Abstract

Acute Coronary Syndrome (ACS) remains a leading cause of morbidity and mortality worldwide despite significant advances in diagnosis, pharmacotherapy, and revascularization strategies. Early risk assessment is essential for guiding treatment decisions, optimizing resource allocation, and improving patient outcomes. Risk stratification models provide clinicians with standardized tools to estimate short- and long-term risks of mortality, recurrent ischemic events, bleeding complications, and major adverse cardiovascular events (MACE). Several validated scoring systems, including the GRACE, TIMI, PURSUIT, HEART, and CRUSADE scores, have become integral components of contemporary ACS management. This review examines the development, validation, strengths, limitations, and clinical applications of major ACS risk stratification models. Additionally, emerging approaches involving artificial intelligence, machine learning, biomarker integration, and precision medicine are discussed. An Integrated ACS Risk Assessment Framework (IARAF) is proposed to support individualized patient management and improve clinical outcomes.