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高海拔地区急性心肌梗死合并室间隔破裂患者死亡的危险因素及预测模型

Risk factors and predictive model for mortality in acute myocardial infarction with ventricular septal rupture at high altitudes.

作者信息

Zhang Li-Hong, Cen Zhi-Fu, Qiao Qian, Ye Xue-Rui, Cheng Lu, Liu Gui-Qin, Liu Yi, Zhang Xing-Qiang, Pan Xian-Feng, Zhang Hao-Ling, Zhang Jing-Jing

机构信息

Department of Cardiovascular Medicine, Fuwai Yunnan Hospital, Chinese Academy of Medical Sciences, Kunming 650000, Yunnan Province, China.

Affiliated Cardiovascular Hospital of Kunming Medical University, Kunming 650000, Yunnan Province, China.

出版信息

World J Cardiol. 2025 Jul 26;17(7):109044. doi: 10.4330/wjc.v17.i7.109044.

DOI:10.4330/wjc.v17.i7.109044
PMID:40741023
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12304822/
Abstract

BACKGROUND

Acute myocardial infarction (AMI) combined with ventricular septal perforation (VSR) is still a highly fatal condition in the era of reperfusion therapy. The incidence rate has decreased to 0.2%-0.4% due to the popularization of percutaneous coronary intervention. However, the risk is significantly increased for those who fail to undergo revascularization in time, and the mortality rate remains high. The current core contradiction in clinical practice lies in the selection of surgical timing, and the disparity in medical resources significantly affects prognosis. There is an urgent need to optimize the identification of high-risk populations and individualized treatment strategies.

AIM

To investigate the clinical features, determine the prognostic factors, and develop a predictive model for 30-day mortality in patients with acute myocardial infarction complicated by ventricular septal rupture (AMI-VSR) residing in high-altitude regions.

METHODS

This study retrospectively analyzed 48 AMI-VSR patients admitted to a Yunnan hospital from 2017 to 2024, with the establishment of survival ( = 30) and mortality ( = 18) groups based on patients' survival status. Risk factors were identified by univariate and multivariate logistic regression analyses. A nomogram model was developed using R software and validated receiver operating characteristic (ROC) analysis and calibration curves.

RESULTS

Age, uric acid (UA), interleukin-6 (IL-6), and low hemoglobin (Hb) were independent risk factors for 30-day mortality (odds ratios: 1.147, 1.006, 1.034, and 0.941, respectively; < 0.05). The nomogram demonstrated excellent discrimination (area under the ROC curve = 0.939) and calibration (Hosmer-Lemeshow ² = 2.268, = 0.971). In addition, patients' poor outcomes could be synergistically predicted by IL-6 and UA, advanced age, and reduced Hb.

CONCLUSION

This study highlights age, UA, IL-6, and Hb as critical predictors of mortality in AMI-VSR patients at high altitudes. The validated nomogram provides a practical tool for early risk stratification and tailored interventions, addressing gaps in managing this high-risk population in resource-limited settings.

摘要

背景

在再灌注治疗时代,急性心肌梗死(AMI)合并室间隔穿孔(VSR)仍然是一种高致死性疾病。由于经皮冠状动脉介入治疗的普及,其发病率已降至0.2%-0.4%。然而,对于未能及时进行血运重建的患者,风险显著增加,死亡率仍然很高。临床实践中当前的核心矛盾在于手术时机的选择,而医疗资源的差异显著影响预后。迫切需要优化高危人群的识别和个体化治疗策略。

目的

探讨高原地区急性心肌梗死合并室间隔破裂(AMI-VSR)患者的临床特征,确定预后因素,并建立30天死亡率的预测模型。

方法

本研究回顾性分析了2017年至2024年在云南某医院收治的48例AMI-VSR患者,根据患者的生存状态分为生存组(n = 30)和死亡组(n = 18)。通过单因素和多因素逻辑回归分析确定危险因素。使用R软件建立列线图模型,并通过受试者操作特征(ROC)分析和校准曲线进行验证。

结果

年龄、尿酸(UA)、白细胞介素-6(IL-6)和低血红蛋白(Hb)是30天死亡率的独立危险因素(比值比分别为1.147、1.006、1.034和0.941;P < 0.05)。列线图显示出良好的区分度(ROC曲线下面积 = 0.939)和校准度(Hosmer-Lemeshow χ² = 2.268,P = 0.971)。此外,IL-6和UA、高龄以及Hb降低可协同预测患者的不良预后。

结论

本研究强调年龄、UA、IL-6和Hb是高原地区AMI-VSR患者死亡率的关键预测因素。经过验证的列线图为早期风险分层和个性化干预提供了实用工具,弥补了资源有限环境下管理这一高危人群的不足。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae98/12304822/85d26607bb96/wjc-17-7-109044-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae98/12304822/454d1b3c48ee/wjc-17-7-109044-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae98/12304822/3c6ddaf0bd9f/wjc-17-7-109044-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae98/12304822/1a6fcef81cb0/wjc-17-7-109044-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae98/12304822/85d26607bb96/wjc-17-7-109044-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae98/12304822/454d1b3c48ee/wjc-17-7-109044-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae98/12304822/3c6ddaf0bd9f/wjc-17-7-109044-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae98/12304822/1a6fcef81cb0/wjc-17-7-109044-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ae98/12304822/85d26607bb96/wjc-17-7-109044-g004.jpg

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