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Although implantable cardioverter\u2010defibrillator (ICD) therapy, guided primarily by left ventricular ejection fraction (LVEF), has been proven to reduce mortality in landmark trials (e.g., MADIT, MUSTT, and MADIT\u2010II), the modest sensitivity and specificity of LVEF limit its utility as a sole risk stratifier. Emerging clinical trends highlight the progression from traditional LVEF\u2010based models to more comprehensive risk assessment strategies. This review critically examines additional modalities, including advanced cardiac magnetic resonance imaging, echocardiography, myocardial biomarkers, detailed electrocardiographic parameters, and assessments of myocardial tissue heterogeneity, as well as patient\u2010specific factors such as age, sex, and prehospital cardiac arrest history. Furthermore, we discuss recent advances, particularly the integration of artificial intelligence and machine learning algorithms, that help to enhance risk prediction and optimize ICD therapy decisions. By combining these novel approaches into a comprehensive risk assessment framework, our review promotes a more personalized strategy to identify STEMI patients most likely to benefit from ICD implantation, improve survival outcomes, and reduce unnecessary interventions. Future research should focus on validating these integrative models in large prospective cohorts and refining current guidelines for ICD therapy post\u2010STEMI.<\/jats:p>","DOI":"10.1155\/crp\/1981993","type":"journal-article","created":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T05:47:32Z","timestamp":1781502452000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Narrative Review of Risk Assessment Approaches for Implantable Defibrillator Therapy to Prevent Sudden Cardiac Death in Acute Myocardial 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