title: Long-term prognosis associated with coronary calcification: observations from a registry of 25,253 patients.
abstract: OBJECTIVE
  The purpose of this study was to develop risk-adjusted multivariable models that include risk factors and coronary artery calcium (CAC) scores measured with electron-beam tomography in asymptomatic patients for the prediction of all-cause mortality.
  
  
  BACKGROUND
  Several smaller studies have documented the efficacy of CAC testing for assessment of cardiovascular risk. Larger studies with longer follow-up will lend strength to the hypothesis that CAC testing will improve outcomes, cost-effectiveness, and safety of primary prevention efforts.
  
  
  METHODS
  We used an observational outcome study of a cohort of 25,253 consecutive, asymptomatic individuals referred by their primary physician for CAC scanning to assess cardiovascular risk. Multivariable Cox proportional hazards models were developed to predict all-cause mortality. Risk-adjusted models incorporated traditional risk factors for coronary disease and CAC scores.
  
  
  RESULTS
  The frequency of CAC scores was 44%, 14%, 20%, 13%, 6%, and 4% for scores of 0, 1 to 10, 11 to 100, 101 to 400, 401 to 1,000, and >1,000, respectively. During a mean follow-up of 6.8 +/- 3 years, the death rate was 2% (510 deaths). The CAC was an independent predictor of mortality in a multivariable model controlling for age, gender, ethnicity, and cardiac risk factors (model chi-square = 2,017, p < 0.0001). The addition of CAC to traditional risk factors increased the concordance index significantly (0.61 for risk factors vs. 0.81 for the CAC score, p < 0.0001). Risk-adjusted relative risk ratios for CAC were 2.2-, 4.5-, 6.4-, 9.2-, 10.4-, and 12.5-fold for scores of 11 to 100, 101 to 299, 300 to 399, 400 to 699, 700 to 999, and >1,000, respectively (p < 0.0001), when compared with a score of 0. Ten-year survival (after adjustment for risk factors, including age) was 99.4% for a CAC score of 0 and worsened to 87.8% for a score of >1,000 (p < 0.0001).
  
  
  CONCLUSIONS
  This large observational data series shows that CAC provides independent incremental information in addition to traditional risk factors in the prediction of all-cause mortality.
authors.affiliation: Harbor-UCLA Los Angeles Biomedical Research Institute, Torrance, California 90502, USA. mbudoff@labiomed.org
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Budoff
authors.forename: Leslee J
authors.initials: LJ
authors.lastname: Shaw
authors.forename: Sandy T
authors.initials: ST
authors.lastname: Liu
authors.forename: Steven R
authors.initials: SR
authors.lastname: Weinstein
authors.forename: Tristen P
authors.initials: TP
authors.lastname: Mosler
authors.forename: Philip H
authors.initials: PH
authors.lastname: Tseng
authors.forename: Ferdinand R
authors.initials: FR
authors.lastname: Flores
authors.forename: Tracy Q
authors.initials: TQ
authors.lastname: Callister
authors.forename: Paolo
authors.initials: P
authors.lastname: Raggi
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
mesh_terms: D000328:Adult; D000368:Aged; D002114:Calcinosis / Q000401:mortality*; D009202:Cardiomyopathies / Q000401:mortality*; D017023:Coronary Angiography; D003331:Coronary Vessels / Q000473:pathology*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011379:Prognosis; D016016:Proportional Hazards Models; D012372:ROC Curve; D012042:Registries; D018570:Risk Assessment; D012307:Risk Factors; D016019:Survival Analysis; D014057:Tomography, X-Ray Computed