Xue Joel, Aufderheide Tom, Scott Wright R, Klein John, Farrell Robert, Rowlandson Ian, Young Brian
GE Healthcare Information Technologies, Rochester, MN, USA.
J Electrocardiol. 2004;37 Suppl:233-9. doi: 10.1016/j.jelectrocard.2004.08.063.
A new computerized acute coronary syndrome (ACS) computer algorithm has been developed with the aim of improving the electrocardiographic detection of acute myocardial ischemia and infarction in the emergency department (ED). The purpose of this study was to determine the added value of the new ACS algorithm in assisting ED physicians to obtain a more accurate diagnosis in patients with ACS. The new algorithm combines a rule-based decision tree, which uses well-known clinical criteria and a data-centered neural network model for more robust pattern recognition. Input parameters of the neural network model consist of morphology features of derived Frank X, Y, Z waveforms and the patient's gender and age. The neural network model was trained with electrocardiograms obtained from documented acute myocardial infarction patients at the Mayo Clinic who were a part of a research ACS database, which includes electrocardiograms (ECGs) of more than 5,000 individuals at hospital admission (1st ECG in the ED). The test set portion of the study was conducted in 2 steps: 1) One emergency physician and 1 cardiologist classified 1,902 clinically correlated out-of-hospital ECGs without seeing the interpretation statement from the algorithm into 1 of the following categories: 1) acute myocardial infarction, acute ischemia, or nonischemic; 2) After 9 months, the same 2 physicians classified the same group of ECGs but with the interpretation statement of the algorithm printed on the tracing. The results demonstrated that with the assistance of the new algorithm, the emergency physician and cardiologist improved their sensitivity of interpreting acute myocardial infarction by 50% and 26%, respectively, without a loss of specificity. The new algorithm also improved the emergency physician's acute ischemia interpretation sensitivity by 53% and still maintained a reasonable specificity (91%). The new ACS algorithm provides added value for improving acute ischemia and infarction detection in the ED.
一种新的计算机化急性冠状动脉综合征（ACS）计算机算法已被开发出来，目的是改善急诊科（ED）对急性心肌缺血和梗死的心电图检测。本研究的目的是确定新的ACS算法在协助急诊科医生对ACS患者进行更准确诊断方面的附加价值。新算法结合了基于规则的决策树，该决策树使用著名的临床标准以及以数据为中心的神经网络模型进行更强大的模式识别。神经网络模型的输入参数包括推导的Frank X、Y、Z波形的形态特征以及患者的性别和年龄。神经网络模型是使用从梅奥诊所记录的急性心肌梗死患者获得的心电图进行训练的，这些患者是研究ACS数据库的一部分，该数据库包括超过5000名个体在入院时（急诊科的第一份心电图）的心电图。该研究的测试集部分分两步进行：1）一名急诊科医生和一名心脏病专家将1902份与临床相关的院外心电图在未查看算法解释声明的情况下分类为以下类别之一：1）急性心肌梗死、急性缺血或非缺血性；2）9个月后，同两位医生对同一组心电图进行分类，但在描记图上打印了算法的解释声明。结果表明，在新算法的协助下，急诊科医生和心脏病专家分别将急性心肌梗死的解释敏感性提高了50%和26%，且特异性未降低。新算法还将急诊科医生对急性缺血的解释敏感性提高了53%，并仍保持合理的特异性（91%）。新的ACS算法为改善急诊科对急性缺血和梗死的检测提供了附加价值。
