Bilik D.,University of Michigan |
McEwen L.N.,University of Michigan |
Brown M.B.,University of Michigan |
Selby J.V.,Kaiser Permanente |
And 8 more authors.
Pharmacoepidemiology and Drug Safety | Year: 2010
Background: Studies have associated thiazolidinedione (TZD) treatment with cardiovascular disease (CVD) and questioned whether the two available TZDs, rosiglitazone and pioglitazone, have different CVD risks. We compared CVD incidence, cardiovascular (CV), and all-cause mortality in type 2 diabetic patients treated with rosiglitazone or pioglitazone as their only TZD. Methods: We analyzed survey, medical record, administrative, and National Death Index (NDI) data from 1999 through 2003 from Translating Research Into Action for Diabetes (TRIAD), a prospective observational study of diabetes care in managed care. Medications, CV procedures, and CVD were determined from health plan (HP) administrative data, and mortality was from NDI. Adjusted hazard rates (AHR) were derived from Cox proportional hazard models adjusted for age, sex, race/ethnicity, income, history of diabetic nephropathy, history of CVD, insulin use, and HP. Results: Across TRIAD's 10 HPs, 1,815 patients (24%) filled prescriptions for a TZD, 773 (10%) for only rosiglitazone, 711 (10%) for only pioglitazone, and 331 (4%) for multiple TZDs. In the seven HPs using both TZDs, 1,159 patients (33%) filled a prescription for a TZD, 564 (16%) for only rosiglitazone, 334 (10%) for only pioglitazone, and 261 (7%) for multiple TZDs. For all CV events, CV, and all-cause mortality, we found no significant difference between rosiglitazone and pioglitazone. Conclusions: In this relatively small, prospective, observational study, we found no statistically significant differences in CV outcomes for rosiglitazone- compared to pioglitazone-treated patients. There does not appear to be a pattern of clinically meaningful differences in CV outcomes for rosiglitazone- versus pioglitazone-treated patients. Copyright © 2010 John Wiley & Sons, Ltd.
Kim H.C.,Northwestern University |
Kim H.C.,Yonsei University |
Greenland P.,Northwestern University |
Rossouw J.E.,U.S. National Institutes of Health |
And 7 more authors.
Journal of the American College of Cardiology | Year: 2010
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. © 2010 American College of Cardiology Foundation.