Abstract
Objectives: The aim of this study was to investigate whether multiple biomarkers contribute to improved coronary heart disease (CHD) risk prediction in post-menopausal women compared with assessment using traditional risk factors (TRFs) only. Background: The utility of newer biomarkers remains uncertain when added to predictive models using only TRFs for CHD risk assessment. Methods: The Women's Health Initiative Hormone Trials enrolled 27,347 post-menopausal women ages 50 to 79 years. Associations of TRFs and 18 biomarkers were assessed in a nested case-control study including 321 patients with CHD and 743 controls. Four prediction equations for 5-year CHD risk were compared: 2 Framingham risk score covariate models; a TRF model including statin treatment, hormone treatment, and cardiovascular disease history as well as the Framingham risk score covariates; and an additional biomarker model that additionally included the 5 significantly associated markers of the 18 tested (interleukin-6, d-dimer, coagulation factor VIII, von Willebrand factor, and homocysteine). Results: The TRF model showed an improved C-statistic (0.729 vs. 0.699, p = 0.001) and net reclassification improvement (6.42%) compared with the Framingham risk score model. The additional biomarker model showed additional improvement in the C-statistic (0.751 vs. 0.729, p = 0.001) and net reclassification improvement (6.45%) compared with the TRF model. Predicted CHD risks on a continuous scale showed high agreement between the TRF and additional biomarker models (Spearman's coefficient = 0.918). Among the 18 biomarkers measured, C-reactive protein level did not significantly improve CHD prediction either alone or in combination with other biomarkers. Conclusions: Moderate improvement in CHD risk prediction was found when an 18-biomarker panel was added to predictive models using TRFs in post-menopausal women.
| Original language | English |
|---|---|
| Pages (from-to) | 2080-2091 |
| Number of pages | 12 |
| Journal | Journal of the American College of Cardiology |
| Volume | 55 |
| Issue number | 19 |
| DOIs | |
| Publication status | Published - 2010 May 11 |
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All Science Journal Classification (ASJC) codes
- Cardiology and Cardiovascular Medicine
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Multimarker Prediction of Coronary Heart Disease Risk. The Women's Health Initiative. / Kim, Hyeon Chang; Greenland, Philip; Rossouw, Jacques E.; Manson, Jo Ann E.; Cochrane, Barbara B.; Lasser, Norman L.; Limacher, Marian C.; Lloyd-Jones, Donald M.; Margolis, Karen L.; Robinson, Jennifer G.
In: Journal of the American College of Cardiology, Vol. 55, No. 19, 11.05.2010, p. 2080-2091.Research output: Contribution to journal › Article
TY - JOUR
T1 - Multimarker Prediction of Coronary Heart Disease Risk. The Women's Health Initiative
AU - Kim, Hyeon Chang
AU - Greenland, Philip
AU - Rossouw, Jacques E.
AU - Manson, Jo Ann E.
AU - Cochrane, Barbara B.
AU - Lasser, Norman L.
AU - Limacher, Marian C.
AU - Lloyd-Jones, Donald M.
AU - Margolis, Karen L.
AU - Robinson, Jennifer G.
PY - 2010/5/11
Y1 - 2010/5/11
N2 - Objectives: The aim of this study was to investigate whether multiple biomarkers contribute to improved coronary heart disease (CHD) risk prediction in post-menopausal women compared with assessment using traditional risk factors (TRFs) only. Background: The utility of newer biomarkers remains uncertain when added to predictive models using only TRFs for CHD risk assessment. Methods: The Women's Health Initiative Hormone Trials enrolled 27,347 post-menopausal women ages 50 to 79 years. Associations of TRFs and 18 biomarkers were assessed in a nested case-control study including 321 patients with CHD and 743 controls. Four prediction equations for 5-year CHD risk were compared: 2 Framingham risk score covariate models; a TRF model including statin treatment, hormone treatment, and cardiovascular disease history as well as the Framingham risk score covariates; and an additional biomarker model that additionally included the 5 significantly associated markers of the 18 tested (interleukin-6, d-dimer, coagulation factor VIII, von Willebrand factor, and homocysteine). Results: The TRF model showed an improved C-statistic (0.729 vs. 0.699, p = 0.001) and net reclassification improvement (6.42%) compared with the Framingham risk score model. The additional biomarker model showed additional improvement in the C-statistic (0.751 vs. 0.729, p = 0.001) and net reclassification improvement (6.45%) compared with the TRF model. Predicted CHD risks on a continuous scale showed high agreement between the TRF and additional biomarker models (Spearman's coefficient = 0.918). Among the 18 biomarkers measured, C-reactive protein level did not significantly improve CHD prediction either alone or in combination with other biomarkers. Conclusions: Moderate improvement in CHD risk prediction was found when an 18-biomarker panel was added to predictive models using TRFs in post-menopausal women.
AB - Objectives: The aim of this study was to investigate whether multiple biomarkers contribute to improved coronary heart disease (CHD) risk prediction in post-menopausal women compared with assessment using traditional risk factors (TRFs) only. Background: The utility of newer biomarkers remains uncertain when added to predictive models using only TRFs for CHD risk assessment. Methods: The Women's Health Initiative Hormone Trials enrolled 27,347 post-menopausal women ages 50 to 79 years. Associations of TRFs and 18 biomarkers were assessed in a nested case-control study including 321 patients with CHD and 743 controls. Four prediction equations for 5-year CHD risk were compared: 2 Framingham risk score covariate models; a TRF model including statin treatment, hormone treatment, and cardiovascular disease history as well as the Framingham risk score covariates; and an additional biomarker model that additionally included the 5 significantly associated markers of the 18 tested (interleukin-6, d-dimer, coagulation factor VIII, von Willebrand factor, and homocysteine). Results: The TRF model showed an improved C-statistic (0.729 vs. 0.699, p = 0.001) and net reclassification improvement (6.42%) compared with the Framingham risk score model. The additional biomarker model showed additional improvement in the C-statistic (0.751 vs. 0.729, p = 0.001) and net reclassification improvement (6.45%) compared with the TRF model. Predicted CHD risks on a continuous scale showed high agreement between the TRF and additional biomarker models (Spearman's coefficient = 0.918). Among the 18 biomarkers measured, C-reactive protein level did not significantly improve CHD prediction either alone or in combination with other biomarkers. Conclusions: Moderate improvement in CHD risk prediction was found when an 18-biomarker panel was added to predictive models using TRFs in post-menopausal women.
UR - http://www.scopus.com/inward/record.url?scp=77951626147&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77951626147&partnerID=8YFLogxK
U2 - 10.1016/j.jacc.2009.12.047
DO - 10.1016/j.jacc.2009.12.047
M3 - Article
C2 - 20447530
AN - SCOPUS:77951626147
VL - 55
SP - 2080
EP - 2091
JO - Journal of the American College of Cardiology
JF - Journal of the American College of Cardiology
SN - 0735-1097
IS - 19
ER -