PDF(Pubmed)
Abstract：
Electrocardiogram (ECG) changes after primary percutaneous coronary intervention (PCI) in ST-segment elevation myocardial infarction (STEMI) patients are associated with prognosis. This study investigated the feasibility of predicting left ventricular (LV) dysfunction in STEMI patients using an artificial intelligence (AI)-enabled ECG algorithm developed to diagnose STEMI. Serial ECGs from 637 STEMI patients were analyzed with the AI algorithm, which quantified the probability of STEMI at various time points. The time points included pre-PCI, immediately post-PCI, 6 h post-PCI, 24 h post-PCI, at discharge, and one-month post-PCI. The prevalence of LV dysfunction was significantly associated with the AI-derived probability index. A high probability index was an independent predictor of LV dysfunction, with higher cardiac death and heart failure hospitalization rates observed in patients with higher indices. The study demonstrates that the AI-enabled ECG index effectively quantifies ECG changes post-PCI and serves as a digital biomarker capable of predicting post-STEMI LV dysfunction, heart failure, and mortality. These findings suggest that AI-enabled ECG analysis can be a valuable tool in the early identification of high-risk patients, enabling timely and targeted interventions to improve clinical outcomes in STEMI patients.
摘要：
ST段抬高型心肌梗死(STEMI)患者经皮冠状动脉介入治疗(PCI)后心电图(ECG)变化与预后相关。这项研究调查了使用开发用于诊断STEMI的人工智能（AI）启用的ECG算法预测STEMI患者左心室（LV）功能障碍的可行性。采用AI算法对637例STEMI患者的连续心电图进行分析，量化了不同时间点STEMI的概率。时间点包括PCI前，PCI后即刻，PCI后6小时，PCI后24小时，出院时，PCI术后一个月。LV功能障碍的患病率与AI衍生的概率指数显着相关。高概率指数是左心室功能障碍的独立预测因子，在指数较高的患者中观察到更高的心脏死亡和心力衰竭住院率。该研究表明，AI启用的ECG指数有效地量化了PCI后的ECG变化，并作为能够预测STEMI后LV功能障碍的数字生物标志物。心力衰竭,和死亡率。这些发现表明，人工智能支持的ECG分析可以成为早期识别高危患者的有价值的工具。及时和有针对性的干预措施，以改善STEMI患者的临床结局。
