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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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LoCoco S, Kashou AH, Noseworthy PA, Cooper DH, Ghadban R, May AM. The emergence and destiny of automated methods to differentiate wide QRS complex tachycardias. J Electrocardiol 2023; 81:44-50. [PMID: 37517201 DOI: 10.1016/j.jelectrocard.2023.07.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 07/10/2023] [Accepted: 07/17/2023] [Indexed: 08/01/2023]
Abstract
Accurate differentiation of wide complex tachycardias (WCTs) into ventricular tachycardia (VT) or supraventricular wide complex tachycardia (SWCT) using non-invasive methods such as 12‑lead electrocardiogram (ECG) interpretation is crucial in clinical practice. Recent studies have demonstrated the potential for automated approaches utilizing computerized ECG interpretation software to achieve accurate WCT differentiation. In this review, we provide a comprehensive analysis of contemporary automated methods for VT and SWCT differentiation. Our objectives include: (i) presenting a general overview of the emergence of automated WCT differentiation methods, (ii) examining the role of machine learning techniques in automated WCT differentiation, (iii) reviewing the electrophysiology concepts leveraged existing automated algorithms, (iv) discussing recently developed automated WCT differentiation solutions, and (v) considering future directions that will enable the successful integration of automated methods into computerized ECG interpretation platforms.
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Affiliation(s)
- Sarah LoCoco
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America.
| | - Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America
| | - Daniel H Cooper
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America
| | - Rugheed Ghadban
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America
| | - Adam M May
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, 660 S. Euclid Ave, CB 8086, St. Louis, MO 63110, United States of America
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Sun X, Teng Y, Mu S, Wang Y, Chen H. Diagnostic accuracy of different ECG-based algorithms in wide QRS complex tachycardia: a systematic review and meta-analysis. BMJ Open 2023; 13:e069273. [PMID: 37487685 PMCID: PMC10373685 DOI: 10.1136/bmjopen-2022-069273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/26/2023] Open
Abstract
OBJECTIVE Several ECG-based algorithms have been proposed to enhance the effectiveness of distinguishing Wide QRS complex tachycardia (WCT), but a comprehensive comparison of their accuracy is still lacking. This meta-analysis aimed to assess the diagnostic precision of various non-artificial intelligence ECG-based algorithms for WCT. DESIGN Systematic review with meta-analysis. DATA SOURCES Electronic databases (PubMed, MEDLINE, the Cochrane Library, and Web of Science) are searched up to May 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES All studies reporting the diagnostic accuracy of different ECG-based algorithms for WCT are included. The risk of bias in included studies is assessed using the Cochrane Collaboration's risk of bias tools. DATA EXTRACTION AND SYNTHESIS Two independent reviewers extracted data and assessed risk of bias. Data were pooled using random-effects model and expressed as mean differences with 95% CIs. Heterogeneity was calculated by the I2 method. The Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was applied to assess the internal validity of the diagnostic studies. RESULTS In total, 467 studies were identified, and 14 studies comprising 3966 patients were included, involving four assessable ECG-based algorithms: the Brugada algorithm, Vereckei-pre algorithm, Vereckei-aVR algorithm and R wave peak time of lead II (RWPT-II) algorithm. The overall sensitivity was 88.89% (95% CI: 85.03 to 91.86), with a specificity of 70.55% (95% CI: 62.10 to 77.79) and a diagnostic OR (DOR) of 19.17 (95% CI: 11.45 to 32.10). Heterogeneity of the DOR was 89.1%. The summary sensitivity of each algorithm was Brugada 90.25%, Vereckei-pre 94.80%, Vereckei-aVR 90.35% and RWPT-II 78.15%; the summary specificity was Brugada 64.02%, Vereckei-pre 75.40%, Vereckei-aVR 60.88% and RWPT-II 88.30% and the summary DOR was Brugada 16.48, Vereckei-pre 60.70, Vereckei-aVR 14.57 and RWPT-II 27.00. CONCLUSIONS ECG-based algorithms exhibit high sensitivity and moderate specificity in diagnosing WCT. A combination of Brugada or Vereckei-aVR algorithm with RWPT-II could be considered to diagnose WCT. PROSPERO REGISTRATION NUMBER CRD42022344996.
