Choi James W H, Torelli Vincent, Silverman Alex, Diaz Sara Saravia, Kong Darren, Vaish Esha, Katic Luka, Nagourney Alex, Khan Zara, Robbins Lexi, Pinney Sean, Barman Nitin, Farhan Serdar
Department of Medicine.
Department of Cardiology, Icahn School of Medicine at Mount Sinai, Mount Sinai Morningside and.
Coron Artery Dis. 2025 Aug 7. doi: 10.1097/MCA.0000000000001555.
Artificial intelligence (AI) augmentation of ECG assessment has significant potential to improve patient outcomes in acute coronary syndrome.
We sought to evaluate the performance of a novel AI device (PMCardio) in assessing angiographic occlusion myocardial infarction (OMI) and predicting clinical outcomes.
We used a 1-year retrospective cohort of angiographic data from patients presenting with ST-elevation myocardial infarction (STEMI) and non-ST-elevation myocardial infarction (NSTEMI). The device analyzed precatheterization ECGs to identify OMI, defined as a culprit vessel with thrombolysis In myocardial infarction (TIMI) 0-2 flow or TIMI 3 flow and peak cardiac troponin I > 10.0 ng/ml.
A total of 217 patients were included: 72 STEMI (32%) and 145 NSTEMI (65%). Angiographic OMI was confirmed in 60 (83%) STEMI and 51 (35%) NSTEMI cases. The AI model achieved a sensitivity of 86.5%, specificity of 82.2%, and an area under the curve of 0.84. Traditional STEMI criteria had a sensitivity of 54.1% and a specificity of 88.7%. The AI model was 100% sensitive in detecting STEMI-OMI. The odds ratio for mortality in AI-detected OMI patients was 12.44 (1.56-98.98), unplanned readmissions 1.15 (0.53-2.51), and reduced ejection fraction at 1 year 0.24 (0.26-2.16).
The AI model demonstrated higher sensitivity and similar specificity compared with traditional STEMI criteria, improving OMI detection while reducing false positives. These findings suggest potential benefits in triage accuracy and resource utilization, but further prospective validation is needed to determine its clinical impact.
人工智能（AI）增强心电图评估在改善急性冠状动脉综合征患者预后方面具有巨大潜力。
我们旨在评估一种新型AI设备（PMCardio）在评估血管造影闭塞性心肌梗死（OMI）和预测临床结局方面的性能。
我们使用了一个为期1年的回顾性队列，该队列包含了因ST段抬高型心肌梗死（STEMI）和非ST段抬高型心肌梗死（NSTEMI）就诊患者的血管造影数据。该设备分析了导管插入术前的心电图，以识别OMI，OMI定义为罪犯血管的心肌梗死溶栓（TIMI）血流为0 - 2级或TIMI 3级且心肌肌钙蛋白I峰值>10.0 ng/ml。
共纳入217例患者：72例STEMI（32%）和145例NSTEMI（65%）。60例（83%）STEMI和51例（35%）NSTEMI病例经血管造影证实为OMI。AI模型的敏感性为86.5%，特异性为82.2%，曲线下面积为0.84。传统STEMI标准的敏感性为54.1%，特异性为88.7%。AI模型在检测STEMI - OMI时敏感性为100%。AI检测出的OMI患者的死亡比值比为12.44（1.56 - 98.98），非计划再入院比值比为1.15（0.53 - 2.51），1年时射血分数降低的比值比为0.24（0.26 - 2.16）。
与传统STEMI标准相比，AI模型表现出更高的敏感性和相似的特异性，在改善OMI检测的同时减少了假阳性。这些发现提示在分诊准确性和资源利用方面可能存在益处，但需要进一步的前瞻性验证来确定其临床影响。
