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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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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, 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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Ching CK, Leong BSH, Nair P, Chan KC, Seow E, Lee F, Heng K, Sewa DW, Lim TW, Chong DTT, Yeo KK, Fong WK, Anantharaman V, Lim SH. Singapore Advanced Cardiac Life Support Guidelines 2021. Singapore Med J 2021; 62:390-403. [PMID: 35001112 PMCID: PMC8804484 DOI: 10.11622/smedj.2021109] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/18/2023]
Abstract
Advanced cardiac life support (ACLS) emphasises the use of advanced airway management and ventilation, circulatory support and the appropriate use of drugs in resuscitation, as well as the identification of reversible causes of cardiac arrest. Extracorporeal cardiopulmonary resuscitation and organ donation, as well as special circumstances including drowning, pulmonary embolism and pregnancy are addressed. Resuscitation does not end with ACLS but must continue in post-resuscitation care. ACLS also covers the recognition and management of unstable pre-arrest tachy- and bradydysrhythmias that may deteriorate further.
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Affiliation(s)
- Chi Keong Ching
- Department of Cardiology, National Heart Centre Singapore, Singapore
| | | | - Praseetha Nair
- Acute and Emergency Care Centre, Khoo Teck Puat Hospital, Singapore
| | - Kim Chai Chan
- Acute and Emergency Care Centre, Khoo Teck Puat Hospital, Singapore
| | - Eillyne Seow
- Acute and Emergency Care Centre, Khoo Teck Puat Hospital, Singapore
| | - Francis Lee
- Acute and Emergency Care Centre, Khoo Teck Puat Hospital, Singapore
| | - Kenneth Heng
- Emergency Medicine Department, Tan Tock Seng Hospital, Singapore
| | - Duu Wen Sewa
- Department of Respiratory Medicine, Singapore General Hospital, Singapore
| | - Toon Wei Lim
- Department of Cardiology, National University Hospital, Singapore
| | | | - Khung Keong Yeo
- Department of Cardiology, National Heart Centre Singapore, Singapore
| | - Wee Kim Fong
- Department of Anaesthesia, Tan Tock Seng Hospital, Singapore
| | | | - Swee Han Lim
- Department of Emergency Medicine, Singapore General Hospital, Singapore
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