Use real word data to evaluate the performance of the electrocardiographic markers of GEH as features in a machine learning model with Standard ECG features and Risk Factors in Predicting Outcome of patients in a population referred to a tertiary cardiology hospital. Patients forwarded to specific evaluation in a cardiology specialized hospital performed an ECG and a risk factor anamnesis. A series of follow up attendances occurred in periods of 6 months, 12 months and 15 months to check for cardiovascular related events (mortality or new nonfatal cardiovascular events (Stroke, MI, PCI, CS), as identified during 1-year phone follow-ups. The first attendance ECG was measured by a specialist and processed in order to obtain the global electric heterogeneity (GEH) using the Kors Matriz. The ECG measurements, GEH parameters and risk factors were combined for training multiple instances of XGBoost decision trees models. Each instance were optmized for the AUCPR and the instance with higher AUC is chosen as representative to the model. The importance of each parameter for the winner tree model was compared to better understand the improvement from using GEH parameters. The GEH parameters turned out to have statistical significance for this population specially the QRST angle and the SVG. The combined model with the tree parameters class had the best performance. The findings suggest that using VCG features can facilitate more accurate identification of patients who require tertiary care, thereby optimizing resource allocation and improving patient outcomes. Moreover, the decision tree model's transparency and ability to pinpoint critical features make it a valuable tool for clinical decision-making and align well with existing clinical practices.

翻译：本研究利用真实世界数据，评估了将整体电异质性（GEH）的心电图标记物作为特征，与标准心电图特征及风险因素结合构建机器学习模型，在预测转诊至三级心脏医院患者群体预后方面的表现。转诊至心脏专科医院接受特定评估的患者接受了心电图检查和风险因素问诊。研究在6个月、12个月和15个月的时间点进行了一系列随访，以检查心血管相关事件（死亡率或新的非致命性心血管事件，如卒中、心肌梗死、经皮冠状动脉介入治疗、心源性休克），这些事件通过为期1年的电话随访进行确认。由专科医生测量首次就诊时的心电图，并使用Kors矩阵进行处理以获得整体电异质性（GEH）参数。将心电图测量值、GEH参数和风险因素结合，用于训练多个XGBoost决策树模型实例。每个实例均针对AUCPR进行优化，并选择AUC最高的实例作为模型的代表。通过比较获胜树模型中各参数的重要性，以更好地理解使用GEH参数带来的改进。结果表明，GEH参数（特别是QRST角和空间QRS-T夹角）对该人群具有统计学意义。结合树参数类别的模型表现出最佳性能。研究结果表明，使用心电向量图特征可以更准确地识别需要三级护理的患者，从而优化资源分配并改善患者预后。此外，决策树模型的透明度和识别关键特征的能力使其成为临床决策的宝贵工具，并与现有临床实践高度契合。