Lin Ziwei, Aw Tar Choon, Jackson Laurel, Kow Cheryl Shumin, Murtagh Gillian, Chua Siang Jin Terrance, Richards Arthur Mark, Lim Swee Han
Department of Emergency Medicine, Sengkang General Hospital, Singapore.
Department of Laboratory Medicine, Changi General Hospital, Singapore.
Ann Acad Med Singap. 2025 Apr 22;54(4):219-226. doi: 10.47102/annals-acadmedsg.2024232.
Elevated troponin, while essential for diagnosing myocardial infarction, can also be present in non-myocardial infarction conditions. The myocardial-ischaemic-injury-index (MI3) algorithm is a machine learning algorithm that considers age, sex and cardiac troponin I (TnI) results to risk-stratify patients for type 1 myocardial infarction.
Patients aged ≥25 years who presented to the emergency department (ED) of Singapore General Hospital with symptoms suggestive of acute coronary syndrome with no diagnostic 12-lead electrocardiogram (ECG) changes were included. Participants had serial ECGs and high-sensitivity troponin assays performed at 0, 2 and 7 hours. The primary outcome was the adjudicated diagnosis of type 1 myocardial infarction at 30 days. We compared the performance of MI3 in predicting the primary outcome with the European Society of Cardiology (ESC) 0/2-hour algorithm as well as the 99th percentile upper reference limit (URL) for TnI.
There were 1351 patients included (66.7% male, mean age 56 years), 902 (66.8%) of whom had only 0-hour troponin results and 449 (33.2%) with serial (both 0 and 2-hour) troponin results available. MI3 ruled out type 1 myocardial infarction with a higher sensitivity (98.9, 95% confidence interval [CI] 93.4-99.9%) and similar negative predictive value (NPV) 99.8% (95% CI 98.6-100%) as compared to the ESC strategy. The 99th percentile cut-off strategy had the lowest sensitivity, specificity, positive predictive value and NPV.
The MI3 algorithm was accurate in risk stratifying ED patients for myocardial infarction. The 99th percentile URL cut-off was the least accurate in ruling in and out myocardial infarction compared to the other strategies.
肌钙蛋白升高虽对诊断心肌梗死至关重要，但在非心肌梗死情况下也可能出现。心肌缺血损伤指数（MI3）算法是一种机器学习算法，它考虑年龄、性别和心肌肌钙蛋白I（TnI）结果，对1型心肌梗死患者进行风险分层。
纳入年龄≥25岁、因疑似急性冠状动脉综合征到新加坡总医院急诊科就诊且12导联心电图（ECG）无诊断性改变的患者。参与者在0、2和7小时进行了系列心电图检查和高敏肌钙蛋白检测。主要结局是30天时判定的1型心肌梗死诊断。我们将MI3预测主要结局的性能与欧洲心脏病学会（ESC）0/2小时算法以及TnI的第99百分位数上限参考值（URL）进行了比较。
共纳入1351例患者（男性占66.7%，平均年龄56岁），其中902例（66.8%）仅有0小时肌钙蛋白结果，449例（33.2%）有系列（0小时和2小时）肌钙蛋白结果。与ESC策略相比，MI3排除1型心肌梗死的敏感性更高（98.9，95%置信区间[CI] 93.4 - 99.9%），阴性预测值（NPV）相似，为99.8%（95% CI 98.6 - 100%）。第99百分位数截断策略的敏感性、特异性、阳性预测值和NPV最低。
MI3算法在对急诊科患者进行心肌梗死风险分层方面是准确的。与其他策略相比，第99百分位数URL截断在判定心肌梗死方面最不准确。
