Indications for and Utilization of ACE Inhibitors in Older Individuals with Diabetes

Angiotensin-converting enzyme inhibitors (ACE) and angiotensin receptor blockers (ARB) improve cardiovascular outcomes in high-risk individuals with diabetes. Despite the marked benefit, it is unknown what percentage of patients with diabetes would benefit from and what percentage actually receive this preventive therapy. OBJECTIVES : To examine the proportion of older diabetic patients with indications for ACE or ARB (ACE/ARB). To generate national estimates of ACE/ARB use. DESIGN AND PARTICIPANTS : Survey of 742 individuals≥55 years (representing 8.02 million U.S. adults) self-reporting diabetes in the 1999 to 2002 National Health and Nutrition Examination Survey. MEASUREMENTS : Prevalence of guideline indications (albuminuria, cardiovascular disease, hypertension) and other cardiac risk factors (hyperlipidemia, smoking) with potential benefit from ACE/ARB. Prevalence of ACE/ARB use overall and by clinical indication. RESULTS : Ninety-two percent had guideline indications for ACE/ARB. Including additional cardiac risk factors, the entire (100%) U.S. noninstitutionalized older population with diabetes had indications for ACE/ARB. Overall, 43% of the population received ACE/ARB. Hypertension was associated with higher rates of ACE/ARB use, while albuminuria and cardiovascular disease were not. As the number of indications increased, rates of use increased, however, the maximum prevalence of use was only 53% in individuals with 4 or more indications for ACE/ARB. CONCLUSIONS : ACE/ARB is indicated in virtually all older individuals with diabetes; yet, national rates of use are disturbingly low and key risk factors (albuminuria and cardiovascular disease) are being missed. To improve quality of diabetes care nationally, use of ACE/ARB therapy by ALL older diabetics may be a desirable addition to diabetes performance measurement sets.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/74734/1/j.1525-1497.2006.00351.x.pd

Testing for heterogeneity among the components of a binary composite outcome in a clinical trial

<p>Abstract</p> <p>Background</p> <p>Investigators designing clinical trials often use composite outcomes to overcome many statistical issues. Trialists want to maximize power to show a statistically significant treatment effect and avoid inflation of Type I error rate due to evaluation of multiple individual clinical outcomes. However, if the treatment effect is not similar among the components of this composite outcome, we are left not knowing how to interpret the treatment effect on the composite itself. Given significant heterogeneity among these components, a composite outcome may be judged as being invalid or un-interpretable for estimation of the treatment effect. This paper compares the power of different tests to detect heterogeneity of treatment effect across components of a composite binary outcome.</p> <p>Methods</p> <p>Simulations were done comparing four different models commonly used to analyze correlated binary data. These models included: logistic regression for ignoring correlation, logistic regression weighted by the intra cluster correlation coefficient, population average logistic regression using generalized estimating equations (GEE), and random effects logistic regression.</p> <p>Results</p> <p>We found that the population average model based on generalized estimating equations (GEE) had the greatest power across most scenarios. Adequate power to detect possible composite heterogeneity or variation between treatment effects of individual components of a composite outcome was seen when the power for detecting the main study treatment effect for the composite outcome was also reasonably high.</p> <p>Conclusions</p> <p>It is recommended that authors report tests of composite heterogeneity for composite outcomes and that this accompany the publication of the statistically significant results of the main effect on the composite along with individual components of composite outcomes.</p