Machine Learning of 12-Lead QRS Waveforms to Identify Cardiac Resynchronization Therapy Patients With Differential Outcomes.
Journal:
Circulation. Arrhythmia and electrophysiology
PMID:
32538136
Abstract
BACKGROUND: Cardiac resynchronization therapy (CRT) improves heart failure outcomes but has significant nonresponse rates, highlighting limitations in ECG selection criteria: QRS duration (QRSd) ≥150 ms and subjective labeling of left bundle branch block (LBBB). We explored unsupervised machine learning of ECG waveforms to identify CRT subgroups that may differentiate outcomes beyond QRSd and LBBB.
Authors
Keywords
Aged
Bundle-Branch Block
Cardiac Resynchronization Therapy
Diagnosis, Computer-Assisted
Disease Progression
Electrocardiography
Female
Heart Failure
Heart Transplantation
Heart-Assist Devices
Humans
Male
Middle Aged
Predictive Value of Tests
Recovery of Function
Retrospective Studies
Risk Assessment
Risk Factors
Signal Processing, Computer-Assisted
Stroke Volume
Time Factors
Treatment Outcome
Unsupervised Machine Learning
Ventricular Function, Left