Machine learning analysis of plasma proteins from 9,300 UK Biobank participants with established cardiovascular disease achieved 74.3% accuracy (C-index 0.743) predicting recurrent heart attacks and strokes, substantially outperforming the standard SMART2 clinical risk score's 65.3% accuracy. The protein-based model identified patients with 10-year recurrence rates ranging from 2.4% in the lowest-risk quintile to 27.4% in the highest-risk group. This represents a paradigm shift in cardiovascular risk assessment, moving beyond traditional risk factors like cholesterol and blood pressure toward direct molecular signatures of disease activity. The findings could transform secondary prevention by enabling precise identification of patients who need aggressive intervention versus those at genuinely low risk despite having cardiovascular disease. However, this preprint awaits peer review and validation studies will be crucial before clinical implementation. The researchers developed a streamlined panel using existing clinical-grade protein assays, suggesting practical deployment may be feasible. While promising, the study population was predominantly from the UK Biobank, and broader validation across diverse populations and healthcare systems remains essential.