Article

Renal function and effectiveness of angiotensin-converting enzyme inhibitor therapy in patients with chronic stable coronary disease in the Prevention of Events with ACE inhibition (PEACE) trial.

George Washington University, Washington, Washington, D.C., United States
Circulation (Impact Factor: 14.95). 08/2006; 114(1):26-31. DOI: 10.1161/CIRCULATIONAHA.105.592733
Source: PubMed

ABSTRACT Patients with reduced renal function are at increased risk for adverse cardiovascular outcomes. In the post-myocardial infarction setting, angiotensin-converting enzyme (ACE) inhibitors have been shown to be as effective in patients with impaired renal function as in those with preserved renal function.
We assessed the relation between renal function and outcomes, the influence of ACE inhibition on this relation, and whether renal function modifies the effectiveness of ACE inhibition in patients with stable coronary artery disease and preserved systolic function enrolled in the Prevention of Events with ACE inhibition trial (PEACE). Patients (n=8290) were randomly assigned to receive trandolapril (target, 4 mg/d) or placebo. Clinical creatinine measures were available for 8280 patients before randomization. The estimated glomerular filtration rate (eGFR) was calculated with the 4-point Modification of Diet in Renal Disease equation. Renal function was related to outcomes, and the influence of ACE-inhibitor therapy was assessed with formal interaction modeling. The mean eGFR in PEACE was 77.6+/-19.4, and 1355 (16.3%) patients had reduced renal function (eGFR <60 mg.mL(-1).1.73 m(-2)). We observed a significant interaction between eGFR and treatment group with respect to cardiovascular and all-cause mortality (P=0.02). Trandolapril was associated with a reduction in total mortality in patients with reduced renal function (adjusted HR, 0.73; 95% CI, 0.54 to 1.00) but not in patients with preserved renal function (adjusted HR, 0.94; 95% CI, 0.78 to 1.13).
Although trandolapril did not improve survival in the overall PEACE cohort, in which mean eGFR was relatively high, trandolapril reduced mortality in patients with reduced eGFR. These data suggest that reduced renal function may define a subset of patients most likely to benefit from ACE-inhibitor therapy for cardiovascular protection.

0 Bookmarks
 · 
57 Views
  • The Canadian journal of cardiology 11/2013; 29(11):1371-3. DOI:10.1016/j.cjca.2013.09.010 · 3.12 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Objective To assess the association between ACEI/ARB use and mortality in CKD patients. Background There is insufficient evidence about the association of angiotensin converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARBs) with mortality in chronic kidney disease (CKD) patients. Methods A logistic regression analysis was used to calculate the propensity of ACEI/ARB initiation in 141,413 US veterans with non-dialysis CKD previously unexposed to ACEI/ARB treatment. We examined the association of ACEI/ARB administration with all-cause mortality in patients matched by propensity scores, using the Kaplan-Meier method and Cox models in “intention-to-treat” analyses, and in generalized linear models with binary outcomes and inverse probability treatment weighing (IPTW) in “as-treated” analyses. Results The mean±SD age of the patients at baseline was 75±10 years, 8% of patients were black, and 22% were diabetic. ACEI/ARB administration was associated with significantly lower risk of mortality both in the intention-to-treat analysis (HR=0.81; 95%CI: 0.78-0.84, p<0.001) and in the as-treated analysis with IPTW (OR=0.37; 95%CI: 0.34-0.41, p<0.001). The association of ACEI/ARB treatment with lower risk of mortality was present in all examined subgroups. Conclusions In this large contemporary cohort of non-dialysis dependent CKD patients, ACEI/ARB administration was associated with greater survival.
    Journal of the American College of Cardiology 01/2013; 63(7). DOI:10.1016/j.jacc.2013.10.050 · 15.34 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: When comparing a new treatment with a control in a randomized clinical study, the treatment effect is generally assessed by evaluating a summary measure over a specific study population. The success of the trial heavily depends on the choice of such a population. In this paper, we show a systematic, effective way to identify a promising population, for which the new treatment is expected to have a desired benefit, utilizing the data from a current study involving similar comparator treatments. Specifically, using the existing data, we first create a parametric scoring system as a function of multiple multiple baseline covariates to estimate subject-specific treatment differences. Based on this scoring system, we specify a desired level of treatment difference and obtain a subgroup of patients, defined as those whose estimated scores exceed this threshold. An empirically calibrated threshold-specific treatment difference curve across a range of score values is constructed. The subpopulation of patients satisfying any given level of treatment benefit can then be identified accordingly. To avoid bias due to overoptimism, we utilize a cross-training-evaluation method for implementing the above two-step procedure. We then show how to select the best scoring system among all competing models. Furthermore, for cases in which only a single pre-specified working model is involved, inference procedures are proposed for the average treatment difference over a range of score values using the entire data set, and are justified theoretically and numerically. Lastly, the proposals are illustrated with the data from two clinical trials in treating HIV and cardiovascular diseases. Note that if we are not interested in designing a new study for comparing similar treatments, the new procedure can also be quite useful for the management of future patients, so that treatment may be targeted towards those who would receive nontrivial benefits to compensate for the risk or cost of the new treatment.
    Journal of the American Statistical Association 06/2013; 108(502):527-539. DOI:10.1080/01621459.2013.770705 · 2.11 Impact Factor

Preview

Download
0 Downloads
Available from