title: Evolving biomarkers improve prediction of long-term mortality in patients with stable coronary artery disease: the BIO-VILCAD score.
abstract: OBJECTIVE
  Algorithms to predict the future long-term risk of patients with stable coronary artery disease (CAD) are rare. The VIenna 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.
  
  
  METHODS
  We included 1275 patients with stable CAD from the LUdwigshafen RIsk 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, 25OH-vitamin D3 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).
  
  
  CONCLUSIONS
  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.
authors.forename: M E
authors.initials: ME
authors.lastname: Kleber
authors.forename: G
authors.initials: G
authors.lastname: Goliasch
authors.forename: T B
authors.initials: TB
authors.lastname: Grammer
authors.forename: S
authors.initials: S
authors.lastname: Pilz
authors.forename: A
authors.initials: A
authors.lastname: Tomaschitz
authors.forename: G
authors.initials: G
authors.lastname: Silbernagel
authors.forename: G
authors.initials: G
authors.lastname: Maurer
authors.forename: W
authors.initials: W
authors.lastname: März
authors.forename: A
authors.initials: A
authors.lastname: Niessner
mesh_terms: D000465:Algorithms; D015415:Biomarkers / Q000097:blood*; D050071:Bone Density Conservation Agents / Q000097:blood; D002762:Cholecalciferol / Q000097:blood; D003324:Coronary Artery Disease / Q000097:blood / Q000175:diagnosis* / Q000401:mortality*; D055316:Cystatin C / Q000097:blood; D005500:Follow-Up Studies; D006442:Glycated Hemoglobin / Q000378:metabolism; D006339:Heart Rate; D006801:Humans; D045283:Natriuretic Agents / Q000097:blood; D020097:Natriuretic Peptide, Brain / Q000097:blood; D011237:Predictive Value of Tests; D011379:Prognosis; D012083:Renin / Q000097:blood; D018570:Risk Assessment; D012307:Risk Factors; D012680:Sensitivity and Specificity; D013318:Stroke Volume
chemical_list: D015415:Biomarkers; D050071:Bone Density Conservation Agents; D002762:Cholecalciferol; D055316:Cystatin C; D006442:Glycated Hemoglobin; D045283:Natriuretic Agents; D020097:Natriuretic Peptide, Brain; D012083:Renin