Successful prediction of left bundle branch block-induced cardiomyopathy and treatment effect by artificial intelligence-enabled electrocardiogram.
Pacing Clin Electrophysiol
; 47(6): 776-779, 2024 Jun.
Article
em En
| MEDLINE
| ID: mdl-38583090
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. 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.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.Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Bloqueio de Ramo
/
Inteligência Artificial
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Eletrocardiografia
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Cardiomiopatias
Limite:
Aged
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Humans
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Male
Idioma:
En
Revista:
Pacing Clin Electrophysiol
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
Estados Unidos