Use of Calcium Channel Blockers is Associated with Mortality in Patients with Chronic Kidney Disease.

BACKGROUND/AIMS The use of antihypertensive medicines has been shown to reduce proteinuria, morbidity, and mortality in patients with chronic kidney disease (CKD). A specific recommendation for a class of antihypertensive drugs is not available in this population, despite the pharmacodynamic differences. We have therefore analysed the association between antihypertensive medicines and survival of patients with chronic kidney disease. METHODS Out of 2687 consecutive patients undergoing kidney biopsy a cohort of 606 subjects with retrievable medical therapy was included into the analysis. Kidney function was assessed by glomerular filtration rate (GFR) estimation at the time point of kidney biopsy. Main outcome variable was death. RESULTS Overall 114 (18.7%) patients died. In univariate regression analysis the use of alpha-blockers and calcium channel antagonists, progression of disease, diabetes mellitus (DM) type 1 and 2, arterial hypertension, coronary heart disease, peripheral vascular disease, male sex and age were associated with mortality (all p<0.05). In a multivariate Cox regression model the use of calcium channel blockers (HR 1.89), age (HR 1.04), DM type 1 (HR 8.43) and DM type 2 (HR 2.17) and chronic obstructive pulmonary disease (HR 1.66) were associated with mortality (all p < 0.05). CONCLUSION The use of calcium channel blockers but not of other antihypertensive medicines is associated with mortality in primarily GN patients with CKD.

A Logarithmic Image Prior for Blind Deconvolution

Blind Deconvolution consists in the estimation of a sharp image and a blur kernel from an observed blurry image. Because the blur model admits several solutions it is necessary to devise an image prior that favors the true blur kernel and sharp image. Many successful image priors enforce the sparsity of the sharp image gradients. Ideally the L0 “norm” is the best choice for promoting sparsity, but because it is computationally intractable, some methods have used a logarithmic approximation. In this work we also study a logarithmic image prior. We show empirically how well the prior suits the blind deconvolution problem. Our analysis confirms experimentally the hypothesis that a prior should not necessarily model natural image statistics to correctly estimate the blur kernel. Furthermore, we show that a simple Maximum a Posteriori formulation is enough to achieve state of the art results. To minimize such formulation we devise two iterative minimization algorithms that cope with the non-convexity of the logarithmic prior: one obtained via the primal-dual approach and one via majorization-minimization.