraventricular wide complex tachycardia. Prompt and accurate differentiation of wide complex tachycardias naturally influences short- and long-term management decisions and may directly affect patient outcomes. Currently, there are many useful electrocardiographic criteria and algorithms designed to distinguish ventricular tachycardia and supraventricular wide complex tachycardia accurately; however, no single approach guarantees diagnostic certainty. In this review, we offer an in-depth analysis of available methods to differentiate wide complex tachycardias by retrospectively examining its rich literature base - one that spans several decades.
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Affiliation(s)
| | - Christopher M Evenson
- Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
| | | | - Thoddi R Muralidharan
- Department of Cardiology, Sri Ramachandra Medical Centre, Porur Chennai, Tamil Nadu, India
| | | | | | | | - Adam M May
- Cardiovascular Division, Washington University School of Medicine, St. Louis, MO, USA
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