A multiple biomarker risk score for guiding clinical decisions using a decision curve approach
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A multiple biomarker risk score for guiding clinical decisions using a decision curve approach. / Hughes, Maria F; Saarela, Olli; Blankenberg, Stefan; Zeller, Tanja; Havulinna, Aki S; Kuulasmaa, Kari; Yarnell, John; Schnabel, Renate B; Tiret, Laurence; Salomaa, Veikko; Evans, Alun; Kee, Frank; MORGAM Project.
in: EUR J PREV CARDIOL, Jahrgang 19, Nr. 4, 08.2012, S. 874-884.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
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TY - JOUR
T1 - A multiple biomarker risk score for guiding clinical decisions using a decision curve approach
AU - Hughes, Maria F
AU - Saarela, Olli
AU - Blankenberg, Stefan
AU - Zeller, Tanja
AU - Havulinna, Aki S
AU - Kuulasmaa, Kari
AU - Yarnell, John
AU - Schnabel, Renate B
AU - Tiret, Laurence
AU - Salomaa, Veikko
AU - Evans, Alun
AU - Kee, Frank
AU - MORGAM Project
PY - 2012/8
Y1 - 2012/8
N2 - AIMS: 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.METHODS AND 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.CONCLUSION: 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.
AB - AIMS: 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.METHODS AND 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.CONCLUSION: 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.
KW - Adult
KW - Age Factors
KW - Biomarkers/blood
KW - Blood Pressure
KW - C-Reactive Protein/analysis
KW - Cardiovascular Diseases/blood
KW - Cholesterol/blood
KW - Decision Support Techniques
KW - Europe
KW - Female
KW - Humans
KW - Male
KW - Middle Aged
KW - Natriuretic Peptide, Brain/blood
KW - Peptide Fragments/blood
KW - Predictive Value of Tests
KW - Prognosis
KW - Prospective Studies
KW - Risk Assessment
KW - Risk Factors
KW - Sex Factors
KW - Troponin I/blood
U2 - 10.1177/1741826711417341
DO - 10.1177/1741826711417341
M3 - SCORING: Journal article
C2 - 21775414
VL - 19
SP - 874
EP - 884
JO - EUR J PREV CARDIOL
JF - EUR J PREV CARDIOL
SN - 2047-4873
IS - 4
ER -
