{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T03:51:22Z","timestamp":1791258682306,"version":"4.1.0"},"reference-count":80,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T00:00:00Z","timestamp":1784592000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Pacing Clinical Electrophis"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>Atrial fibrillation (AF) is the most prevalent sustained arrhythmia worldwide. Acute myocardial infarction (AMI) is closely intertwined with AF through a bidirectional relationship: pre\u2010existing AF is associated with increased risk of AMI, while AMI predisposes to new\u2010onset atrial fibrillation (NOAF). This narrative review synthesizes evidence on the global burden of AF in the setting of AMI: pre\u2010existing AF, NOAF following AMI, and the prognostic implications of AF in AMI, encompassing the \u2018past, present and future\u2019 of AF in AMI. Pre\u2010existing AF is present in 3%\u20134% 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%\u201320% 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\u2010inflammation index and hs\u2010CRP), neurohormonal activation (NT\u2010proBNP), 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\u2010risk population.<\/jats:p>","DOI":"10.1111\/pace.70355","type":"journal-article","created":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T05:40:23Z","timestamp":1784698823000},"page":"1540-1549","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Atrial Fibrillation in Patients With Myocardial Infarction: Global Burden, Predictors, and Prognostic Impact"],"prefix":"10.1111","volume":"49","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-1299-6645","authenticated-orcid":false,"given":"Eddy","family":"Xiong","sequence":"first","affiliation":[{"name":"Department of Cardiology Royal Brisbane and Women's Hospital  Herston, Brisbane Queensland Australia"},{"name":"Faculty of Health, Medicine and Behavioural Sciences University of Queensland 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