Background

Early and correct diagnosis of ST-segment elevation myocardial infarction (STEMI) is crucial for providing timely reperfusion therapy. Patients with ischemic symptoms presenting with ST-segment elevation on the electrocardiogram (ECG) are preferably transported directly to a catheterization laboratory (Cath-lab) for primary percutaneous coronary intervention (PPCI). However, the ECG often contains confounding factors making the STEMI diagnosis challenging leading to false positive Cath-lab activation. The objective of this study was to test the performance of a standard automated algorithm against an additional high specificity setting developed for reducing the false positive STEMI calls.

Methods

We included consecutive patients with an available digital prehospital ECG triaged directly to Cath-lab for acute coronary angiography between 2009 and 2012. An adjudicated discharge diagnosis of STEMI or no myocardial infarction (no-MI) was assigned for each patient. The new automatic algorithm contains a feature to reduce false positive STEMI interpretation. The STEMI performance with the standard setting (STD) and the high specificity setting (HiSpec) was tested against the adjudicated discharge diagnosis in a retrospective manner.

Results

In total, 2256 patients with an available digital prehospital ECG (mean age 63 ± 13 years, male gender 71%) were included in the analysis. The discharge diagnosis of STEMI was assigned in 1885 (84%) patients. The STD identified 165 true negative and 1457 true positive (206 false positive and 428 false negative) cases (77.3%, 44.5%, 87.6% and 17.3% for sensitivity, specificity, PPV and NPV, respectively). The HiSpec identified 191 true negative and 1316 true positive (180 false positive and 569 false negative) cases (69.8%, 51.5%, 88.0% and 25.1% for sensitivity, specificity, PPV and NPV, respectively). From STD to HiSpec, false positive cases were reduced by 26 (12,6%), but false negative results were increased by 33%.

Conclusions

Implementing an automated ECG algorithm with a high specificity setting was able to reduce the number of false positive STEMI cases. However, the predictive values for both positive and negative STEMI identification were moderate in this highly selected STEMI population. Finally, due the reduced sensitivity/increased false negatives, a negative AMI statement should not be solely based on the automated ECG statement.

中文翻译：

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一种用于减少疑似急性冠状动脉综合征患者假阳性病例的新心电图算法的诊断性能

背景

ST 段抬高型心肌梗死 (STEMI) 的早期和正确诊断对于提供及时的再灌注治疗至关重要。心电图 (ECG) 上出现 ST 段抬高的缺血症状患者最好直接转运到导管室 (Cath-lab) 进行初次经皮冠状动脉介入治疗 (PPCI)。然而，心电图通常包含使 STEMI 诊断具有挑战性的混杂因素，从而导致假阳性 Cath-lab 激活。本研究的目的是针对为减少假阳性 STEMI 调用而开发的附加高特异性设置来测试标准自动化算法的性能。

方法

我们纳入了 2009 年至 2012 年间具有可用数字院前心电图直接分类到 Cath-lab 进行急性冠状动脉造影的连续患者。为每位患者分配了 STEMI 或无心肌梗死 (no-MI) 的裁定出院诊断。新的自动算法包含减少误报 STEMI 解释的功能。以回顾性方式对标准设置 (STD) 和高特异性设置 (HiSpec) 的 STEMI 性能与裁定出院诊断进行了测试。

结果

总共有 2256 名具有可用数字院前心电图的患者（平均年龄 63 ± 13 岁，男性 71%）被纳入分析。1885 名 (84%) 患者被分配出 STEMI 的出院诊断。STD 确定了 165 个真阴性和 1457 个真阳性（206 个假阳性和 428 个假阴性）病例（敏感性、特异性、PPV 和 NPV 分别为 77.3%、44.5%、87.6% 和 17.3%）。HiSpec 确定了 191 个真阴性和 1316 个真阳性（180 个假阳性和 569 个假阴性）病例（敏感性、特异性、PPV 和 NPV 分别为 69.8%、51.5%、88.0% 和 25.1%）。从 STD 到 HiSpec，假阳性病例减少了 26 (12.6%)，但假阴性结果增加了 33%。

结论

实施具有高特异性设置的自动 ECG 算法能够减少假阳性 STEMI 病例的数量。然而，在这个高度选择的 STEMI 人群中，阳性和阴性 STEMI 识别的预测值是中等的。最后，由于敏感性降低/假阴性增加，阴性 AMI 声明不应仅基于自动 ECG 声明。