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

The Cost-Effectiveness of Improving Diabetes Care in U.S. Federally Qualified Community Health Centers

Objective. To estimate the incremental cost-effectiveness of improving diabetes care with the Health Disparities Collaborative (HDC), a national collaborative quality improvement (QI) program conducted in community health centers (HCs). Data Sources/Study Settings. Data regarding the impact of the Diabetes HDC program came from a serial cross-sectional follow-up study (1998, 2000, 2002) of the program in 17 Midwestern HCs. Data inputs for the simulation model of diabetes came from the latest clinical trials and epidemiological studies. Study Design. We conducted a societal cost-effectiveness analysis, incorporating data from QI program evaluation into a Monte Carlo simulation model of diabetes. Data Collections/Extraction Methods. Data on diabetes care processes and risk factor levels were extracted from medical charts of randomly selected patients. Principal Findings. From 1998 to 2002, multiple processes of care (e.g., glycosylated hemoglobin testing [HbA1C] [71 -\u3e 92 percent] and ACE inhibitor prescribing [33 -\u3e 55 percent]) and risk factor levels (e.g., 1998 mean HbA1C 8.53 percent, mean difference 0.45 percent [95 percent confidence intervals -0.72, -0.17]) improved significantly. With these improvements, the HDC was estimated to reduce the lifetime incidence of blindness (17 -\u3e 15 percent), end-stage renal disease (18 -\u3e 15 percent), and coronary artery disease (28 -\u3e 24 percent). The average improvement in quality-adjusted life year (QALY) was 0.35 and the incremental cost-effectiveness ratio was $33,386/QALY. Conclusions. During the first 4 years of the HDC, multiple improvements in diabetes care were observed. If these improvements are maintained or enhanced over the lifetime of patients, the HDC program will be cost-effective for society based on traditionally accepted thresholds

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

Reno-protective effects of renin–angiotensin system blockade in type 2 diabetic patients: a systematic review and network meta-analysis

AIMS/HYPOTHESIS: This meta-analysis aimed to compare the renal outcomes between ACE inhibitor (ACEI)/angiotensin II receptor blocker (ARB) and other antihypertensive drugs or placebo in type 2 diabetes. METHODS: Publications were identified from Medline and Embase up to July 2011. Only randomised controlled trials comparing ACEI/ARB monotherapy with other active drugs or placebo were eligible. The outcome of end-stage renal disease, doubling of serum creatinine, microvascular complications, microalbuminuria, macroalbuminuria and albuminuria regression were extracted. Risk ratios were pooled using a random-effects model if heterogeneity was present; a fixed-effects model was used in the absence of heterogeneity. RESULTS: Of 673 studies identified, 28 were eligible (n = 13-4,912). In direct meta-analysis, ACEI/ARB had significantly lower risk of serum creatinine doubling (pooled RR = 0.66 [95% CI 0.52, 0.83]), macroalbuminuria (pooled RR = 0.70 [95% CI 0.50, 1.00]) and albuminuria regression (pooled RR 1.16 [95% CI 1.00, 1.39]) than other antihypertensive drugs, mainly calcium channel blockers (CCBs). Although the risks of end-stage renal disease and microalbuminuria were lower in the ACEI/ARB group (pooled RR 0.82 [95% CI 0.64, 1.05] and 0.84 [95% CI 0.61, 1.15], respectively), the differences were not statistically significant. The ACEI/ARB benefit over placebo was significant for all outcomes except microalbuminuria. A network meta-analysis detected significant treatment effects across all outcomes for both active drugs and placebo comparisons. CONCLUSIONS/INTERPRETATION: Our review suggests a consistent reno-protective effect of ACEI/ARB over other antihypertensive drugs, mainly CCBs, and placebo in type 2 diabetes. The lack of any differences in BP decrease between ACEI/ARB and active comparators suggest this benefit is not due simply to the antihypertensive effect