A Machine Learning Approach to Differentiate Wide QRS Tachycardia: Distinguishing Ventricular Tachycardia from Supraventricular Tachycardia

Overview

Journal J Interv Card Electrophysiol

Publisher Springer

Date 2024 Jan 21

PMID 38246906

Authors

Zhen-Zhen Li

Wei Zhao

YangMing Mao

Dan Bo

Qiushi Chen

Pipin Kojodjojo

Fengxiang Zhang

Affiliations

Soon will be listed here.

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.

References

Brugada P, Brugada J, Mont L, Smeets J, Andries E . A new approach to the differential diagnosis of a regular tachycardia with a wide QRS complex. Circulation. 1991; 83(5):1649-59. DOI: 10.1161/01.cir.83.5.1649. View

Griffith M, Garratt C, MOUNSEY P, Camm A . Ventricular tachycardia as default diagnosis in broad complex tachycardia. Lancet. 1994; 343(8894):386-8. DOI: 10.1016/s0140-6736(94)91223-8. View

Vereckei A, Duray G, Szenasi G, Altemose G, Miller J . New algorithm using only lead aVR for differential diagnosis of wide QRS complex tachycardia. Heart Rhythm. 2008; 5(1):89-98. DOI: 10.1016/j.hrthm.2007.09.020. View

Pava L, Perafan P, Badiel M, Arango J, Mont L, Morillo C . R-wave peak time at DII: a new criterion for differentiating between wide complex QRS tachycardias. Heart Rhythm. 2010; 7(7):922-6. DOI: 10.1016/j.hrthm.2010.03.001. View

Chen Q, Xu J, Gianni C, Trivedi C, Della Rocca D, Bassiouny M . Simple electrocardiographic criteria for rapid identification of wide QRS complex tachycardia: The new limb lead algorithm. Heart Rhythm. 2019; 17(3):431-438. DOI: 10.1016/j.hrthm.2019.09.021. View

Jastrzebski M, Kukla P, Czarnecka D, Kawecka-Jaszcz K . Comparison of five electrocardiographic methods for differentiation of wide QRS-complex tachycardias. Europace. 2012; 14(8):1165-71. DOI: 10.1093/europace/eus015. View

Missel R, Gyawali P, Murkute J, Li Z, Zhou S, AbdelWahab A . A hybrid machine learning approach to localizing the origin of ventricular tachycardia using 12-lead electrocardiograms. Comput Biol Med. 2020; 126:104013. PMC: 7606703. DOI: 10.1016/j.compbiomed.2020.104013. View

Feeny A, Chung M, Madabhushi A, Attia Z, cikes M, Firouznia M . Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circ Arrhythm Electrophysiol. 2020; 13(8):e007952. PMC: 7808396. DOI: 10.1161/CIRCEP.119.007952. View

AlAref S, Anchouche K, Singh G, Slomka P, Kolli K, Kumar A . Clinical applications of machine learning in cardiovascular disease and its relevance to cardiac imaging. Eur Heart J. 2018; 40(24):1975-1986. DOI: 10.1093/eurheartj/ehy404. View

10.

Smole T, Zunkovic B, Piculin M, Kokalj E, Robnik-Sikonja M, Kukar M . A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy. Comput Biol Med. 2021; 135:104648. DOI: 10.1016/j.compbiomed.2021.104648. View

11.

Narula S, Shameer K, Omar A, Dudley J, Sengupta P . Machine-Learning Algorithms to Automate Morphological and Functional Assessments in 2D Echocardiography. J Am Coll Cardiol. 2016; 68(21):2287-2295. DOI: 10.1016/j.jacc.2016.08.062. View

12.

Li Y, Xie P, Lu L, Wang J, Diao L, Liu Z . An integrated bioinformatics platform for investigating the human E3 ubiquitin ligase-substrate interaction network. Nat Commun. 2017; 8(1):347. PMC: 5570908. DOI: 10.1038/s41467-017-00299-9. View

13.

Filli L, Rosskopf A, Sutter R, Fucentese S, Pfirrmann C . MRI Predictors of Posterolateral Corner Instability: A Decision Tree Analysis of Patients with Acute Anterior Cruciate Ligament Tear. Radiology. 2018; 289(1):170-180. DOI: 10.1148/radiol.2018180194. View

14.

Fabris F, Doherty A, Palmer D, de Magalhaes J, Freitas A . A new approach for interpreting Random Forest models and its application to the biology of ageing. Bioinformatics. 2018; 34(14):2449-2456. PMC: 6041990. DOI: 10.1093/bioinformatics/bty087. View

15.

Mall R, Cerulo L, Garofano L, Frattini V, Kunji K, Bensmail H . RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes. Nucleic Acids Res. 2018; 46(7):e39. PMC: 6283452. DOI: 10.1093/nar/gky015. View

16.

Bertsimas D, Kallus N, Weinstein A, Zhuo Y . Personalized Diabetes Management Using Electronic Medical Records. Diabetes Care. 2016; 40(2):210-217. DOI: 10.2337/dc16-0826. View

17.

Mookiah M, Hogg S, MacGillivray T, Prathiba V, Pradeepa R, Mohan V . A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification. Med Image Anal. 2021; 68:101905. DOI: 10.1016/j.media.2020.101905. View

18.

Tolios A, De Las Rivas J, Hovig E, Trouillas P, Scorilas A, Mohr T . Computational approaches in cancer multidrug resistance research: Identification of potential biomarkers, drug targets and drug-target interactions. Drug Resist Updat. 2020; 48:100662. DOI: 10.1016/j.drup.2019.100662. View