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Machine learning for distinguishing right from left premature ventricular contraction origin using surface electrocardiogram features

  • Author Footnotes
    1 Wei Zhao, MS, Rui Zhu, MS, and Jian Zhang, MS, have contributed equally to this work.
    Wei Zhao
    Footnotes
    1 Wei Zhao, MS, Rui Zhu, MS, and Jian Zhang, MS, have contributed equally to this work.
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
    Search for articles by this author
  • Author Footnotes
    1 Wei Zhao, MS, Rui Zhu, MS, and Jian Zhang, MS, have contributed equally to this work.
    Rui Zhu
    Footnotes
    1 Wei Zhao, MS, Rui Zhu, MS, and Jian Zhang, MS, have contributed equally to this work.
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
    Search for articles by this author
  • Author Footnotes
    1 Wei Zhao, MS, Rui Zhu, MS, and Jian Zhang, MS, have contributed equally to this work.
    Jian Zhang
    Footnotes
    1 Wei Zhao, MS, Rui Zhu, MS, and Jian Zhang, MS, have contributed equally to this work.
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
    Search for articles by this author
  • Yangming Mao
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Hongwu Chen
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
    Search for articles by this author
  • Weizhu Ju
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Mingfang Li
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Gang Yang
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Kai Gu
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Zidun Wang
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Hailei Liu
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Jiaojiao Shi
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Xiaohong Jiang
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Pipin Kojodjojo
    Affiliations
    Department of Cardiology, National University Heart Centre, Singapore
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  • Minglong Chen
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
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  • Fengxiang Zhang
    Correspondence
    Address reprint requests and correspondence: Dr Fengxiang Zhang, Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital of Nanjing Medical University, Guangzhou Road 300, Nanjing, 210029, PR China.
    Affiliations
    Section of Pacing and Electrophysiology, Division of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Nanjing, China
    Search for articles by this author
  • Author Footnotes
    1 Wei Zhao, MS, Rui Zhu, MS, and Jian Zhang, MS, have contributed equally to this work.

      BACKGROUND

      Precise localization of the site of origin of premature ventricular contractions (PVCs) before ablation can facilitate the planning and execution of the electrophysiological procedure.

      OBJECTIVE

      The purpose of this study was to develop a predictive model that can be used to differentiate PVCs between the left ventricular outflow tract and right ventricular outflow tract (RVOT) using surface electrocardiogram characteristics.

      METHODS

      A total of 851 patients undergoing radiofrequency ablation of premature ventricular beats from January 2015 to March 2022 were enrolled. Ninety-two patients were excluded. The other 759 patients were enrolled into the development (n = 605), external validation (n = 104), or prospective cohort (n = 50). The development cohort consisted of the training group (n = 423) and the internal validation group (n = 182). Machine learning algorithms were used to construct predictive models for the origin of PVCs using body surface electrocardiogram features.

      RESULTS

      In the development cohort, the Random Forest model showed a maximum receiver operating characteristic curve area of 0.96. In the external validation cohort, the Random Forest model surpasses 4 reported algorithms in predicting performance (accuracy 94.23%; sensitivity 97.10%; specificity 88.57%). In the prospective cohort, the Random Forest model showed good performance (accuracy 94.00%; sensitivity 85.71%; specificity 97.22%).

      CONCLUSION

      Random Forest algorithm has improved the accuracy of distinguishing the origin of PVCs, which surpasses 4 previous standards, and would be used to identify the origin of PVCs before the interventional procedure.