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Affiliation(s)
- Xingxing Sun
- Department of Cardiology, The Second People's Hospital of Lianyungang, Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Yanling Teng
- Department of Cardiology, The First people's Hospital of Lianyungang, The First Affiliated Hospital of Kangda College of Nanjing Medical University, Lianyungang, China
| | - Shengnan Mu
- Department of Cardiology, The Second People's Hospital of Lianyungang, Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China
| | - Yilian Wang
- Department of Cardiology, The Second People's Hospital of Lianyungang, Affiliated to Kangda College of Nanjing Medical University, Lianyungang, China
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Hongwu Chen
- Department of Cardiology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
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Kashou AH, May AM, Noseworthy PA. Comparison of two artificial intelligence-augmented ECG approaches: Machine learning and deep learning. J Electrocardiol 2023; 79:75-80. [PMID: 36989954 DOI: 10.1016/j.jelectrocard.2023.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 02/24/2023] [Accepted: 03/10/2023] [Indexed: 03/17/2023]
Abstract
BACKGROUND Artificial intelligence-augmented ECG (AI-ECG) refers to the application of novel AI solutions for complex ECG interpretation tasks. A broad variety of AI-ECG approaches exist, each having differing advantages and limitations relating to their creation and application. PURPOSE To provide illustrative comparison of two general AI-ECG modeling approaches: machine learning (ML) and deep learning (DL). METHOD COMPARISON Two AI-ECG algorithms were developed to carry out two separate tasks using ML and DL, respectively. ML modeling techniques were used to create algorithms designed for automatic wide QRS complex tachycardia differentiation into ventricular tachycardia and supraventricular tachycardia. A DL algorithm was formulated for the task of comprehensive 12‑lead ECG interpretation. First, we describe the ML models for WCT differentiation, which rely upon expert domain knowledge to identify and formulate ECG features (e.g., percent monophasic time-voltage area [PMonoTVA]) that enable strong diagnostic performance. Second, we describe the DL method for comprehensive 12‑lead ECG interpretation, which relies upon the independent recognition and analysis of a virtually incalculable number of ECG features from a vast collection of standard 12‑lead ECGs. CONCLUSION We have showcased two different AI-ECG methods, namely ML and DL respectively. In doing so, we highlighted the strengths and weaknesses of each approach. It is essential for investigators to understand these differences when attempting to create and apply novel AI-ECG solutions.
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Affiliation(s)
- Anthony H Kashou
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
| | - Adam M May
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States of America.
| | - Peter A Noseworthy
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, United States of America.
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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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Ding WY, Mahida S. Wide complex tachycardia: differentiating ventricular tachycardia from supraventricular tachycardia. Heart 2021; 107:1995-2003. [PMID: 34035115 DOI: 10.1136/heartjnl-2020-316874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Affiliation(s)
- Wern Yew Ding
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK
| | - Saagar Mahida
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK
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9
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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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Evenson CM, Kashou AH, LoCoco S, DeSimone CV, Deshmukh AJ, Cuculich PS, Noseworthy PA, May AM. Conceptual and literature basis for wide complex tachycardia and baseline ECG comparison. J Electrocardiol 2021; 65:50-54. [PMID: 33503517 DOI: 10.1016/j.jelectrocard.2021.01.007] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 01/08/2021] [Accepted: 01/11/2021] [Indexed: 11/29/2022]
Abstract
Accurate wide QRS complex tachycardia (WCT) differentiation into either ventricular tachycardia or supraventricular wide complex tachycardia using 12‑lead electrocardiogram (ECG) interpretation is essential for diagnostic, therapeutic, and prognostic reasons. There is an ever-expanding variety of WCT differentiation methods and criteria available to clinicians. However, only a few make use of the diagnostic value of comparing the ECG during WCT to that of the patient's baseline ECG. Therefore, we highlight the conceptual rationale and scientific literature supporting the diagnostic value of WCT and baseline ECG comparison.
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Affiliation(s)
- Christopher M Evenson
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, USA
| | | | - Sarah LoCoco
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, USA
| | | | | | - Phillip S Cuculich
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, USA
| | | | - Adam M May
- Department of Medicine, Division of Cardiovascular Diseases, Washington University School of Medicine in St. Louis, USA.
