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

Clinical risk stratification for sudden cardiac death (SCD) in hypertrophic cardiomyopathy (HC) employs rules derived from 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 fo r sustained ventricular tachycardia or fibrillation (VT/VF).

Source: The American Journal of Cardiology - February 26, 2019 Category: Cardiology Authors: Moumita Bhattacharya, Dai-Yin Lu, Shibani M. Kudchadkar, Gabriela Villarreal Greenland, Prasanth Lingamaneni, Celia P. Corona-Villalobos, Yufan Guan, Joseph E. Marine, Jeffrey E. Olgin, Stefan Zimmerman, Theodore P. Abraham, Hagit Shatkay, M. Roselle Abra Source Type: research