      KEYWORDS

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      References

        • Morady F.
        • Kadish A.H.
        • DiCarlo L.
        • et al.
        Long-term results of catheter ablation of idiopathic right ventricular tachycardia.
        Circulation. 1990; 82: 2093-2099
        • Hellestrand K.J.
        • Whalley D.W.
        Radiofrequency catheter ablation of left ventricular tachycardia in the normal heart.
        Aust N Z J Med. 1996; 26: 380-385
        • Coggins D.L.
        • Lee R.J.
        • Sweeney J.
        • et al.
        Radiofrequency catheter ablation as a cure for idiopathic tachycardia of both left and right ventricular origin.
        J Am Coll Cardiol. 1994; 23: 1333-1341
        • Ito S.
        • Tada H.
        • Naito S.
        • et al.
        Development and validation of an ECG algorithm for identifying the optimal ablation site for idiopathic ventricular outflow tract tachycardia.
        J Cardiovasc Electrophysiol. 2003; 14: 1280-1286
        • Kim R.J.
        • Iwai S.
        • Markowitz S.M.
        • Shah B.K.
        • Stein K.M.
        • Lerman B.B.
        Clinical and electrophysiological spectrum of idiopathic ventricular outflow tract arrhythmias.
        J Am Coll Cardiol. 2007; 49: 2035-2043
        • Betensky B.P.
        • Park R.E.
        • Marchlinski F.E.
        • et al.
        The V(2) transition ratio: a new electrocardiographic criterion for distinguishing left from right ventricular outflow tract tachycardia origin.
        J Am Coll Cardiol. 2011; 57: 2255-2262
        • Kamakura S.
        • Shimizu W.
        • Matsuo K.
        • et al.
        Localization of optimal ablation site of idiopathic ventricular tachycardia from the right and left ventricular outflow tract by body surface ECG.
        Circulation. 1998; 98: 1525-1533
        • Samad M.D.
        • Ulloa A.
        • Wehner G.J.
        • et al.
        Predicting survival from large echocardiography and electronic health record datasets: optimization with machine learning.
        JACC Cardiovasc Imaging. 2019; 12: 681-689
        • Ambale-Venkatesh B.
        • Yang X.
        • Wu C.O.
        • et al.
        Cardiovascular event prediction by machine learning: The Multi-Ethnic Study of Atherosclerosis.
        Circ Res. 2017; 121: 1092-1101
        • Singal A.G.
        • Mukherjee A.
        • Elmunzer B.J.
        • et al.
        Machine learning algorithms outperform conventional regression models in predicting development of hepatocellular carcinoma.
        Am J Gastroenterol. 2013; 108: 1723-1730
        • Cheng D.
        • Ju W.
        • Zhu L.
        • et al.
        V3R/V7 index: a novel electrocardiographic criterion for differentiating left from right ventricular outflow tract arrhythmias origins.
        Circ Arrhythm Electrophysiol. 2018; 11e006243
        • Narula S.
        • Shameer K.
        • Salem Omar A.M.
        • Dudley J.T.
        • Sengupta P.P.
        Machine-learning algorithms to automate morphological and functional assessments in 2D echocardiography.
        J Am Coll Cardiol. 2016; 68: 2287-2295
        • Li Y.
        • Xie P.
        • Lu L.
        • et al.
        An integrated bioinformatics platform for investigating the human E3 ubiquitin ligase-substrate interaction network.
        Nat Commun. 2017; 8: 347
        • Filli L.
        • Rosskopf A.B.
        • Sutter R.
        • Fucentese S.F.
        • Pfirrmann C.W.A.
        MRI predictors of posterolateral corner instability: a decision tree analysis of patients with acute anterior cruciate ligament tear.
        Radiology. 2018; 289: 170-180
        • Fabris F.
        • Doherty A.
        • Palmer D.
        • de Magalhães J.P.
        • Freitas A.A.
        A new approach for interpreting Random Forest models and its application to the biology of ageing.
        Bioinformatics. 2018; 34: 2449-2456
        • Mall R.
        • Cerulo L.
        • Garofano L.
        • et al.
        RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes.
        Nucleic Acids Res. 2018; 46: e39
        • Bertsimas D.
        • Kallus N.
        • Weinstein A.M.
        • Zhuo Y.D.
        Personalized diabetes management using electronic medical records.
        Diabetes Care. 2017; 40: 210-217
        • Mao Z.
        • Xia M.
        • Jiang B.
        • Xu D.
        • Shi P.
        Incipient fault diagnosis for high-speed train traction systems via stacked generalization.
        IEEE Trans Cybern. 2022; 52: 7624-7633
        • Heo J.
        • Park S.J.
        • Kang S.H.
        • Oh C.W.
        • Bang J.S.
        • Kim T.
        Prediction of intracranial aneurysm risk using machine learning.
        Sci Rep. 2020; 10: 6921
        • Yoshida N.
        • Inden Y.
        • Uchikawa T.
        • et al.
        Novel transitional zone index allows more accurate differentiation between idiopathic right ventricular outflow tract and aortic sinus cusp ventricular arrhythmias.
        Heart Rhythm. 2011; 8: 349-356
        • Yoshida N.
        • Yamada T.
        • McElderry H.T.
        • et al.
        A novel electrocardiographic criterion for differentiating left from right ventricular outflow tract tachycardia origin: the V2S/V3R index.
        J Cardiovasc Electrophysiol. 2014; 25: 747-753
        • Ouyang F.
        • Fotuhi P.
        • Ho S.Y.
        • et al.
        Repetitive monomorphic ventricular tachycardia originating from the aortic sinus cusp: electrocardiographic characterization for guiding catheter ablation.
        J Am Coll Cardiol. 2002; 39: 500-508
        • Zhang F.
        • Chen M.
        • Yang B.
        • et al.
        Electrocardiographic algorithm to identify the optimal target ablation site for idiopathic right ventricular outflow tract ventricular premature contraction.
        Europace. 2009; 11: 1214-1220
        • Mookiah M.R.K.
        • Hogg S.
        • MacGillivray T.J.
        • et al.
        A review of machine learning methods for retinal blood vessel segmentation and artery/vein classification.
        Med Image Anal. 2021; 68101905
        • Yang L.
        • Wu H.
        • Jin X.
        • et al.
        Study of cardiovascular disease prediction model based on random forest in eastern China.
        Sci Rep. 2020; 10: 5245
        • 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; 48100662

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