title: A multiple biomarker risk score for guiding clinical decisions using a decision curve approach.
abstract: OBJECTIVE
  We assessed whether a cardiovascular risk model based on classic risk factors (e.g. cholesterol, blood pressure) could refine disease prediction if it included novel biomarkers (C-reactive protein, N-terminal pro-B-type natriuretic peptide, troponin I) using a decision curve approach which can incorporate clinical consequences.
  
  
  RESULTS
  We evaluated whether a model including biomarkers and classic risk factors could improve prediction of 10 year risk of cardiovascular disease (CVD; chronic heart disease and ischaemic stroke) against a classic risk factor model using a decision curve approach in two prospective MORGAM cohorts. This included 7739 men and women with 457 CVD cases from the FINRISK97 cohort; and 2524 men with 259 CVD cases from PRIME Belfast. The biomarker model improved disease prediction in FINRISK across the high-risk group (20-40%) but not in the intermediate risk group, at the 23% risk threshold net benefit was 0.0033 (95% CI 0.0013-0.0052). However, in PRIME Belfast the net benefit of decisions guided by the decision curve was improved across intermediate risk thresholds (10-20%). At p(t) = 10% in PRIME, the net benefit was 0.0059 (95% CI 0.0007-0.0112) with a net increase in 6 true positive cases per 1000 people screened and net decrease of 53 false positive cases per 1000 potentially leading to 5% fewer treatments in patients not destined for an event.
  
  
  CONCLUSIONS
  The biomarker model improves 10-year CVD prediction at intermediate and high-risk thresholds and in particular, could be clinically useful at advising middle-aged European males of their CVD risk.
authors.affiliation: UKCRC Centre of Excellence for Public Health, School of Medicine Dentistry and Biomedical Sciences, Institute of Clinical Sciences B, Queens University Belfast, Grosvenor Road, Belfast, BT12 6BJ, UK. maria.hughes@qub.ac.uk
authors.forename: Maria F
authors.initials: MF
authors.lastname: Hughes
authors.forename: Olli
authors.initials: O
authors.lastname: Saarela
authors.forename: Stefan
authors.initials: S
authors.lastname: Blankenberg
authors.forename: Tanja
authors.initials: T
authors.lastname: Zeller
authors.forename: Aki S
authors.initials: AS
authors.lastname: Havulinna
authors.forename: Kari
authors.initials: K
authors.lastname: Kuulasmaa
authors.forename: John
authors.initials: J
authors.lastname: Yarnell
authors.forename: Renate B
authors.initials: RB
authors.lastname: Schnabel
authors.forename: Laurence
authors.initials: L
authors.lastname: Tiret
authors.forename: Veikko
authors.initials: V
authors.lastname: Salomaa
authors.forename: Alun
authors.initials: A
authors.lastname: Evans
authors.forename: Frank
authors.initials: F
authors.lastname: Kee
mesh_terms: D000328:Adult; D000367:Age Factors; D015415:Biomarkers / Q000097:blood*; D001794:Blood Pressure; D002097:C-Reactive Protein / Q000032:analysis; D002318:Cardiovascular Diseases / Q000097:blood / Q000453:epidemiology* / Q000503:physiopathology; D002784:Cholesterol / Q000097:blood; D003661:Decision Support Techniques; D005060:Europe; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D020097:Natriuretic Peptide, Brain / Q000097:blood; D010446:Peptide Fragments / Q000097:blood; D011237:Predictive Value of Tests; D011379:Prognosis; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D012737:Sex Factors; D019210:Troponin I / Q000097:blood
chemical_list: D015415:Biomarkers; D010446:Peptide Fragments; D019210:Troponin I; C109794:pro-brain natriuretic peptide (1-76); D020097:Natriuretic Peptide, Brain; D002097:C-Reactive Protein; D002784:Cholesterol