Tsivanyuk M M, Shakhgeldyan K I, Markov M A, Shirobokov V G, Geltser B I
Senior Researcher, Laboratory of Big Data Analysis in Healthcare and Medicine; Far East Federal University, 10 Ayaks Village, Russkiy Island, Vladivostok, 690922, Russia; Interventional Cardiologist; Vladivostok City Clinical Hospital No.1, 22 Sadovaya St., Vladivostok, 690078, Russia.
Associate Professor, Head of the Laboratory of Big Data Analysis in Healthcare and Medicine; Far East Federal University, 10 Ayaks Village, Russkiy Island, Vladivostok, 690922, Russia; Director of Scientific and Educational Center for Artificial Intelligence; Vladivostok State University, 41 Gogolya St., Vladivostok, 690014, Russia.
Sovrem Tekhnologii Med. 2025;17(3):50-60. doi: 10.17691/stm2025.17.3.05. Epub 2025 Jun 30.
was to assess the accuracy of prognostic models for obstructive coronary artery disease (OCAD) in the first hours of admission in patients with non-ST segment elevation acute coronary syndrome (NSTE-ACS).
The study involved 610 patients with low- and intermediate-risk NSTE-ACS (Me - 62 years). Based on invasive coronary angiography findings the patients were divided into 2 groups: the first - 363 (59.5%) patients with OCAD (coronary artery luminal occlusion ≥50%), the second - 247 (40.5%) patients without coronary obstruction (<50%). Clinical and functional status was assessed using 62 parameters available at the early hospitalization including: clinical and demographic, anthropometric, laboratory, electrocardiographic and echocardiographic data.OCAD predictive models were developed using machine learning methods: multifactorial logistic regression, random forest, and stochastic gradient boosting (SGB). The models contained the sets of predictors identified during the initial medical examination in the hospital (the first scenario), after 1-hour observation (the second scenario), and 3 h later (the third scenario). The quality of the models was assessed using six metrics. The impact degree of individual predictors on the study endpoint was determined by the Shapley method of additive explanation (SHAP). OCAD probability stratification was performed by distinguishing the categories of low, medium, high and very high risk.
Based on machine learning methods, OCAD predictive models were developed, among which the best quality metrics were demonstrated by SGB models with the sets of predictors corresponding to three prognostic scenarios (the area under ROC curve: 0.846, 0.887, and 0.949, respectively). Using the SHAP method, we identified the factors with a dominant impact on OCAD, which included the anthropometric indicators (waist circumference, hip circumference, and their ratio) - in the first and second prognostic scenarios; and global longitudinal systolic strain of the left ventricle - in the third scenario. Based on SGB model data there were distinguished the categories of low, medium, high and very high risk of OCAD, their digital ranges depended on the prognostic scenarios.
The prognostic OCAD models developed based on SGB enable to highly accurately assess the degree of coronary damage in NSTE-ACS patients in the first hours of hospitalization. The highest accuracy of OCAD prediction was demonstrated by the models of the third scenario, the structure of which, in addition to anamnestic, anthropometric and ECG data, included clinical and biochemical blood parameters and echocardiographic indicators. Thus, OCAD risk stratification using the mentioned models can be a useful tool in selecting the optimal myocardial revascularization strategy.
旨在评估非ST段抬高型急性冠状动脉综合征（NSTE-ACS）患者入院后最初数小时内阻塞性冠状动脉疾病（OCAD）预后模型的准确性。
该研究纳入了610例低危和中危NSTE-ACS患者（平均年龄62岁）。根据有创冠状动脉造影结果，将患者分为两组：第一组363例（59.5%）为OCAD患者（冠状动脉管腔闭塞≥50%），第二组247例（40.5%）为无冠状动脉阻塞患者（<50%）。使用入院早期可获得的62项参数评估临床和功能状态，包括临床和人口统计学、人体测量学、实验室、心电图和超声心动图数据。使用机器学习方法开发OCAD预测模型：多因素逻辑回归、随机森林和随机梯度提升（SGB）。模型包含在医院初次体检时（第一种情况）、1小时观察后（第二种情况）和3小时后（第三种情况）确定的预测因子集。使用六个指标评估模型质量。通过夏普利加法解释法（SHAP）确定个体预测因子对研究终点的影响程度。通过区分低、中、高和极高风险类别进行OCAD概率分层。
基于机器学习方法开发了OCAD预测模型，其中SGB模型在对应三种预后情况的预测因子集下表现出最佳质量指标（ROC曲线下面积分别为：0.846、0.887和0.949）。使用SHAP方法，我们确定了对OCAD有主要影响的因素，在第一种和第二种预后情况中包括人体测量指标（腰围、臀围及其比值）；在第三种情况中包括左心室整体纵向收缩应变。根据SGB模型数据区分了OCAD的低、中、高和极高风险类别，其数值范围取决于预后情况。
基于SGB开发的OCAD预后模型能够在住院后的最初数小时内高度准确地评估NSTE-ACS患者的冠状动脉损伤程度。第三种情况的模型显示出OCAD预测的最高准确性，其结构除了既往史、人体测量学和心电图数据外，还包括临床和生化血液参数以及超声心动图指标。因此，使用上述模型进行OCAD风险分层可以成为选择最佳心肌血运重建策略的有用工具。
