The presence or absence of standard modifiable cardiovascular risk factors in patients with myocardial infarction impacts long-term but not 30-day mortality: a UK Biobank prospective cohort study.

IF 8.4 2区 医学 Q1 CARDIAC & CARDIOVASCULAR SYSTEMS
Wen Kai Wong, Fumihiko Takeuchi, Li Lian Kuan, Stephen J Nicholls, Karlheinz Peter, Derek P Chew
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引用次数: 0

Abstract

Aims: Prior studies reported higher early mortality after acute myocardial infarction (MI) in patients without standard modifiable cardiovascular risk factors (SMuRFs), warranting further validation. We aimed to evaluate whether SMuRF-absence is associated with increased 30-day cardiovascular mortality following MI.

Methods and results: We conducted a population-based cohort study using UK Biobank data (n = 487 177). Incident MI cases occurring between 2006 and 2022 were identified through linkage to hospital and death registries. Standard modifiable cardiovascular risk factors (diabetes, hypertension, hypercholesterolaemia, current smoker) were defined at baseline and continuously assessed until MI onset. Thirty-day mortality following MI was estimated using Cox proportional hazards models, adjusted for sociodemographic, clinical, and cardiogenomic variables, were used to estimate 30-day mortality risks. Logistic regression model was used to estimate mortality risk at 10 years post-MI. Among 15 463 patients experiencing an MI (1034 without SMuRFs), SMuRF-absence was not significantly associated with 30-day mortality (HR: 0.82, 95% CI: 0.65-1.04, P = 0.103). Propensity score-matched analyses supported these findings (HR: 0.95, 95% CI: 0.69-1.29, P = 0.729). Further analyses stratified by distinct time intervals (2006-2022) revealed no significant modification of this association by advancements in acute MI management. Interaction analyses indicated no significant effect modification by sex, age, socioeconomic status, or period of MI occurrence. However, extended analysis to 10 years revealed that SMuRF absence was significantly associated with lower long-term mortality (OR: 0.61, 95% CI: 0.49-0.75, P < 0.01).

Conclusion: In this population-based cohort, SMuRF status significantly impacted long-term but not short-term mortality following MI, indicating early survival is predominantly driven by acute-phase factors rather than baseline cardiovascular risk profiles.

英国生物银行的一项前瞻性队列研究表明,心肌梗死患者存在或不存在标准可改变的心血管危险因素会影响长期死亡率,但不会影响30天死亡率。
目的:先前的研究报告了没有标准可改变心血管危险因素(smurf)的急性心肌梗死(MI)患者的早期死亡率更高,需要进一步验证。我们的目的是评估smurf缺失是否与心肌梗死后30天心血管死亡率增加有关。方法和结果:我们使用英国生物银行的数据进行了一项基于人群的队列研究(n = 487 177)。通过与医院和死亡登记处的联系,确定了2006年至2022年期间发生的意外性心肌梗死病例。标准可改变的心血管危险因素(糖尿病、高血压、高胆固醇血症、当前吸烟者)在基线时被定义,并持续评估直到心肌梗死发作。使用Cox比例风险模型估计心肌梗死后30天死亡率,并根据社会人口学、临床和心脏基因组变量进行调整,用于估计30天死亡率风险。采用Logistic回归模型估计心肌梗死后10年的死亡风险。在15463例MI患者(1034例无smurf)中,smurf缺失与30天死亡率无显著相关性(HR: 0.82, 95% CI: 0.65-1.04, P = 0.103)。倾向评分匹配分析支持这些发现(HR: 0.95, 95% CI: 0.69-1.29, P = 0.729)。根据不同的时间间隔(2006-2022)进行的进一步分析显示,急性心肌梗死管理的进步并没有显著改变这种关联。相互作用分析显示,性别、年龄、社会经济地位或心肌梗死发生时间没有显著的影响。然而,延长至10年的分析显示,SMuRF缺失与较低的长期死亡率显著相关(OR: 0.61, 95% CI: 0.49-0.75, P < 0.01)。结论:在这个基于人群的队列中,SMuRF状态显著影响心肌梗死后的长期而非短期死亡率,表明早期生存主要由急性期因素驱动,而不是基线心血管风险概况。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
European journal of preventive cardiology
European journal of preventive cardiology CARDIAC & CARDIOVASCULAR SYSTEMS-
CiteScore
12.50
自引率
12.00%
发文量
601
审稿时长
3-8 weeks
期刊介绍: European Journal of Preventive Cardiology (EJPC) is an official journal of the European Society of Cardiology (ESC) and the European Association of Preventive Cardiology (EAPC). The journal covers a wide range of scientific, clinical, and public health disciplines related to cardiovascular disease prevention, risk factor management, cardiovascular rehabilitation, population science and public health, and exercise physiology. The categories covered by the journal include classical risk factors and treatment, lifestyle risk factors, non-modifiable cardiovascular risk factors, cardiovascular conditions, concomitant pathological conditions, sport cardiology, diagnostic tests, care settings, epidemiology, pharmacology and pharmacotherapy, machine learning, and artificial intelligence.
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