Sha Xiang, Wang Wei, Wang Jian, Wang Ruzhu
Department of Cardiology, The Affiliated Taizhou People's Hospital of Nanjing Medical University, Taizhou School of Clinical Medicine, Nanjing Medical University, Taizhou, 225300, People's Republic of China.
J Multidiscip Healthc. 2025 Sep 6;18:5591-5602. doi: 10.2147/JMDH.S549547. eCollection 2025.
Coronary microvascular dysfunction (CMD) significantly impacts outcomes in patients with acute ST-segment elevation myocardial infarction (STEMI) undergoing percutaneous coronary intervention (PCI). The cardiometabolic index (CMI), an indicator combining lipid and anthropometric parameters, has been linked to cardiovascular risk, but its association with CMD remains unclear. This study aims to investigate the relationship between CMI and the occurrence of CMD following PCI in STEMI patients and to assess its predictive value using Least Absolute Shrinkage and Selection Operator (LASSO)-based feature selection and multiple machine learning algorithms.
This retrospective cohort study enrolled STEMI patients who underwent primary PCI with stent implantation and post-procedural coronary microvascular function assessment between January 2021 and December 2024. Patients were categorized into CMD and non-CMD groups based on noninvasive microvascular resistance indices. Logistic regression, restricted cubic spline analysis, and machine learning models (Random Forest (RF), LightGBM, XGboost and K-Nearest Neighbors) were employed to evaluate the predictive value of CMI for post-PCI CMD.
A total of 702 STEMI patients were included, and CMD was observed in 52.1% of patients. Compared to the first CMI tertile (T1) group, T2 and T3 group had increased odds of CMD (T2: adjusted odds ratio (aOR) 2.41, 95% confidence interval (CI) 1.60-3.63; T3: aOR 3.40, 95% CI 2.17-5.32). There was a non-linear relationship between CMI and CMD ( < 0.001). The area under the curve (AUC) for CMI predicting CMD was 0.627 (95% CI: 0.586-0.666). Seven variables were screened by LASSO-Logistic regression for model development. Comparing four models' performances, the RF model achieved the best performance (AUC = 0.772). SHapley analysis revealed that CMI had the highest predictive value for CMD.
A higher CMI level is an independent risk factor for CMD of STEMI patients after PCI, and its predictive value enhanced when integrated into RF model.
冠状动脉微血管功能障碍（CMD）对接受经皮冠状动脉介入治疗（PCI）的急性ST段抬高型心肌梗死（STEMI）患者的预后有显著影响。心脏代谢指数（CMI）是一种结合脂质和人体测量参数的指标，与心血管风险有关，但其与CMD的关联仍不明确。本研究旨在探讨STEMI患者PCI术后CMI与CMD发生之间的关系，并使用基于最小绝对收缩和选择算子（LASSO）的特征选择和多种机器学习算法评估其预测价值。
这项回顾性队列研究纳入了2021年1月至2024年12月期间接受首次PCI并植入支架以及术后进行冠状动脉微血管功能评估的STEMI患者。根据无创微血管阻力指数将患者分为CMD组和非CMD组。采用逻辑回归、受限立方样条分析和机器学习模型（随机森林（RF）、LightGBM、XGBoost和K近邻）来评估CMI对PCI术后CMD的预测价值。
共纳入702例STEMI患者，52.1%的患者观察到CMD。与第一CMI三分位数（T1）组相比，T2和T3组CMD的发生几率增加（T2：调整后的优势比（aOR）2.41，95%置信区间（CI）1.60 - 3.63；T3：aOR 3.40，95% CI 2.17 - 5.32）。CMI与CMD之间存在非线性关系（<0.001）。CMI预测CMD的曲线下面积（AUC）为0.627（95% CI：0.586 - 0.666）。通过LASSO逻辑回归筛选出7个变量用于模型构建。比较四种模型的性能，RF模型表现最佳（AUC = 0.772）。SHapley分析显示CMI对CMD的预测价值最高。
较高的CMI水平是STEMI患者PCI术后CMD的独立危险因素，当将其纳入RF模型时，其预测价值得到增强。