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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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12
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Subramany S, Kattoor AJ, Kovelamudi S, Devabhaktuni S, Mehta JL, Vallurupalli S, Paydak H, Pothineni NVK. Utility of Inferior Lead Q-waveforms in diagnosing Ventricular Tachycardia. CLINICAL MEDICINE INSIGHTS-CARDIOLOGY 2020; 14:1179546820953416. [PMID: 32943967 PMCID: PMC7466884 DOI: 10.1177/1179546820953416] [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: 04/28/2020] [Accepted: 07/31/2020] [Indexed: 11/16/2022]
Abstract
Background Electrocardiogram (ECG) differentiation of wide complex tachycardia (WCT) into ventricular tachycardia (VT) and supraventricular tachycardia with aberration (SVT-A) is often challenging. Objective To determine if the presence of Q-waveforms (QS, Qr, QRs) in the inferior leads (II, III, aVF) can differentiate VT from SVT-A in a WCT compared to Brugada algorithm. We studied 2 inferior lead criteria namely QWC-A where all the inferior leads had a similar Q wave pattern and QWC-B where only lead aVF had a Q-waveform. Methods A total of 181 consecutive cases of WCT were identified, digitally separated into precordial leads and inferior leads and independently reviewed by 2 electrophysiologists. An electrocardiographic diagnosis of VT or SVT-A was assigned based on Brugada and inferior lead algorithms. Results were compared to the final clinical diagnosis. Results VT was the final clinical diagnosis in 24.9% of ECG cohort (45/181); 75.1% (136/181) were SVT-A. QWC-A and QWC-B had a high specificity (93.3% and 82.8%) and accuracy (78.2% and 71.0%), but low sensitivity (33.3% and 35.6%) in differentiating VT from SVT-A. The Brugada algorithm yielded a sensitivity of 82.2% and specificity of 68.4%. Area under the curve in ROC analysis was highest with Brugada algorithm (0.75, 95% CI 0.69-0.81) followed by QWC-A (0.63, 95% CI 0.56-0.70) and QWC-B (0.59, 95% CI 0.52-0.67). Conclusion QWC-A and QWC-B criteria had poor sensitivity but high specificity in diagnosing VT in patients presenting with WCT. Further research combining this simple criterion with other newer diagnostic algorithms can potentially improve the accuracy of the overall diagnostic algorithm.
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Affiliation(s)
- Swathi Subramany
- Division of Internal medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | | | - Swathi Kovelamudi
- Division of Cardiovascular medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Subodh Devabhaktuni
- Division of Cardiovascular medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Jawahar L Mehta
- Division of Cardiovascular medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Srikanth Vallurupalli
- Division of Cardiovascular medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Hakan Paydak
- Division of Cardiovascular medicine, University of Arkansas for Medical Sciences, Little Rock, AR, USA
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13
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Kashou AH, DeSimone CV, Deshmukh AJ, McGill TD, Hodge DO, Carter R, Cooper DH, Cuculich PS, Noheria A, Asirvatham SJ, Noseworthy PA, May AM. The WCT Formula II: An effective means to automatically differentiate wide complex tachycardias. J Electrocardiol 2020; 61:121-129. [DOI: 10.1016/j.jelectrocard.2020.05.004] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 05/01/2020] [Accepted: 05/09/2020] [Indexed: 10/24/2022]
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14
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Kashou AH, Noseworthy PA, DeSimone CV, Deshmukh AJ, Asirvatham SJ, May AM. Wide Complex Tachycardia Differentiation: A Reappraisal of the State-of-the-Art. J Am Heart Assoc 2020; 9:e016598. [PMID: 32427020 PMCID: PMC7428989 DOI: 10.1161/jaha.120.016598] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The primary goal of the initial ECG evaluation of every wide complex tachycardia is to determine whether the tachyarrhythmia has a ventricular or supraventricular origin. The answer to this question drives immediate patient care decisions, ensuing clinical workup, and long‐term management strategies. Thus, the importance of arriving at the correct diagnosis cannot be understated and has naturally spurred rigorous research, which has brought forth an ever‐expanding abundance of manually applied and automated methods to differentiate wide complex tachycardias. In this review, we provide an in‐depth analysis of traditional and more contemporary methods to differentiate ventricular tachycardia and supraventricular wide complex tachycardia. In doing so, we: (1) review hallmark wide complex tachycardia differentiation criteria, (2) examine the conceptual and structural design of standard wide complex tachycardia differentiation methods, (3) discuss practical limitations of manually applied ECG interpretation approaches, and (4) highlight recently formulated methods designed to differentiate ventricular tachycardia and supraventricular wide complex tachycardia automatically.
