Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy (HCM-VAr-Risk Model).

Journal: The American journal of cardiology
PMID: 30952382

Abstract

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from American College of Cardiology Foundation/American Heart Association (ACCF/AHA) guidelines or the HCM Risk-SCD model (C-index ∼0.69), which utilize a few clinical variables. We assessed whether data-driven machine learning methods that consider a wider range of variables can effectively identify HC patients with ventricular arrhythmias (VAr) that lead to SCD. We scanned the electronic health records of 711 HC patients for sustained ventricular tachycardia or ventricular fibrillation. Patients with ventricular tachycardia or ventricular fibrillation (n = 61) were tagged as VAr cases and the remaining (n = 650) as non-VAr. The 2-sample ttest and information gain criterion were used to identify the most informative clinical variables that distinguish VAr from non-VAr; patient records were reduced to include only these variables. Data imbalance stemming from low number of VAr cases was addressed by applying a combination of over- and undersampling strategies. We trained and tested multiple classifiers under this sampling approach, showing effective classification. We evaluated 93 clinical variables, of which 22 proved predictive of VAr. The ensemble of logistic regression and naïve Bayes classifiers, trained based on these 22 variables and corrected for data imbalance, was most effective in separating VAr from non-VAr cases (sensitivity = 0.73, specificity = 0.76, C-index = 0.83). Our method (HCM-VAr-Risk Model) identified 12 new predictors of VAr, in addition to 10 established SCD predictors. In conclusion, this is the first application of machine learning for identifying HC patients with VAr, using clinical attributes. Our model demonstrates good performance (C-index) compared with currently employed SCD prediction algorithms, while addressing imbalance inherent in clinical data.

Authors

  • Moumita Bhattacharya
    Computational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, DE, USA. moumitab@udel.edu.
  • Dai-Yin Lu
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland; Division of Cardiology, Taipei Veterans General Hospital, Taipei, Taiwan; Institute of Public Health, National Yang-Ming University, Taipei, Taiwan.
  • Shibani M Kudchadkar
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland.
  • Gabriela Villarreal Greenland
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland; Division of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California.
  • Prasanth Lingamaneni
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland.
  • Celia P Corona-Villalobos
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland; Department of Radiology, Johns Hopkins University, Baltimore, Maryland.
  • Yufan Guan
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland.
  • Joseph E Marine
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland.
  • Jeffrey E Olgin
    Division of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California.
  • Stefan Zimmerman
    Department of Radiology, Johns Hopkins University, Baltimore, Maryland.
  • Theodore P Abraham
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland; Division of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California.
  • Hagit Shatkay
    Computational Biomedicine Lab, Computer and Information Sciences, University of Delaware, Newark, DE, USA.
  • Maria Roselle Abraham
    Hypertrophic Cardiomyopathy Center of Excellence, Johns Hopkins University, Baltimore, Maryland; Division of Cardiology, Hypertrophic Cardiomyopathy Center of Excellence, University of California San Francisco, San Francisco, California. Electronic address: Roselle.Abraham@ucsf.edu.

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