Evolving biomarkers improve prediction of long‐term mortality in patients with stable coronary artery disease: the BIO ‐ VILCAD score

Objective Algorithms to predict the future long‐term risk of patients with stable coronary artery disease ( CAD ) are rare. The VI enna and Ludwigshafen CAD ( VILCAD ) risk score was one of the first scores specifically tailored for this clinically important patient population. The aim of this study was to refine risk prediction in stable CAD creating a new prediction model encompassing various pathophysiological pathways. Therefore, we assessed the predictive power of 135 novel biomarkers for long‐term mortality in patients with stable CAD . Design, setting and subjects We included 1275 patients with stable CAD from the LU dwigshafen RI sk and Cardiovascular health study with a median follow‐up of 9.8 years to investigate whether the predictive power of the VILCAD score could be improved by the addition of novel biomarkers. Additional biomarkers were selected in a bootstrapping procedure based on Cox regression to determine the most informative predictors of mortality. Results The final multivariable model encompassed nine clinical and biochemical markers: age, sex, left ventricular ejection fraction ( LVEF ), heart rate, N‐terminal pro‐brain natriuretic peptide, cystatin C, renin, 25 OH ‐vitamin D 3 and haemoglobin A1c. The extended VILCAD biomarker score achieved a significantly improved C‐statistic (0.78 vs. 0.73; P  = 0.035) and net reclassification index (14.9%; P  < 0.001) compared to the original VILCAD score. Omitting LVEF , which might not be readily measureable in clinical practice, slightly reduced the accuracy of the new BIO ‐ VILCAD score but still significantly improved risk classification (net reclassification improvement 12.5%; P  < 0.001). Conclusion The VILCAD biomarker score based on routine parameters complemented by novel biomarkers outperforms previous risk algorithms and allows more accurate classification of patients with stable CAD , enabling physicians to choose more personalized treatment regimens for their patients.