Deep Learning–Based Continuous QT Monitoring to Identify High-Risk Prolongation Events After Class III Antiarrhythmic Initiation
A single-lead, deep-learning approach can achieve guideline-level measurement accuracy, enable continuous QTc surveillance from nonstandard ECG vectors, and identify clinically meaningful outpatient QTc prolongation associated with a >4-fold increase in serious ventricular arrhythmias. This strategy may enhance safety monitoring after class III antiarrhythmic initiation and support targeted intervention.
Authors
- Albert J. Rogers (ORCID: https://orcid.org/0000-0001-6585-534X)
- Kelly Brennan (ORCID: https://orcid.org/0000-0002-7698-7353)
- Sanjiv M. Narayan (ORCID: https://orcid.org/0000-0001-7552-5053)
- Xichong Liu (ORCID: https://orcid.org/0000-0002-8772-5623)
- Rishi Trivedi (ORCID: https://orcid.org/0000-0003-3046-4722)
- Todd P. Coleman (ORCID: https://orcid.org/0000-0002-2144-2495)
- Paul J. Wang (ORCID: https://orcid.org/0000-0002-5467-5877)
- Marco Valentin Perez (ORCID: https://orcid.org/0000-0003-2746-0202)
- Sabyasachi Bandyopadhyay (ORCID: https://orcid.org/0000-0003-4490-1946)
- Euan Angus Ashley (ORCID: https://orcid.org/0000-0001-9418-9577)
- David Ouyang (ORCID: https://orcid.org/0000-0002-3813-7518)
- Alexander C. Perino (ORCID: https://orcid.org/0000-0001-5482-8055)
- Jasmine Hughes (ORCID: https://orcid.org/0009-0001-9810-6094)
- Prasanth Ganesan (ORCID: https://orcid.org/0000-0002-1885-0690)
- Rayan A. Ansari (ORCID: https://orcid.org/0000-0001-6153-7272)
Institutions
- Cedars-Sinai Medical Center (US)
- Kaiser Permanente (US)
- Cardiovascular Institute of the South (US)
- Columbia University Irving Medical Center (US)
- Anna Needs Neuroblastoma Answers (US)
- Stanford Medicine (US)
- Stanford University (US)
Publication Details
- Journal
- Circulation
- Published
- 2025-11-08
- DOI
- https://doi.org/10.1161/circulationaha.125.077494
- Citations
- 1
- Primary Topic
- Cardiac electrophysiology and arrhythmias
- Type
- article
- Field-Weighted Citation Impact
- 1.39