BackgroundLeft 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 reportA 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.ConclusionsArtificial intelligence‐enabled ECGS may help identify patients who are at risk of developing LBBB‐induced cardiomyopathy and predict the response to LBBA pacing.

中文翻译：

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人工智能心电图成功预测左束支传导阻滞性心肌病及治疗效果

背景左束支传导阻滞（LBBB）诱发的心肌病是一种日益被认可的疾病实体。然而，没有临床测试表明能够预测这种情况的发生。病例报告一名 70 岁男性，有 LBBB 既往病史，射血分数 (EF) 正常，没有其他已知的心血管疾病，表现为先兆晕厥、高血压。级房室传导阻滞和 EF 降低的心力衰竭 (36%)。他的冠状动脉造影显示任何阻塞性疾病均为阴性。尚未发现心肌病的其他已知病因。在临床表现前 6 年进行的人工智能心电图一致预测低 EF 的可能性很高（高达 91%）。患者成功接受了左束支区 (LBBA) 起搏并纠正了潜在的 LBBB。随后的 AI 心电图显示，LBBA 起搏后，低 EF 的概率立即大幅下降至 47%，然后在术后 2 个月下降至 3%。他的心力衰竭症状明显改善，同时 EF 正常化至 54%。结论人工智能支持的 ECGS 可能有助于识别有患 LBBB 诱发心肌病风险的患者，并预测对 LBBA 起搏的反应。