title: Progression of coronary artery calcium predicts all-cause mortality.
abstract: OBJECTIVE
  This study examined a large cohort to assess whether progression of coronary artery calcium (CAC) was associated with all-cause mortality, and which among 3 different methods to assess CAC progression provided the best estimate of risk.
  
  
  BACKGROUND
  Serial assessment of CAC scores has been proposed as a method to follow progression of coronary artery disease, and it has been suggested that excessive CAC progression may be a useful noninvasive predictor of the patient's risk of future events. However, the optimal method to measure calcium progression has not been well established.
  
  
  METHODS
  The study sample consisted of 4,609 consecutive asymptomatic individuals referred by primary physicians for CAC measurement with electron beam tomography, who underwent repeat screening. Three general statistical approaches were taken: 1) the absolute difference between follow-up and baseline CAC score; 2) percent annualized differences between follow-up and baseline CAC score; and 3) difference between square root of baseline and square root of follow-up CAC score >2.5 (the "SQRT method").
  
  
  RESULTS
  The average interscan time was 3.1 years, and there were 288 deaths. Progression of CAC was significantly associated with mortality regardless of the method used to assess progression (p < 0.0001). After adjusting for baseline score, age, sex, and time between scans, the best CAC progression model to predict mortality was the SQRT method (hazard ratio [HR]: 3.34; 95% confidence interval [CI]: 2.65 to 4.21; p < 0.0001), followed by a >15% yearly increase (HR: 2.98; 95% CI: 2.20 to 4.95; p < 0.0001). Progression was very limited and did not predict mortality in patients with baseline CAC = 0.
  
  
  CONCLUSIONS
  The CAC progression added incremental value in predicting all-cause mortality over baseline score, time between scans, demographics, and cardiovascular risk factors. Serial assessment may have clinical value in assessing plaque progression and future cardiovascular risk.
authors.affiliation: Department of Medicine, Los Angeles Biomedical Research Institute at Harbor-UCLA, Torrance, California 90502, USA. mbudoff@labiomed.org
authors.forename: Matthew J
authors.initials: MJ
authors.lastname: Budoff
authors.forename: John E
authors.initials: JE
authors.lastname: Hokanson
authors.forename: Khurram
authors.initials: K
authors.lastname: Nasir
authors.forename: Leslee J
authors.initials: LJ
authors.lastname: Shaw
authors.forename: Gregory L
authors.initials: GL
authors.lastname: Kinney
authors.forename: David
authors.initials: D
authors.lastname: Chow
authors.forename: Daniel
authors.initials: D
authors.lastname: Demoss
authors.forename: Vivek
authors.initials: V
authors.lastname: Nuguri
authors.forename: Vahid
authors.initials: V
authors.lastname: Nabavi
authors.forename: Raghu
authors.initials: R
authors.lastname: Ratakonda
authors.forename: Daniel S
authors.initials: DS
authors.lastname: Berman
authors.forename: Paolo
authors.initials: P
authors.lastname: Raggi
mesh_terms: D000368:Aged; D002114:Calcinosis / Q000000981:diagnostic imaging* / Q000401:mortality*; D016009:Chi-Square Distribution; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging* / Q000401:mortality*; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D011857:Radiographic Image Interpretation, Computer-Assisted; D018570:Risk Assessment; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors; D014057:Tomography, X-Ray Computed