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Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact

Jul 2026 · Pacing and clinical electrophysiology : PACE · Vol 49, pp. 1540 - 1549 · 0 citations · 75 references
Medicine

TL;DR

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.

Abstract

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.

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