Background

Novel temporal-spatial features of the 12‑lead ECG can conceptually optimize culprit lesions' detection beyond that of classical ST amplitude measurements. We sought to develop a data-driven approach for ECG feature selection to build a clinically relevant algorithm for real-time detection of culprit lesion.

Methods

This was a prospective observational cohort study of chest pain patients transported by emergency medical services to three tertiary care hospitals in the US. We obtained raw 10-s, 12‑lead ECGs (500 s/s, HeartStart MRx, Philips Healthcare) during prehospital transport and followed patients 30 days after the encounter to adjudicate clinical outcomes. A total of 557 global and lead-specific features of P-QRS-T waveform were harvested from the representative average beats. We used Recursive Feature Elimination and LASSO to identify 35/557, 29/557, and 51/557 most recurrent and important features for LAD, LCX, and RCA culprits, respectively. Using the union of these features, we built a random forest classifier with 10-fold cross-validation to predict the presence or absence of culprit lesions. We compared this model to the performance of a rule-based commercial proprietary software (Philips DXL ECG Algorithm).

Results

Our sample included 2400 patients (age 59 ± 16, 47% female, 41% Black, 10.7% culprit lesions). The area under the ROC curves of our random forest classifier was 0.85 ± 0.03 with sensitivity, specificity, and negative predictive value of 71.1%, 84.7%, and 96.1%. This outperformed the accuracy of the automated interpretation software of 37.2%, 95.6%, and 92.7%, respectively, and corresponded to a net reclassification improvement index of 23.6%. Metrics of ST80; Tpeak-Tend; spatial angle between QRS and T vectors; PCA ratio of STT waveform; T axis; and QRS waveform characteristics played a significant role in this incremental gain in performance.

Conclusions

Novel computational features of the 12‑lead ECG can be used to build clinically relevant machine learning-based classifiers to detect culprit lesions, which has important clinical implications.

中文翻译：

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新型心电图特征和机器学习优化疑似急性冠状动脉综合征患者的罪犯病变检测

背景

12 导联心电图的新颖时空特征可以在概念上优化罪犯病变的检测，超越经典的 ST 幅度​​测量。我们试图开发一种用于 ECG 特征选择的数据驱动方法，以构建用于实时检测罪魁祸首病变的临床相关算法。

方法

这是一项前瞻性观察性队列研究，研究对象是通过紧急医疗服务转运到美国三家三级医院的胸痛患者。我们在院前转运期间获得了原始 10 秒、12 导联心电图（500 秒/秒，HeartStart MRx，飞利浦医疗），并在会面后 30 天对患者进行随访以判断临床结果。从具有代表性的平均搏动中获得了 P-QRS-T 波形的 557 个全局和导联特定特征。我们使用递归特征消除和 LASSO 分别识别出 LAD、LCX 和 RCA 罪魁祸首的 35/557、29/557 和 51/557 最常见和最重要的特征。使用这些特征的联合，我们构建了一个具有 10 折交叉验证的随机森林分类器来预测是否存在罪魁祸首病变。

结果

我们的样本包括 2400 名患者（年龄 59 ± 16 岁，47% 为女性，41% 为黑人，10.7% 为罪魁祸首）。我们的随机森林分类器的 ROC 曲线下面积为 0.85 ± 0.03，灵敏度、特异性和阴性预测值分别为 71.1%、84.7% 和 96.1%。这优于自动解释软件的准确性分别为 37.2%、95.6% 和 92.7%，对应于 23.6% 的净重分类改进指数。ST80 指标；Tpeak-Tend; QRS 和 T 矢量之间的空间角度；STT波形的PCA比率；出租车; 和 QRS 波形特征在性能的这种增量增益中发挥了重要作用。

结论

12 导联心电图的新颖计算特征可用于构建临床相关的基于机器学习的分类器，以检测罪魁祸首病变，这具有重要的临床意义。