Circulation: Arrhythmia And Electrophysiology On The Beat

Paul J. Wang: Welcome to the monthly podcast, On the BEAT, for Circulation: Arrhythmia and Electrophysiology. I'm Dr. Paul Wang, Editor in Chief, with some of the key highlights from this month's issue. Albert Feeny and Associates used unsupervised machine learning of electrocardiogram [ECG] waveforms to identify cardiac resynchronization therapy [CRT] subgroups to differentiate outcomes beyond QRS duration and left bundle branch block. They retrospectively analyzed 946 CRT patients with conduction delay. Principal component analysis [PCA] dimensionality reduction obtained a 2-dimensional representation of pre-CRT 12-lead QRS waveforms. K-means clustering of the 2-dimensional PCA representation of 12-lead QRS waveforms identified two patient subgroups [QRS PCA groups]. Vectorcardiographic QRS area was also calculated. They examined two primary outcomes: (1) composite endpoint of death, left ventricular assist device, or heart transplant, and (2) degree of echocardiographic left ventricular ejection fraction [