title: Risk prediction of incident coronary heart disease in The Netherlands: re-estimation and improvement of the SCORE risk function.
abstract: OBJECTIVE
  To re-estimate the SCORE risk function using individual data on risk factors and coronary heart disease (CHD) incidence from the Dutch Cardiovascular Registry Maastricht (CAREMA) population-based cohort study; to evaluate changes that may improve risk prediction after re-estimation; and to compare the performance of the resulting CAREMA risk function with that of existing risk scores.
  
  
  RESULTS
  The cohort consisted of 21,148 participants, born in 1927-1977 and randomly sampled from the Maastricht region in 1987-1997. After follow-up (median 10.9 years), 783 incident CHD cases occurred. Model performance was assessed by discrimination and calibration. The additional value of including other risk factors or current risk factors in a different manner was evaluated using the net reclassification index (NRI). The c statistic of the re-estimated SCORE model was 0.799 (95% CI 0.782-0.816). Separating the total/high-density lipoprotein (HDL) cholesterol ratio into total and HDL cholesterol levels did not improve the c statistic (p = 0.22), but reclassified 6.0% of the participants into a more appropriate risk category (p < 0.001) compared with the re-estimated model. The resulting CAREMA function reclassified 28% of the participants into a more appropriate risk category than the Framingham score. Compared with the SCORE functions for high- and low-risk regions, the NRIs were 28% and 35%, respectively, which can largely be explained by the difference in outcome definition (CHD incidence vs. CHD mortality).
  
  
  CONCLUSIONS
  In this Dutch population, a re-estimated SCORE function with total and HDL cholesterol levels instead of the cholesterol ratio can be used for the risk prediction of CHD incidence.
authors.affiliation: Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Maastricht University, Maastricht, The Netherlands. Audrey.Merry@maastrichtuniversity.nl
authors.forename: Audrey H H
authors.initials: AH
authors.lastname: Merry
authors.forename: Jolanda M A
authors.initials: JM
authors.lastname: Boer
authors.forename: Leo J
authors.initials: LJ
authors.lastname: Schouten
authors.forename: Ton
authors.initials: T
authors.lastname: Ambergen
authors.forename: Ewout W
authors.initials: EW
authors.lastname: Steyerberg
authors.forename: Edith J M
authors.initials: EJ
authors.lastname: Feskens
authors.forename: W M Monique
authors.initials: WM
authors.lastname: Verschuren
authors.forename: Anton P M
authors.initials: AP
authors.lastname: Gorgels
authors.forename: Piet A
authors.initials: PA
authors.lastname: van den Brandt
mesh_terms: D000328:Adult; D015415:Biomarkers / Q000097:blood; D001794:Blood Pressure; D002784:Cholesterol / Q000097:blood; D008076:Cholesterol, HDL / Q000097:blood; D003327:Coronary Disease / Q000097:blood / Q000175:diagnosis / Q000453:epidemiology* / Q000503:physiopathology; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D015994:Incidence; D053208:Kaplan-Meier Estimate; D008019:Life Style; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D009426:Netherlands / Q000453:epidemiology; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011446:Prospective Studies; D012042:Registries; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D055815:Young Adult
chemical_list: D015415:Biomarkers; D008076:Cholesterol, HDL; D002784:Cholesterol