实用心电与临床诊疗2025，Vol.34Issue(6)：792-798,804,8.DOI:10.13308/j.issn.2097-5716.2025.06.003
合并心肌梗死对脓毒症患者临床特征及预后的影响:一项基于MIMIC-Ⅳ数据库的实证分析
Impact of myocardial infarction on clinical features and outcomes in septic patients:a real-world analysis based on MIMIC-Ⅳ database
摘要
Abstract
Objective To systematically analyze the clinical characteristics of septic patients complicated by myocardial infarction(MI),evaluate its impact on in-hospital mortality,and to explore related influencing factors and prognostic indicators.Methods Based on the MIMIC-Ⅳ v2.0 database,a total of 12 838 adult septic patients meeting the Sepsis-3 diagnostic criteria were included.They were divided into two groups according to the presence of MI:a sepsis with MI group(n=2 236)and a sepsis-alone group(n=10 602).Demographic data,clinical indicators,and outcomes were compared between the two groups.Multivariate Logistic regression and random forest models were used to identify key predictors of in-hospital mortality,and subgroup stratification analysis was performed.Results Septic patients with MI were older,had a higher proportion of males,a significantly higher prevalence of diabetes and chronic obstructive pulmonary disease(COPD),and higher APACHE-Ⅲ and SAPS-Ⅱscores than those in the sepsis-alone group(all P＜0.05).The in-hospital mortality rate in the sepsis with MI group was 25.2％,significantly higher than that in the sepsis-alone group(19.9％,P＜0.001).Multivariate Logistic regression analysis showed that MI was an independent risk factor of in-hospital mortality(OR=1.26,95％CI 1.11-1.43).Subgroup analysis revealed that in patients with MI,COPD,hypoalbuminemia,and low platelet count were significantly associated with an increased risk of death,while diabetes showed a protective effect in patients without MI.The random forest model further validated the importance of these variables in predicting mortality risk.Additionally,statin use was associated with reduced 90-day mortality in septic patients with MI.Conclusion MI significantly aggravates the condition of septic patients and increases the risk of in-hospital mortality,highlighting the importance of early risk stratification and intervention.Predictive models combining multivariate statistics and machine learning may facilitate more accurate individualized prognostic assessment.关键词
脓毒症/心肌梗死/预后/MIMIC-Ⅳ数据库/风险因素/机器学习Key words
sepsis/myocardial infarction/prognosis/MIMIC-Ⅳ database/risk factor/machine learning分类
医药卫生引用本文复制引用
BAI Shasha,DU Yingqiang,LI Mohan,LI Biao..合并心肌梗死对脓毒症患者临床特征及预后的影响:一项基于MIMIC-Ⅳ数据库的实证分析[J].实用心电与临床诊疗,2025,34(6):792-798,804,8.基金项目
江苏省自然科学基金青年基金资助项目(BK20210101) （BK20210101）
