Suppr超能文献

中性粒细胞与高密度脂蛋白胆固醇比值可预测急性ST段抬高型心肌梗死患者PCI术后的左心室重构和主要不良心血管事件。

Neutrophil to high-density lipoprotein cholesterol ratio predicts left ventricular remodeling and MACE after PCI in patients with acute ST-segment elevation myocardial infarction.

作者信息

Chen Jianlin, Liu Anbang, Zhang Dan, Meng Tingting, Zhang Xinhe, Xu Weihong, Zheng Yan, Su Guohai

机构信息

School of Clinical Medicine, Shandong Second Medical University, Weifang, China.

Shandong First Medical University & Shandong Academy of Medical Sciences, Jinan, Shandong, China.

出版信息

Front Cardiovasc Med. 2025 Apr 3;12:1497255. doi: 10.3389/fcvm.2025.1497255. eCollection 2025.

Abstract

BACKGROUND

The neutrophil to high-density lipoprotein cholesterol ratio (NHR) has been proposed as a potential marker for predicting cardiovascular events. However, its prognostic role following percutaneous coronary intervention (PCI) in patients with acute ST-segment elevation myocardial infarction (STEMI) remains unclear. This study aimed to evaluate the predictive value of NHR for left ventricular remodeling (LVR) and long-term outcomes in STEMI patients post-PCI.

METHODS

This retrospective study included 299 STEMI patients who underwent PCI and were followed for 24 months post-procedure. Echocardiography was performed upon admission and at 6 months post-myocardial infarction (MI). LVR was defined as an increase in left ventricular diastolic volume (LVEDV) of at least 20% from baseline. Based on their VR status, patients were divided into LVR ( = 81) and non-LVR ( = 218) groups and clinical data were compared. A weighted logistic regression model was used to study the correlation between NHR and LVR. Weighted Cox proportional risk models were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) for major adverse cardiovascular events (MACE). And the NHR was analyzed using receiver operating characteristic (ROC) curves to predict the occurrence of postoperative LVR and MACE in STEMI patients. Restricted cubic spline (RCS) analysis was used to explore the linear or non-linear relationship between NHR and LVR or MACE. Cox survival analysis was used to assess the relationship between NHR, LVR and survival time.

RESULTS

Among the 299 STEMI patients enrolled in the study, LVR was observed in 81 patients after 24 months of follow-up. The LVR group had significantly higher NHR levels compared to the non-LVR group (8.19 ± 1.95 vs. 6.23 ± 1.91,  < 0.001). After adjusting for potential confounders, a significant positive correlation was found between NHR and LVR. Each standard deviation increase in NHR was associated with a 43% higher risk of MACE (HR: 1.43, 95% CI: 1.25-1.64,  < 0.001). ROC curve analysis demonstrated that NHR could predict both LVR (AUC: 0.762) and MACE (AUC: 0.722). An NHR cut-off value of >8.13 was significantly linked to an increased risk of MACE (HR: 4.30, 95% CI: 2.41-7.69).

CONCLUSIONS

NHR is an independent predictor of LVR and MACE after PCI in STEMI patients. Monitoring NHR may aid in identifying high-risk patients early, facilitating individualized treatment.

摘要

背景

中性粒细胞与高密度脂蛋白胆固醇比值(NHR)已被提出作为预测心血管事件的潜在标志物。然而,其在急性ST段抬高型心肌梗死(STEMI)患者经皮冠状动脉介入治疗(PCI)后的预后作用仍不明确。本研究旨在评估NHR对STEMI患者PCI术后左心室重构(LVR)和长期预后的预测价值。

方法

本回顾性研究纳入了299例行PCI的STEMI患者,并在术后随访24个月。入院时及心肌梗死(MI)后6个月进行超声心动图检查。LVR定义为左心室舒张末期容积(LVEDV)较基线增加至少20%。根据LVR状态,将患者分为LVR组(n = 81)和非LVR组(n = 218),并比较临床资料。采用加权逻辑回归模型研究NHR与LVR之间的相关性。采用加权Cox比例风险模型估计主要不良心血管事件(MACE)的风险比(HR)和95%置信区间(95%CI)。并使用受试者工作特征(ROC)曲线分析NHR,以预测STEMI患者术后LVR和MACE的发生。采用限制性立方样条(RCS)分析探讨NHR与LVR或MACE之间的线性或非线性关系。采用Cox生存分析评估NHR、LVR与生存时间之间的关系。

结果

在本研究纳入的299例STEMI患者中,随访24个月后有81例患者出现LVR。与非LVR组相比,LVR组的NHR水平显著更高(8.19±1.95 vs. 6.23±1.91,P<0.001)。在调整潜在混杂因素后,发现NHR与LVR之间存在显著正相关。NHR每增加一个标准差,MACE风险增加43%(HR:1.43,95%CI:1.25 - 1.64,P<0.001)。ROC曲线分析表明,NHR可预测LVR(AUC:0.762)和MACE(AUC:0.722)。NHR临界值>8.13与MACE风险增加显著相关(HR:4.30,95%CI:2.41 - 7.69)。

结论

NHR是STEMI患者PCI术后LVR和MACE的独立预测因子。监测NHR可能有助于早期识别高危患者,促进个体化治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c8c8/12003286/bdf344b905a8/fcvm-12-1497255-g001.jpg

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验