Wang Yu-Hang, Li Chang-Ping, Wang Jing-Xian, Cui Zhuang, Zhou Yu, Jing An-Ran, Liang Miao-Miao, Liu Yin, Gao Jing
Thoracic Clinical College, Tianjin Medical University, 300070 Tianjin, China.
School of Public Health, Tianjin Medical University, 300070 Tianjin, China.
Rev Cardiovasc Med. 2025 Jan 16;26(1):26102. doi: 10.31083/RCM26102. eCollection 2025 Jan.
Studies using machine learning to identify the target characteristics and develop predictive models for coronary artery disease severity in patients with premature myocardial infarction (PMI) are limited.
In this observational study, 1111 PMI patients (≤55 years) at Tianjin Chest Hospital from 2017 to 2022 were selected and divided according to their SYNTAX scores into a low-risk group (≤22) and medium-high-risk group (>22). These groups were further randomly assigned to a training or test set in a ratio of 7:3. Lasso-logistic was initially used to screen out target factors. Subsequently, Lasso-logistic, random forest (RF), k-nearest neighbor (KNN), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost) were used to establish prediction models based on the training set. After comparing prediction performance, the best model was chosen to build a prediction system for coronary artery severity in PMI patients.
Glycosylated hemoglobin (HbA1c), angina, apolipoprotein B (ApoB), total bile acid (TBA), B-type natriuretic peptide (BNP), D-dimer, and fibrinogen (Fg) were associated with the severity of lesions. In the test set, the area under the curve (AUC) of Lasso-logistic, RF, KNN, SVM, and XGBoost were 0.792, 0.775, 0.739, 0.656, and 0.800, respectively. XGBoost showed the best prediction performance according to the AUC, accuracy, F1 score, and Brier score. In addition, we used decision curve analysis (DCA) to assess the clinical validity of the XGBoost prediction model. Finally, an online calculator based on the XGBoost was established to measure the severity of coronary artery lesions in PMI patients.
In summary, we established a novel and convenient prediction system for the severity of lesions in PMI patients. This system can swiftly identify PMI patients who also have severe coronary artery lesions before the coronary intervention, thus offering valuable guidance for clinical decision-making.
利用机器学习识别早发心肌梗死（PMI）患者冠状动脉疾病严重程度的目标特征并建立预测模型的研究有限。
在这项观察性研究中，选取了2017年至2022年在天津胸科医院就诊的1111例PMI患者（年龄≤55岁），并根据其SYNTAX评分分为低风险组（≤22分）和中高风险组（>22分）。这些组再以7:3的比例随机分配到训练集或测试集。最初使用套索逻辑回归筛选目标因素。随后，基于训练集使用套索逻辑回归、随机森林（RF）、k近邻（KNN）、支持向量机（SVM）和极端梯度提升（XGBoost）建立预测模型。比较预测性能后，选择最佳模型构建PMI患者冠状动脉严重程度的预测系统。
糖化血红蛋白（HbA1c）、心绞痛、载脂蛋白B（ApoB）、总胆汁酸（TBA）、B型利钠肽（BNP）、D-二聚体和纤维蛋白原（Fg）与病变严重程度相关。在测试集中，套索逻辑回归、RF、KNN、SVM和XGBoost的曲线下面积（AUC）分别为0.792、0.775、0.739、0.656和0.800。根据AUC、准确率、F1分数和布里尔分数，XGBoost显示出最佳预测性能。此外，我们使用决策曲线分析（DCA）评估XGBoost预测模型的临床有效性。最后，建立了基于XGBoost的在线计算器来测量PMI患者冠状动脉病变的严重程度。
总之，我们为PMI患者的病变严重程度建立了一种新颖且便捷 的预测系统。该系统可以在冠状动脉介入治疗前迅速识别出同时患有严重冠状动脉病变的PMI患者，从而为临床决策提供有价值的指导。
