Background: Cardiogenic shock (CS) is a life-threatening complication of ST-elevation myocardial infarction (STEMI) and remains the leading cause of in-hospital mortality, with rates ranging from 5 to 10% despite advances in reperfusion strategies. Early identification and timely intervention are critical for improving outcomes. This study investigates the utility of machine learning (ML) models for predicting the risk of CS during the early phases of care-prehospital, emergency department (ED), and cardiology-on-call-with a focus on accurate triage and prioritization for urgent angiography. Results: In the prehospital phase, the Extra Trees classifier demonstrated the highest overall performance. It achieved an accuracy (ACC) of 0.9062, precision of 0.9078, recall of 0.9062, F1-score of 0.9061, and Matthews correlation coefficient (MCC) of 0.8140, indicating both high predictive power and strong generalization. In the ED phase, the support vector machine model outperformed others with an ACC of 78.12%. During the cardiology-on-call phase, Random Forest showed the best performance with an ACC of 81.25% and consistent values across other metrics. Quadratic discriminant analysis showed consistent and generalizable performance across all early care stages. Key predictive features included the Killip class, ECG rhythm, creatinine, potassium, and markers of renal dysfunction-parameters readily available in routine emergency settings. The greatest clinical utility was observed in prehospital and ED phases, where ML models could support the early identification of critically ill patients and could prioritize coronary catheterization, especially important for centers with limited capacity for angiography. Conclusions: Machine learning-based predictive models offer a valuable tool for early risk stratification in STEMI patients at risk for cardiogenic shock. These findings support the implementation of ML-driven tools in early STEMI care pathways, potentially improving survival through faster and more accurate decision-making, especially in time-sensitive clinical environments.

Keywords: STEMI; angiography prioritization; cardiogenic shock; early triage; machine learning; predictive models.

背景：心源性休克（CS）是ST段抬高型心肌梗死（STEMI）的一种危及生命的并发症，仍然是住院期间死亡率的主要原因，尽管再灌注策略有所进步，但其发生率仍介于5%到10%之间。早期识别和及时干预对于改善预后至关重要。本研究调查了机器学习（ML）模型在护理早期阶段预测CS风险的实用性——包括院前、急诊科（ED）、心脏专科电话值班期间，重点在于准确分诊和优先安排紧急冠状动脉造影。结果：在院前阶段，Extra Trees分类器表现出最高的整体性能。其达到了0.9062的准确性（ACC），0.9078的精确度，0.9062的召回率，0.9061的F1分数和0.8140的马修斯相关系数（MCC），表明具有很高的预测能力和良好的泛化能力。在急诊科阶段，支持向量机模型表现最佳，达到了78.12%的准确性。在心脏专科电话值班期间，随机森林显示出最佳性能，其准确率为81.25%，并且其他指标也表现出一致性和稳定性。二次判别分析在整个早期护理阶段都显示出了稳定且可泛化的性能。关键预测特征包括Killip分级、心电图节律、肌酐水平、钾水平以及肾功能障碍标志物——这些参数在常规急诊环境中易于获取。院前和急诊科阶段观察到最大的临床实用性，ML模型可以帮助早期识别危重患者，并优先安排冠状动脉造影，特别是在冠状动脉造影能力有限的中心尤为重要。结论：基于机器学习的风险分层预测模型为STEMI患者发生心源性休克提供了一种有价值的工具。这些发现支持在早期STEMI护理路径中实施ML驱动的工具，通过更快更准确的决策可能提高生存率，在时间敏感的临床环境中尤其重要。

关键词：STEMI；冠状动脉造影优先级；心源性休克；早期分诊；机器学习；预测模型。