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Affiliation(s)
| | | | | | | | | | - Adam M May
- Cardiovascular Division Washington University School of Medicine St. Louis MO
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15
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McGill TD, Kashou AH, Deshmukh AJ, LoCoco S, May AM, DeSimone CV. Wide complex tachycardia differentiation: An examination of traditional and contemporary approaches. J Electrocardiol 2020; 60:203-208. [DOI: 10.1016/j.jelectrocard.2020.04.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 04/03/2020] [Accepted: 04/11/2020] [Indexed: 10/24/2022]
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16
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Kashou AH, DeSimone CV, Hodge DO, Carter R, Lin G, Asirvatham SJ, Noseworthy PA, Deshmukh AJ, May AM. The ventricular tachycardia prediction model: Derivation and validation data. Data Brief 2020; 30:105515. [PMID: 32382594 PMCID: PMC7200856 DOI: 10.1016/j.dib.2020.105515] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Revised: 03/10/2020] [Accepted: 03/24/2020] [Indexed: 11/23/2022] Open
Abstract
In a recent publication [1], we introduced and described a novel means (i.e. VT Prediction Model) to correctly categorize wide complex tachycardias (WCTs) into ventricular tachycardia (VT) and supraventricular wide complex tachycardia (SWCT) using routine measurements shown on electrocardiogram (ECG) paper recordings. In this article, we summarize data components relating to the derivation and validation of the VT Prediction Model.
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Affiliation(s)
| | | | - David O. Hodge
- Department of Department of Health Sciences Research, Mayo Clinic, United States
| | - Rickey Carter
- Department of Department of Health Sciences Research, Mayo Clinic, United States
| | - Grace Lin
- Department of Cardiovascular Diseases, Mayo Clinic, United States
| | | | | | | | - Adam M. May
- Department of Medicine, Division of Cardiovascular Diseases, Washington University in St. Louis, United States
- Corresponding author.
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17
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Camm AJ, Lip GYH, Schilling R, Calkins H, Steffel J. The year in cardiology: arrhythmias and pacing. Eur Heart J 2020; 41:619-625c. [PMID: 31901935 DOI: 10.1093/eurheartj/ehz931] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 12/03/2019] [Accepted: 12/30/2019] [Indexed: 12/12/2022] Open
Affiliation(s)
- A John Camm
- St. George's University of London, Cardiology Clinical Academic Group, Molecular & Clinical Sciences Institute, Cranmer Terrace, London, SW19 0RE, London, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool Heart & Chest Hospital, Liverpool, UK.,Aalborg Thrombosis Research Unit, Department of Clinical Medicine, Aalborg University, Aalborg, Denmark
| | | | - Hugh Calkins
- Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Jan Steffel
- Department of Cardiology, Division of Electrophysiology, University Hospital Zurich, Switzerland
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18
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May AM, DeSimone CV, Kashou AH, Sridhar H, Hodge DO, Carter R, Lin G, Asirvatham SJ, Noseworthy PA, Deshmukh AJ. The VT Prediction Model: A simplified means to differentiate wide complex tachycardias. J Cardiovasc Electrophysiol 2019; 31:185-195. [DOI: 10.1111/jce.14321] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2019] [Revised: 11/12/2019] [Accepted: 12/12/2019] [Indexed: 11/28/2022]
Affiliation(s)
- Adam M. May
- Division of Cardiovascular Disease, Department of MedicineWashington University in St. LouisSt. Louis Missouri
| | | | | | | | - David O. Hodge
- Department of Health Sciences ResearchMayo ClinicJacksonville Florida
| | - Rickey Carter
- Department of Health Sciences ResearchMayo ClinicJacksonville Florida
| | - Grace Lin
- Department of Cardiovascular DiseasesMayo ClinicRochester Minnesota
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19
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The Wide Complex Tachycardia Formula: Derivation and validation data. Data Brief 2019; 24:103924. [PMID: 31061863 PMCID: PMC6488762 DOI: 10.1016/j.dib.2019.103924] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2019] [Revised: 04/02/2019] [Accepted: 04/08/2019] [Indexed: 11/23/2022] Open
Abstract
A recent publication (May et al., 2019) introduced a novel means (i.e. WCT Formula) to automatically distinguish ventricular tachycardia and supraventricular wide complex tachycardia using modern-day computerized electrocardiogram software measurements. In this article, a summary of data components relating to the derivation and validation of the WCT Formula is presented.
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