急性STEMI患者心脏代谢指数与pci术后冠状动脉微血管功能障碍的关系

IF 2.4 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Journal of Multidisciplinary Healthcare Pub Date : 2025-09-06 eCollection Date: 2025-01-01 DOI:10.2147/JMDH.S549547
Xiang Sha, Wei Wang, Jian Wang, Ruzhu Wang
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引用次数: 0

摘要

背景:冠状动脉微血管功能障碍(CMD)显著影响急性st段抬高型心肌梗死(STEMI)患者经皮冠状动脉介入治疗(PCI)的预后。心血管代谢指数(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的关系尚不清楚。本研究旨在探讨STEMI患者PCI术后CMI与CMD发生的关系,并利用基于最小绝对收缩和选择算子(LASSO)的特征选择和多机器学习算法评估其预测价值。方法:本回顾性队列研究纳入了2021年1月至2024年12月期间接受初级PCI支架植入和术后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标将患者分为CMD组和非CMD组。采用Logistic回归、受限三次样条分析和机器学习模型(Random Forest (RF)、LightGBM、XGboost和K-Nearest Neighbors)评估CMI对pci后CMD的预测价值。结果:共纳入STEMI患者702例,其中52.1%的患者出现CMD。与第一CMI组(T1)相比,T2和T3组患CMD的几率增加(T2:调整优势比(aOR) 2.41, 95%可信区间(CI) 1.60 ~ 3.63;T3: aOR 3.40, 95% CI 2.17-5.32)。CMI与CMD呈非线性关系(P < 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-Logistic回归筛选7个变量进行模型开发。对比四种模型的性能,射频模型的性能最佳(AUC = 0.772)。SHapley分析显示CMI对CMD的预测价值最高。结论:较高的CMI水平是STEMI患者PCI术后发生CMD的独立危险因素,结合RF模型可提高其预测价值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Relationship Between Cardiometabolic Index and Post-PCI Coronary Microvascular Dysfunction in Acute STEMI Patients.

Relationship Between Cardiometabolic Index and Post-PCI Coronary Microvascular Dysfunction in Acute STEMI Patients.

Relationship Between Cardiometabolic Index and Post-PCI Coronary Microvascular Dysfunction in Acute STEMI Patients.

Relationship Between Cardiometabolic Index and Post-PCI Coronary Microvascular Dysfunction in Acute STEMI Patients.

Background: 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.

Methods: 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.

Results: 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 (P < 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.

Conclusion: 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.

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来源期刊
Journal of Multidisciplinary Healthcare
Journal of Multidisciplinary Healthcare Nursing-General Nursing
CiteScore
4.60
自引率
3.00%
发文量
287
审稿时长
16 weeks
期刊介绍: The Journal of Multidisciplinary Healthcare (JMDH) aims to represent and publish research in healthcare areas delivered by practitioners of different disciplines. This includes studies and reviews conducted by multidisciplinary teams as well as research which evaluates or reports the results or conduct of such teams or healthcare processes in general. The journal covers a very wide range of areas and we welcome submissions from practitioners at all levels and from all over the world. Good healthcare is not bounded by person, place or time and the journal aims to reflect this. The JMDH is published as an open-access journal to allow this wide range of practical, patient relevant research to be immediately available to practitioners who can access and use it immediately upon publication.
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