Serum uric acid significantly improves the accuracy of cardiovascular risk score models.

ABSTRACT

AIMS: This study evaluated the impact of serum uric acid (sUA) on the accuracy of pooled cohort equations (PCE) model, Systematic COronary Risk Evaluation 2 (SCORE2), and SCORE2-older persons. METHODS AND RESULTS: We evaluated 19 769 asymptomatic self-referred adults aged 40-79 years free of cardiovascular disease and diabetes who were screened annually in a preventive healthcare setting. sUA levels were expressed as a continuous as well as a dichotomous variable (upper sex-specific tertiles defined as high sUA). The primary endpoint was the composite of death, acute coronary syndrome, or stroke, after excluding subjects diagnosed with metastatic cancer during follow-up. Mean age was 50 ± 8 years and 69% were men. During the median follow-up of 6 years, 1658 (8%) subjects reached the study endpoint. PCE, SCORE2, and high sUA were independently associated with the study endpoint in a multivariable model (P < 0.001 for all). Continuous net reclassification improvement analysis showed a 13% improvement in the accuracy of classification when high sUA was added to either PCE or SCORE2 model (P < 0.001 for both). sUA remained independently associated with the study endpoint among normal-weight subjects in the SCORE2 model (HR 1.3, 95% CI 1.1-1.6) but not among overweight individuals (P for interaction = 0.01). Subgroup analysis resulted in a significant 16-20% improvement in the model performance among normal-weight and low-risk subjects (P < 0.001 for PCE; P = 0.026 and P < 0.001 for SCORE2, respectively). CONCLUSION: sUA significantly improves the classification accuracy of PCE and SCORE2 models. This effect is especially pronounced among normal-weight and low-risk subjects.