Pacing and clinical electrophysiology : PACE · 2024
Successful prediction of left bundle branch block-induced cardiomyopathy and treatment effect by artificial intelligence-enabled electrocardiogram.
СтатьяКлинический случайНазвание и аннотация: Перевод в очереди
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Dhawan Rahul, Omer Mohamed, Carpenter Caitlin, Friedman Paul A, Liu Xiaoke
Аннотация
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.
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.