title: Validating the Framingham Offspring Study equations for predicting incident diabetes mellitus.
abstract: BACKGROUND
  Investigators from the Framingham Offspring Study (FOS) recently proposed a new simple point score for estimating 8-year diabetes mellitus (DM) risk.
  
  
  OBJECTIVE
  To validate prediction models and to compare DM risk estimated by the point score with observed DM incidence.
  
  
  METHODS
  Longitudinal observational cohort study.
  
  
  METHODS
  We identified 20,644 members of Kaiser Permanente Northwest (KPNW) who had no prior evidence of DM and who had all data elements necessary to estimate the models recorded between July 1, 1999, and June 30, 2000. Patients were followed up through June 30, 2007. We reestimated the FOS logistic regression models in the total KPNW sample (cumulative DM incidence, 16.5%) and in a randomly selected subsample with incidence identical to that in the FOS (5.1%). We also compared DM risk predicted by the FOS point score with actual 8-year DM incidence observed in the KPNW samples.
  
  
  RESULTS
  The prediction models performed similarly in the FOS and KPNW samples, with almost identical odds ratios for independent variables and areas under the receiver operating characteristic curves for the models. The FOS point score substantially underestimated actual DM incidence in the total sample. In the subsample with DM incidence identical to that in the FOS sample, the point score was extremely accurate.
  
  
  CONCLUSIONS
  The accuracy of the point score requires that the underlying incidence of the individuals to whom it is applied be similar to the population from which it was derived. Accurate adaptation requires recalculating DM incidence at each point level.
authors.affiliation: Kaiser Permanente Center for Health Research, 3800 N Interstate Ave, Portland, OR 97227-1098, USA. greg.nichols@kpchr.org
authors.forename: Gregory A
authors.initials: GA
authors.lastname: Nichols
authors.forename: Jonathan B
authors.initials: JB
authors.lastname: Brown
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis; D001794:Blood Pressure; D008076:Cholesterol, HDL / Q000097:blood; D002984:Clinical Laboratory Information Systems; D003924:Diabetes Mellitus, Type 2 / Q000175:diagnosis / Q000453:epidemiology*; D005260:Female; D006279:Health Maintenance Organizations / Q000706:statistics & numerical data; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D015994:Incidence; D016015:Logistic Models; D008137:Longitudinal Studies; D008297:Male; D016347:Medical Records Systems, Computerized; D008875:Middle Aged; D009922:Oregon / Q000453:epidemiology; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data; D012307:Risk Factors
chemical_list: D001786:Blood Glucose; D008076:Cholesterol, HDL; D007004:Hypoglycemic Agents