A simple combination of clinical variables readily available before testing improves prediction of high-risk CAD over traditional risk assessments.
Can risk factors predict clinical outcomes of interest in incident CAD?
Traditional risk prediction models for incident CAD, such as the Framingham risk score (FRS) and pooled cohort equation, have incorporated a limited number of well-established risk factors for predicting clinical outcomes of interest [ 4, 5 ].
Why is risk prediction important in preventing atherosclerotic cardiovascular disease (ascvds)?
Risk prediction is a pillar of clinical epidemiology and a cornerstone in the primary prevention of atherosclerotic cardiovascular diseases (ASCVDs), with contemporary guidelines advocating for the use of prediction modeling tools to assess the future risk of developing a disease state and to tailor preventive therapies [ 14, 15 ].
Which rpm predicts CAD in the general population?
We identified 11 articles containing 28 RPMs predicting CAD in the general population. External validation in the UK Biobank, LifeLines, and PREVEND cohorts yielded comparable moderate-to-good discrimination and calibration for most RPMs, indicating that no single best-performing RPM could be distinguished.
