BACKGROUND: In clinical practice, factors associated with cardiovascular disease (CVD) like albuminuria, education level, or coronary artery calcium are often known, but not incorporated in cardiovascular risk prediction models. The aims of the current study were to evaluate a methodology for the flexible addition of risk modifying characteristics on top of SCORE2 and to quantify the added value of several clinically relevant risk modifying characteristics.
METHODS AND RESULTS: Individuals without previous CVD or DM were included from the UK Biobank, ARIC, MESA, EPIC-NL and HNR studies (n=409,757) in whom 16,166 CVD events and 19,149 non-cardiovascular deaths were observed over exactly 10.0 years of follow-up. The effect of each possible risk modifying characteristic was derived using competing risk-adjusted Fine and Gray models. The risk modifying characteristics were applied to individual predictions with a flexible method using the population prevalence and the subdistribution hazard ratio (SHR) of the relevant. Risk modifying characteristics that increased discrimination most were CAC percentile with 0.0198 (95%CI 0.0115; 0.0281) and hs-Troponin-T with 0.0100 (95%CI 0.0063; 0.0137). External validation was performed in the CPRD cohort (UK, n = 518,015, 12,675 CVD events). Adjustment of SCORE2 predicted risks with both single and multiple risk modifiers did not negatively affect calibration and led to a modest increase in discrimination (0.740 (95%CI 0.736-0.745) versus unimproved SCORE2 risk C-index 0.737 [95%CI 0.732-0.741]).
CONCLUSIONS: The current paper presents a method on how to integrate possible risk modifying characteristics that are not included in existing CVD risk models for the prediction of CVD event risk in apparently healthy people. This flexible methodology improves the accuracy of predicted risks and increases applicability of prediction models for individuals with additional risk known modifiers.
