{"title":"A machine learning approach to differentiate wide QRS tachycardia: distinguishing ventricular tachycardia from supraventricular tachycardia.","authors":"Zhen-Zhen Li, Wei Zhao, YangMing Mao, Dan Bo, QiuShi Chen, Pipin Kojodjojo, FengXiang Zhang","doi":"10.1007/s10840-024-01743-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachycardia (VT) and supraventricular tachycardia (SVT) have limited diagnostic capabilities.</p><p><strong>Methods: </strong>A total of 278 patients with WQCT from January 2010 to March 2022 were enrolled. The electrophysiological study confirmed SVT in 154 patients and VT in 65 ones. Two hundred nineteen WQCT 12-lead ECGs were randomly divided into development cohort (n = 165) and testing cohort (n = 54) data sets. The development cohort was split into a training group (n = 115) and an internal validation group (n = 50). Forty ECG features extracted from the 219 WQCT ECGs are fed into 9 iteratively trained ML algorithms. This novel ML algorithm was also compared with four published algorithms.</p><p><strong>Results: </strong>In the development cohort, the Gradient Boosting Machine (GBM) model displayed the maximum area under curve (AUC) (0.91, 95% confidence interval (CI) 0.81-1.00). In the testing cohort, the GBM model had a higher AUC of 0.97 compared to 4 validated ECG algorithms, namely, Brugada (0.68), avR (0.62), RWPTII (0.72), and LLA algorithms (0.70). Accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the GBM model were 0.94, 0.97, 0.90, 0.94, and 0.95, respectively.</p><p><strong>Conclusions: </strong>A GBM ML model contributes to distinguishing SVT from VT based on surface ECG features. In addition, we were able to identify important indicators for distinguishing WQCT.</p>","PeriodicalId":16202,"journal":{"name":"Journal of Interventional Cardiac Electrophysiology","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Interventional Cardiac Electrophysiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s10840-024-01743-9","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/22 0:00:00","PubModel":"Epub","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Differential diagnosis of wide QRS tachycardia (WQCT) has been a challenging issue. Published algorithms to distinguish ventricular tachycardia (VT) and supraventricular tachycardia (SVT) have limited diagnostic capabilities.
Methods: A total of 278 patients with WQCT from January 2010 to March 2022 were enrolled. The electrophysiological study confirmed SVT in 154 patients and VT in 65 ones. Two hundred nineteen WQCT 12-lead ECGs were randomly divided into development cohort (n = 165) and testing cohort (n = 54) data sets. The development cohort was split into a training group (n = 115) and an internal validation group (n = 50). Forty ECG features extracted from the 219 WQCT ECGs are fed into 9 iteratively trained ML algorithms. This novel ML algorithm was also compared with four published algorithms.
Results: In the development cohort, the Gradient Boosting Machine (GBM) model displayed the maximum area under curve (AUC) (0.91, 95% confidence interval (CI) 0.81-1.00). In the testing cohort, the GBM model had a higher AUC of 0.97 compared to 4 validated ECG algorithms, namely, Brugada (0.68), avR (0.62), RWPTII (0.72), and LLA algorithms (0.70). Accuracy, sensitivity, specificity, negative predictive value, and positive predictive value of the GBM model were 0.94, 0.97, 0.90, 0.94, and 0.95, respectively.
Conclusions: A GBM ML model contributes to distinguishing SVT from VT based on surface ECG features. In addition, we were able to identify important indicators for distinguishing WQCT.
期刊介绍:
The Journal of Interventional Cardiac Electrophysiology is an international publication devoted to fostering research in and development of interventional techniques and therapies for the management of cardiac arrhythmias. It is designed primarily to present original research studies and scholarly scientific reviews of basic and applied science and clinical research in this field. The Journal will adopt a multidisciplinary approach to link physical, experimental, and clinical sciences as applied to the development of and practice in interventional electrophysiology. The Journal will examine techniques ranging from molecular, chemical and pharmacologic therapies to device and ablation technology. Accordingly, original research in clinical, epidemiologic and basic science arenas will be considered for publication. Applied engineering or physical science studies pertaining to interventional electrophysiology will be encouraged. The Journal is committed to providing comprehensive and detailed treatment of major interventional therapies and innovative techniques in a structured and clinically relevant manner. It is directed at clinical practitioners and investigators in the rapidly growing field of interventional electrophysiology. The editorial staff and board reflect this bias and include noted international experts in this area with a wealth of expertise in basic and clinical investigation. Peer review of all submissions, conflict of interest guidelines and periodic editorial board review of all Journal policies have been established.