医学
急性冠脉综合征
内科学
心脏病学
经皮冠状动脉介入治疗
风险评估
比例危险模型
索引(排版)
风险因素
传统PCI
查尔森共病指数
心源性猝死
心肌梗塞
急诊医学
试验预测值
体质指数
回顾性队列研究
梅德林
干预(咨询)
作者
Yuewen Qi,Xinchen Wang,Yan Liu,Ying Zhang,Qiyu Sun,Chen Wei,Ge Song,Jingyi Liu,Fei Shi,Lixian Sun
摘要
BACKGROUND AND AIMS: The geriatric nutritional risk index (GNRI) has shown good predictive value for some diseases. However, its association with major adverse cardiovascular events (MACEs) in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) remains uncertain. This study investigated the correlation between the GNRI and MACEs. PATIENTS AND METHODS: This was a prospective cohort study. We consecutively enrolled 1515 ACS patients who underwent PCI. The median duration of follow-up was 1000 days. The primary endpoints were MACEs, including all-cause mortality, severe heart failure rehospitalization, revascularization, acute myocardial infarction (AMI) recurrence, and restenosis/intrastent thrombosis. RESULTS: ROC curve analysis revealed an area under the curve of 0.603, with a GNRI cutoff value of 110.78. Cox regression analysis indicated that lower GNRI levels were independently associated with an increased risk of MACEs, a finding supported by risk score assessments. Kaplan-Meier survival curves and log-rank tests indicated significantly lower cumulative survival rates in patients with lower GNRI value. Lower GNRI levels were also correlated with a higher risk of rehospitalization and cardiovascular death, as confirmed by the competing risk model. These associations remained significant after adjustments (all p for interaction > 0.05). RCS analysis and trend tests (all p < 0.05) further supported these findings. CONCLUSION: GNRI, as an indicator of nutritional status, was correlated with the risk of MACEs in ACS patients undergoing PCI, particularly in predicting cardiac death and rehospitalization, suggesting that the GNRI level may serve as a valid indicator for predicting poor prognosis in patients with ACS undergoing PCI.
科研通智能强力驱动
Strongly Powered by AbleSci AI