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Successful prediction of left bundle branch block-induced cardiomyopathy and treatment effect by artificial intelligence-enabled electrocardiogram.
- Source :
-
Pacing and clinical electrophysiology : PACE [Pacing Clin Electrophysiol] 2024 Jun; Vol. 47 (6), pp. 776-779. Date of Electronic Publication: 2024 Apr 07. - Publication Year :
- 2024
-
Abstract
- Background: Left bundle branch block (LBBB) induced cardiomyopathy is an increasingly recognized disease entity.  However, no clinical testing has been shown to be able to predict such an occurrence.<br />Case Report: A 70-year-old male with a prior history of LBBB with preserved ejection fraction (EF) and no other known cardiovascular conditions presented with presyncope, high-grade AV block, and heart failure with reduced EF (36%). His coronary angiogram was negative for any obstructive disease. No other known etiologies for cardiomyopathy were identified. Artificial intelligence-enabled ECGs performed 6 years prior to clinical presentation consistently predicted a high probability (up to 91%) of low EF. The patient successfully underwent left bundle branch area (LBBA) pacing with correction of the underlying LBBB. Subsequent AI ECGs showed a large drop in the probability of low EF immediately after LBBA pacing to 47% and then to 3% 2 months post procedure. His heart failure symptoms markedly improved and EF normalized to 54% at the same time.<br />Conclusions: Artificial intelligence-enabled ECGS may help identify patients who are at risk of developing LBBB-induced cardiomyopathy and predict the response to LBBA pacing.<br /> (© 2024 Wiley Periodicals LLC.)
Details
- Language :
- English
- ISSN :
- 1540-8159
- Volume :
- 47
- Issue :
- 6
- Database :
- MEDLINE
- Journal :
- Pacing and clinical electrophysiology : PACE
- Publication Type :
- Academic Journal
- Accession number :
- 38583090
- Full Text :
- https://doi.org/10.1111/pace.14980