Casciaro Marco, Di Micco Pierpaolo, Tonacci Alessandro, Vatrano Marco, Russo Vincenzo, Siniscalchi Carmine, Gangemi Sebastiano, Imbalzano Egidio
Allergy and Clinical Immunology Unit, Department of Clinical and Experimental Medicine, University of Messina, 98125 Messina, Italy.
UOC Medicina Interna, AFO Medica, P.O. Santa Maria delle Grazie, ASL Napoli 2 Nord, 80076 Pozzuoli, Italy.
Clin Pract. 2025 Mar 31;15(4):72. doi: 10.3390/clinpract15040072.
Ischemic heart disease is a major global health problem with significant morbidity and mortality. Several cardiometabolic variables play a key role in the incidence of adverse cardiovascular outcomes. The aim of the present study was to apply a machine learning approach to investigate factors that can predict acute coronary syndrome in patients with a previous episode. We recruited 652 patients, admitted to the hospital for acute coronary syndrome, eligible if undergoing immediate coronary revascularization procedures for ST-segment-elevation myocardial infarction or coronary revascularization procedures within 24 h. Baseline pulse wave velocity appears to be the most predictive variable overall, followed by the occurrence of left ventricular hypertrophy and left ventricular end-diastolic diameters. We found that the potential of machine learning to predict life-threatening events is significant. Machine learning algorithms can be used to create models to identify patients at risk for acute myocardial infarction. However, great care must be taken with data quality and ethical use of these algorithms.
缺血性心脏病是一个重大的全球健康问题，具有很高的发病率和死亡率。几个心脏代谢变量在不良心血管结局的发生中起关键作用。本研究的目的是应用机器学习方法来调查能够预测既往有发作史患者急性冠状动脉综合征的因素。我们招募了652例因急性冠状动脉综合征入院的患者，入选标准为因ST段抬高型心肌梗死接受即刻冠状动脉血运重建术或在24小时内接受冠状动脉血运重建术。总体而言，基线脉搏波速度似乎是最具预测性的变量，其次是左心室肥厚的发生和左心室舒张末期直径。我们发现机器学习预测危及生命事件的潜力很大。机器学习算法可用于创建模型以识别急性心肌梗死风险患者。然而，必须格外注意这些算法的数据质量和伦理使用。
