title: Evaluating the prognostic value of positron-emission tomography myocardial perfusion imaging using automated software to calculate perfusion defect size.
abstract: BACKGROUND
  Myocardial perfusion imaging by positron-emission tomography (PET MPI) is regarded as a valid technique for the diagnosis of coronary artery disease (CAD), but the incremental prognostic value of PET MPI among individuals with known or suspected CAD is not firmly established.
  
  
  OBJECTIVE
  Myocardial perfusion defect sizes as measured by PET MPI using automated software will provide incremental prognostic value for cardiac and all-cause mortality.
  
  
  METHODS
  This study included 3739 individuals who underwent rest-stress rubidium-82 PET MPI for the evaluation of known or suspected CAD. Rest, stress, and stress-induced myocardial perfusion defect sizes were determined objectively by automated computer software. Study participants were followed for a mean of 5.2 years for cardiac and all-cause mortality. Cox proportional hazards models were developed to evaluate the incremental prognostic value of PET MPI.
  
  
  RESULTS
  A strong correlation was observed between perfusion defect sizes assessed visually and by automated software (r = 0.76). After adjusting for cardiac risk factors, known CAD, noncoronary vascular disease, and use of cardioprotective medications, stress perfusion defect size was strongly associated with cardiac death (P < 0.001). Rest perfusion defects demonstrated a stronger association with cardiac death (P < 0.001) than stress-induced perfusion defects (P = 0.01), yet both were highly significant. Similar patterns held for all-cause death.
  
  
  CONCLUSIONS
  The current study is the largest to date demonstrating PET MPI provides incremental prognostic value among individuals with known or suspected CAD. Automated calculation of perfusion defect sizes may provide valuable supplementary information to visual assessment.
authors.affiliation: Department of Center for Health Research, Geisinger Health System, Danville, PA 17822, USA. bawilliams2@geisinger.edu
authors.forename: Brent A
authors.initials: BA
authors.lastname: Williams
authors.forename: Joan M
authors.initials: JM
authors.lastname: Dorn
authors.forename: Michael J
authors.initials: MJ
authors.lastname: LaMonte
authors.forename: Richard P
authors.initials: RP
authors.lastname: Donahue
authors.forename: Maurizio
authors.initials: M
authors.lastname: Trevisan
authors.forename: Deborah A
authors.initials: DA
authors.lastname: Leonard
authors.forename: Robert S
authors.initials: RS
authors.lastname: Greene
authors.forename: Michael E
authors.initials: ME
authors.lastname: Merhige
mesh_terms: D000368:Aged; D001331:Automation; D002423:Cause of Death; D016009:Chi-Square Distribution; D003324:Coronary Artery Disease / Q000000981:diagnostic imaging* / Q000401:mortality / Q000503:physiopathology; D018450:Disease Progression; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008297:Male; D008875:Middle Aged; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D009518:New York / Q000453:epidemiology; D049268:Positron-Emission Tomography; D011237:Predictive Value of Tests; D011379:Prognosis; D016016:Proportional Hazards Models; D018570:Risk Assessment; D012307:Risk Factors; D012414:Rubidium Radioisotopes; D012984:Software; D013997:Time Factors
chemical_list: D012414:Rubidium Radioisotopes