School of Medicine, Medical Specialties, University of Monterrey, Monterrey, Nuevo León, Mexico.
Department of Internal Medicine, Hospital Christus Muguerza Alta Especialidad, Monterrey, Nuevo León, Mexico.
PLoS One. 2021 Nov 30;16(11):e0260661. doi: 10.1371/journal.pone.0260661. eCollection 2021.
Left ventricular hypertrophy detected by echocardiography (Echo-LVH) is an independent predictor of mortality. Integration of the Philips DXL-16 algorithm into the electrocardiogram (ECG) extensively analyses the electricity of the heart. Machine learning techniques such as the C5.0 could lead to a new decision tree criterion to detect Echo-LVH.
To search for a new combination of ECG parameters predictive of Echo-LVH. The final model is called the Cardiac Hypertrophy Computer-based model (CHCM).
We extracted the 458 ECG parameters provided by the Philips DXL-16 algorithm in patients with Echo-LVH and controls. We used the C5.0 ML algorithm to train, test, and validate the CHCM. We compared its diagnostic performance to validate state-of-the-art criteria in our patient cohort.
We included 439 patients and considered an alpha value of 0.05 and a power of 99%. The CHCM includes T voltage in I (≤0.055 mV), peak-to-peak QRS distance in aVL (>1.235 mV), and peak-to-peak QRS distance in aVF (>0.178 mV). The CHCM had an accuracy of 70.5% (CI95%, 65.2-75.5), a sensitivity of 74.3%, and a specificity of 68.7%. In the external validation cohort (n = 156), the CHCM had an accuracy of 63.5% (CI95%, 55.4-71), a sensitivity of 42%, and a specificity of 82.9%. The accuracies of the most relevant state-of-the-art criteria were: Romhilt-Estes (57.4%, CI95% 49-65.5), VDP Cornell (55.7%, CI95%47.6-63.7), Cornell (59%, CI95%50.8-66.8), Dalfó (62.9%, CI95%54.7-70.6), Sokolow Lyon (53.9%, CI95%45.7-61.9), and Philips DXL-16 algorithm (54.5%, CI95%46.3-62.5).
ECG computer-based data and the C5.0 determined a new set of ECG parameters to predict Echo-LVH. The CHCM classifies patients as Echo-LVH with repolarization abnormalities or LVH with increased voltage. The CHCM has a similar accuracy, and is slightly more sensitive than the state-of-the-art criteria.
超声心动图（Echo-LVH）检测到的左心室肥厚是死亡率的独立预测因子。飞利浦 DXL-16 算法将心电图（ECG）广泛分析心脏的电流。C5.0 等机器学习技术可以为检测 Echo-LVH 提供新的决策树标准。
寻找新的 ECG 参数组合来预测 Echo-LVH。最终模型称为基于计算机的心脏肥大模型（CHCM）。
我们提取了 Echo-LVH 患者和对照组中飞利浦 DXL-16 算法提供的 458 个 ECG 参数。我们使用 C5.0 ML 算法对 CHCM 进行训练、测试和验证。我们将其诊断性能与我们患者队列中的最新标准进行了比较。
我们纳入了 439 名患者，考虑到α值为 0.05 和功率为 99%。CHCM 包括 I 导联 T 波电压（≤0.055 mV）、aVL 导联 QRS 波峰到峰距离（>1.235 mV）和 aVF 导联 QRS 波峰到峰距离（>0.178 mV）。CHCM 的准确率为 70.5%（95%CI95%，65.2-75.5），灵敏度为 74.3%，特异性为 68.7%。在外部验证队列（n=156）中，CHCM 的准确率为 63.5%（95%CI95%，55.4-71），灵敏度为 42%，特异性为 82.9%。最相关的最新标准的准确率为：Romhilt-Estes（57.4%，95%CI95%，49-65.5）、VDP Cornell（55.7%，95%CI95%，47.6-63.7）、Cornell（59%，95%CI95%，50.8-66.8）、Dalfó（62.9%，95%CI95%，54.7-70.6）、Sokolow Lyon（53.9%，95%CI95%，45.7-61.9）和飞利浦 DXL-16 算法（54.5%，95%CI95%，46.3-62.5）。
基于心电图计算机的数据和 C5.0 确定了一组新的 ECG 参数来预测 Echo-LVH。CHCM 将患者分类为具有复极异常的 Echo-LVH 或具有电压升高的 LVH。CHCM 的准确率与最新标准相似，灵敏度略高。
