title: Framingham risk score and alternatives for prediction of coronary heart disease in older adults.
abstract: BACKGROUND
  Guidelines for the prevention of coronary heart disease (CHD) recommend use of Framingham-based risk scores that were developed in white middle-aged populations. It remains unclear whether and how CHD risk prediction might be improved among older adults. We aimed to compare the prognostic performance of the Framingham risk score (FRS), directly and after recalibration, with refit functions derived from the present cohort, as well as to assess the utility of adding other routinely available risk parameters to FRS.
  
  
  METHODS
  Among 2193 black and white older adults (mean age, 73.5 years) without pre-existing cardiovascular disease from the Health ABC cohort, we examined adjudicated CHD events, defined as incident myocardial infarction, CHD death, and hospitalization for angina or coronary revascularization.
  
  
  RESULTS
  During 8-year follow-up, 351 participants experienced CHD events. The FRS poorly discriminated between persons who experienced CHD events vs. not (C-index: 0.577 in women; 0.583 in men) and underestimated absolute risk prediction by 51% in women and 8% in men. Recalibration of the FRS improved absolute risk prediction, particulary for women. For both genders, refitting these functions substantially improved absolute risk prediction, with similar discrimination to the FRS. Results did not differ between whites and blacks. The addition of lifestyle variables, waist circumference and creatinine did not improve risk prediction beyond risk factors of the FRS.
  
  
  CONCLUSIONS
  The FRS underestimates CHD risk in older adults, particularly in women, although traditional risk factors remain the best predictors of CHD. Re-estimated risk functions using these factors improve accurate estimation of absolute risk.
authors.affiliation: Department of General Internal Medicine, Inselspital, University of Bern, Bern, Switzerland. Nicolas.Rodondi@insel.ch
authors.forename: Nicolas
authors.initials: N
authors.lastname: Rodondi
authors.forename: Isabella
authors.initials: I
authors.lastname: Locatelli
authors.forename: Drahomir
authors.initials: D
authors.lastname: Aujesky
authors.forename: Javed
authors.initials: J
authors.lastname: Butler
authors.forename: Eric
authors.initials: E
authors.lastname: Vittinghoff
authors.forename: Eleanor
authors.initials: E
authors.lastname: Simonsick
authors.forename: Suzanne
authors.initials: S
authors.lastname: Satterfield
authors.forename: Anne B
authors.initials: AB
authors.lastname: Newman
authors.forename: Peter W F
authors.initials: PW
authors.lastname: Wilson
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Pletcher
authors.forename: Douglas C
authors.initials: DC
authors.lastname: Bauer
mesh_terms: D001741:Black or African American; D000368:Aged; D001794:Blood Pressure; D002784:Cholesterol / Q000097:blood; D008076:Cholesterol, HDL / Q000097:blood; D015331:Cohort Studies; D003327:Coronary Disease / Q000175:diagnosis* / Q000517:prevention & control*; D048909:Diabetes Complications; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D012307:Risk Factors; D012907:Smoking; D044465:White People
chemical_list: D008076:Cholesterol, HDL; D002784:Cholesterol