title: Using administrative databases to calculate Framingham scores within a large health care organization.
abstract: OBJECTIVE
  Framingham calculators are typically implemented in 1-on-1 settings to determine if a patient is at high risk for development of cardiovascular disease in the next 10 years. Because health care administrative datasets are including more clinical information, we explored how well administrative data-derived Framingham scores could identify persons who would have stroke develop in the next year.
  
  
  METHODS
  Using a nested case-control design, we compared all 313 persons who had a first-time stroke at 5 Veterans Administration Medical Centers with a random sample of 25,361 persons who did not have a first-time stroke in 2008. We compared Framingham scores and risk using administrative data available at the end of 2007.
  
  
  RESULTS
  Stroke patients had higher risk profile than controls: older age, higher systolic blood pressure and total cholesterol, more likely to have diabetes, cardiovascular disease, left ventricular hypertrophy, and more likely to use treatment for blood pressure (P<0.05). The mean Framingham generalized cardiovascular disease score (18.0 versus 14.5) as well as the mean Framingham stroke-specific score (13.2 versus 10.2) was higher for stroke cases than controls (both P<0.0001). The c-statistic for the generalized cardiovascular disease score was 0.68 (95% CI, 0.65-0.70) and for the stroke score was 0.64 (95% CI, 0.62-0.67).
  
  
  CONCLUSIONS
  Persons who had a stroke develop in the next year had a worse Framingham risk profile, as determined by administrative data. Future studies should examine how to improve the stroke predictive tools and to identify the appropriate populations and uses for applying stroke risk predictive tools.
authors.affiliation: University of California, Los Angeles, CA, USA.
authors.forename: Olaniyi James
authors.initials: OJ
authors.lastname: Ekundayo
authors.forename: Stefanie D
authors.initials: SD
authors.lastname: Vassar
authors.forename: Linda S
authors.initials: LS
authors.lastname: Williams
authors.forename: Dawn M
authors.initials: DM
authors.lastname: Bravata
authors.forename: Eric M
authors.initials: EM
authors.lastname: Cheng
mesh_terms: D000368:Aged; D002318:Cardiovascular Diseases / Q000175:diagnosis* / Q000453:epidemiology; D016022:Case-Control Studies; D003625:Data Collection; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D005260:Female; D006787:Hospitals, Veterans; D006801:Humans; D008297:Male; D008875:Middle Aged; D011897:Random Allocation; D012306:Risk; D018570:Risk Assessment; D020521:Stroke / Q000175:diagnosis* / Q000453:epidemiology; D016896:Treatment Outcome; D014481:United States