title: How much does HDL cholesterol add to risk estimation? A report from the SCORE Investigators.
abstract: BACKGROUND
  Systematic COronary Risk Evaluation (SCORE), the risk estimation system recommended by the European guidelines on cardiovascular disease prevention, estimates 10-year risk of cardiovascular disease mortality based on age, sex, country of origin, systolic blood pressure, smoking status and either total cholesterol (TC) or TC/high-density lipoprotein cholesterol (HDL-C) ratio. As, counterintuitively, these two systems perform very similarly, we have investigated whether incorporating HDL-C and TC as separate variables improves risk estimation.
  
  
  METHODS
  The study consisted of 57,302 men and 47,659 women. Cox proportional hazards method was used to derive the function including HDL-C and an identical function without HDL-C for comparison. Risk charts were developed to illustrate the results.
  
  
  RESULTS
  Inclusion of HDL-C resulted in a modest but statistically significant improvement in risk estimation, based on the area under receiver operating characteristic curve (AUROC); 0.814 versus 0.808, P value less than 0.0001, for the functions with and without HDL-C, respectively. Addition of HDL-C also resulted in a significant and important improvement in risk estimation as measured by net reclassification index, which is highly clinically relevant. Improvement in risk estimation was greatest in women from high-risk countries, in terms of both AUROC and net reclassification index.
  
  
  CONCLUSIONS
  For the general population, the inclusion of HDL-C in risk estimation results in only a modest improvement in overall risk estimation based on AUROC. However, when using the more clinically that examines reclassification of individuals, clinically useful improvements occur. Inclusion of HDL may be particularly useful in women from high-risk countries and individuals with unusually high or low HDL-C levels. Addition of HDL-C is particularly applicable to electronic, interactive risk estimation systems such as HeartScore.
authors.affiliation: Department of Cardiology Adelaide Meath Hospital, Tallaght, Dublin 24, Ireland.
authors.forename: Marie Therese
authors.initials: MT
authors.lastname: Cooney
authors.forename: Alexandra
authors.initials: A
authors.lastname: Dudina
authors.forename: Dirk
authors.initials: D
authors.lastname: De Bacquer
authors.forename: Anthony
authors.initials: A
authors.lastname: Fitzgerald
authors.forename: Ronan
authors.initials: R
authors.lastname: Conroy
authors.forename: Susana
authors.initials: S
authors.lastname: Sans
authors.forename: Alessandro
authors.initials: A
authors.lastname: Menotti
authors.forename: Guy
authors.initials: G
authors.lastname: De Backer
authors.forename: Pekka
authors.initials: P
authors.lastname: Jousilahti
authors.forename: Ulrich
authors.initials: U
authors.lastname: Keil
authors.forename: Troels
authors.initials: T
authors.lastname: Thomsen
authors.forename: Peter
authors.initials: P
authors.lastname: Whincup
authors.forename: Ian
authors.initials: I
authors.lastname: Graham
mesh_terms: D015415:Biomarkers / Q000097:blood; D002318:Cardiovascular Diseases / Q000097:blood / Q000209:etiology* / Q000401:mortality; D002784:Cholesterol / Q000097:blood*; D008076:Cholesterol, HDL / Q000097:blood*; D050171:Dyslipidemias / Q000097:blood / Q000150:complications* / Q000401:mortality; D005060:Europe / Q000453:epidemiology; D005260:Female; D006801:Humans; D008297:Male; D016016:Proportional Hazards Models; D012372:ROC Curve; D018570:Risk Assessment; D012307:Risk Factors
chemical_list: D015415:Biomarkers; D008076:Cholesterol, HDL; D002784:Cholesterol