心肌梗死患者的心房颤动:总体负担、预测因素和预后影响
Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact.
Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact.
心房颤动(AF)是全球最常见的持续性心律失常。急性心肌梗死(AMI)与AF通过双向关系密切交织:既往存在的AF与AMI风险增加相关,而AMI易导致新发心房颤动(NOAF)。本叙述性综述综合了关于AMI背景下AF全球负担的证据:既往存在的AF、AMI后NOAF以及AF在AMI中的预后意义,涵盖AF在AMI中的“过去、现在和未来”。既往存在的AF见于3%-4%的AMI患者,并已证实与急性冠脉综合征存在独立关联,其介导机制包括全身性炎症、促血栓状态、需求性缺血和冠状动脉血栓栓塞。NOAF使5%-20%的AMI病例复杂化,发病高峰在最初六个月内。预测因素包括年龄、合并症如慢性肾脏病、炎症标志物(包括全身免疫炎症指数和hs-CRP)、神经激素激活(NT-proBNP)、舒张功能障碍和左心房应变的超声心动图参数,以及心电图特征如QRS碎裂。已开发出多种NOAF预测模型,其判别性能各异。新兴研究表明,机器学习可能提供更优的风险分层,但仍需进一步研究。AMI背景下的AF显著增加死亡、卒中、出血和心力衰竭的风险,其中NOAF显示出特别强的预后意义。提高对预测标志物的识别,同时开发量身定制的预后模型,对于指导抗血栓治疗和优化这一高危人群的结局至关重要。
Atrial fibrillation (AF) is the most prevalent sustained arrhythmia worldwide. Acute myocardial infarction (AMI) is closely intertwined with AF through a bidirectional relationship: pre-existing AF is associated with increased risk of AMI, while AMI predisposes to new-onset atrial fibrillation (NOAF). This narrative review synthesizes evidence on the global burden of AF in the setting of AMI: pre-existing AF, NOAF following AMI, and the prognostic implications of AF in AMI, encompassing the 'past, present and future' of AF in AMI. Pre-existing AF is present in 3%-4% of patients with AMI and has demonstrated an independent association with acute coronary syndromes, mediated by systemic inflammation, prothrombotic states, demand ischemia, and coronary thromboembolism. NOAF complicates 5%-20% of AMI cases, with peak onset within the first six months. Predictive factors include age, comorbidities such as chronic kidney disease, markers of inflammation (including systemic immune-inflammation index and hs-CRP), neurohormonal activation (NT-proBNP), echocardiographic parameters of diastolic dysfunction and left atrial strain, and electrocardiographic features such as QRS fragmentation. Multiple predictive models for NOAF have been developed with varying discriminatory performance. Emerging research suggests that machine learning may provide superior risk stratification, though further study is needed. AF in the context of AMI confers substantially increased risk of mortality, stroke, bleeding, and heart failure, with NOAF showing particularly strong prognostic significance. Improved recognition of predictive markers, alongside development of tailored prognostic models, is essential to guide antithrombotic therapy and optimize outcomes in this high-risk population.
Eddy Xiong, Sandhir B Prasad, John J Atherton, Jason Davis, Paul Martin, Daniel Lancini. Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact.. Pacing and clinical electrophysiology : PACE. 2026; doi:10.1111/pace.70355.
Eddy Xiong, Sandhir B Prasad, John J Atherton, Jason Davis, Paul Martin, Daniel Lancini (2026). Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact.. Pacing and clinical electrophysiology : PACE. https://doi.org/10.1111/pace.70355
Eddy Xiong, Sandhir B Prasad, John J Atherton, Jason Davis, Paul Martin, Daniel Lancini. Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact.[J]. Pacing and clinical electrophysiology : PACE, 2026 doi:10.1111/pace.70355.
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