Choosing Cardiovascular Assessment and Risk-Prediction Tools Across the Care Pathway

Cardiovascular diseases remain a leading cause of morbidity and mortality worldwide, making accurate and efficient risk stratification essential across the care pathway. Numerous tools support emergency triage, disease assessment, procedural decision-making, long-term prevention, atrial fibrillation management, and evaluation of functional status, but no single model is applicable across all clinical settings or populations. This clinician-oriented review categorizes and critically compares commonly used cardiovascular assessment and risk-prediction tools, including History, Electrocardiogram, Age, Risk factors, Troponin, Global Registry of Acute Coronary Events, and Thrombolysis in Myocardial Infarction for emergency triage; Agatston, Multi-Ethnic Study of Atherosclerosis, and Synergy Between Percutaneous Coronary Intervention with Taxus and Cardiac Surgery for anatomical and clinical assessment; European System for Cardiac Operative Risk Evaluation, Society of Thoracic Surgeons, and Revised Cardiac Risk Index, for procedural risk; and Framingham, the pooled cohort equations implemented in the Atherosclerotic Cardiovascular Disease, Risk Estimator, Systematic Coronary Risk Evaluation 2, QResearch Cardiovascular Risk Algorithm 3, and Predicting Risk of Cardiovascular Disease EVENTs for long-term prediction. Congestive heart failure, hypertension, age ≥75, diabetes, stroke/transient ischemic attack (2 points), congestive heart failure, hypertension, age ≥75 (2 points), diabetes, stroke/TIA (2 points), vascular disease, age 65-74, sex category (female), and hypertension, abnormal renal/liver function, stroke, bleeding history, labile international normalized ratio, elderly, drugs/alcohol, are discussed in the context of atrial fibrillation, alongside New York Heart Association and the Seattle Angina Questionnaire as measures of functional status and quality of life. For each tool, we examine its clinical purpose, principal variables, strengths, limitations, practical applications, and areas of overlap. Differences in implementation across healthcare systems and patient populations are considered to support context-appropriate selection. Persistent limitations include poor standardization, reliance on data that may be unavailable in resource-constrained settings, limited transportability, and incomplete integration into clinical workflows. Greater harmonization, external validation, digital automation, and the careful incorporation of biomarkers and artificial intelligence may support more accessible, dynamic, and personalized cardiovascular risk assessment.