We suggest against the routine use of additional risk markers (e.g., high-sensitivity C-reactive protein, ankle-brachial index, coronary artery calcium) when assessing cardiovascular risk. (Weak against | Reviewed, New-replaced) Discussion Traditional CV risk assessment relies on the use of multivariate models that integrate data from known risk factors and biomarkers to predict the development of CVD in an individual patient. While numerous models exist, all tend to misestimate (i.e., under- or over-estimate) risk.[26,32]In addition, patients and providers remain interested in improving risk prediction for populations identified as intermediate risk by the currently used models. The use of non-traditional biomarkers to improve the estimation of CV risk assessment has been extensively studied. Non-traditional biomarkers include genetic, serologic, psychosocial, and physiologic markers (e.g., ankle-brachial index [ABI], electrocardiogram [ECG], apolipoproteins [Apo], high-sensitivity C-reactive protein [hsCRP], physical activity, and socioeconomic status). Numerous biomarkers and other variables were assessed in this review, including ABI, lipoprotein(a), Apo A1, Apo B, CAC score, ECG, fibrinogen, HDL-C, N-terminal-pro hormone brain natriuretic peptide (NT-proBNP), hsCRP, physical activity, socioeconomic status, and Apo C3.[29,33-35] Several biomarkers have demonstrated slight additive predictive risk (e.g., hsCRP, CAC, and ABI); however, the model performance studies that demonstrate these differences are hampered by low or very low quality evidence. Also, the potential clinical impact is unknown. For example, the addition of ABI to either the FRS or PCE improves model discrimination (change in AUC: -0.006 0.112 for FRS, 0.01 for PCE) but does not significantly improve calibration or risk reclassification.[29] The addition of hsCRP to either the FRS or PCE results in minimally improved discrimination (changes in AUC: 0 0.027).[29] Other than CAC scoring, none of the other risk markers reviewed demonstrated any additive predictive risk value to traditional risk assessment models. 