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      Evaluation of the adequacy of drug prescriptions in patients with chronic kidney disease: results from the CKD-REIN cohort : Adequacy of drug prescriptions in CKD patients

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          Abstract

          Drug prescription is difficult to manage in patients with chronic kidney disease (CKD). We assessed the prevalence and determinants of inappropriate drug prescriptions (whether contraindications or inappropriately high doses) with regard to kidney function in patients with CKD under nephrology care. We also assessed the impact of the equation used to estimate GFR on the prevalence estimates.

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          Most cited references31

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          A new equation to estimate glomerular filtration rate.

          Equations to estimate glomerular filtration rate (GFR) are routinely used to assess kidney function. Current equations have limited precision and systematically underestimate measured GFR at higher values. To develop a new estimating equation for GFR: the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation. Cross-sectional analysis with separate pooled data sets for equation development and validation and a representative sample of the U.S. population for prevalence estimates. Research studies and clinical populations ("studies") with measured GFR and NHANES (National Health and Nutrition Examination Survey), 1999 to 2006. 8254 participants in 10 studies (equation development data set) and 3896 participants in 16 studies (validation data set). Prevalence estimates were based on 16,032 participants in NHANES. GFR, measured as the clearance of exogenous filtration markers (iothalamate in the development data set; iothalamate and other markers in the validation data set), and linear regression to estimate the logarithm of measured GFR from standardized creatinine levels, sex, race, and age. In the validation data set, the CKD-EPI equation performed better than the Modification of Diet in Renal Disease Study equation, especially at higher GFR (P < 0.001 for all subsequent comparisons), with less bias (median difference between measured and estimated GFR, 2.5 vs. 5.5 mL/min per 1.73 m(2)), improved precision (interquartile range [IQR] of the differences, 16.6 vs. 18.3 mL/min per 1.73 m(2)), and greater accuracy (percentage of estimated GFR within 30% of measured GFR, 84.1% vs. 80.6%). In NHANES, the median estimated GFR was 94.5 mL/min per 1.73 m(2) (IQR, 79.7 to 108.1) vs. 85.0 (IQR, 72.9 to 98.5) mL/min per 1.73 m(2), and the prevalence of chronic kidney disease was 11.5% (95% CI, 10.6% to 12.4%) versus 13.1% (CI, 12.1% to 14.0%). The sample contained a limited number of elderly people and racial and ethnic minorities with measured GFR. The CKD-EPI creatinine equation is more accurate than the Modification of Diet in Renal Disease Study equation and could replace it for routine clinical use. National Institute of Diabetes and Digestive and Kidney Diseases.
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            Prediction of Creatinine Clearance from Serum Creatinine

            A formula has been developed to predict creatinine clearance (C cr ) from serum creatinine (S cr ) in adult males: Ccr = (140 – age) (wt kg)/72 × S cr (mg/100ml) (15% less in females). Derivation included the relationship found between age and 24-hour creatinine excretion/kg in 249 patients aged 18–92. Values for C cr were predicted by this formula and four other methods and the results compared with the means of two 24-hour C cr’s measured in 236 patients. The above formula gave a correlation coefficient between predicted and mean measured Ccr·s of 0.83; on average, the difference between predicted and mean measured values was no greater than that between paired clearances. Factors for age and body weight must be included for reasonable prediction.
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              KDIGO 2012 clinical practice guideline for the evaluation and management of chronic kidney disease

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                British Journal of Clinical Pharmacology
                Br J Clin Pharmacol
                Wiley
                03065251
                December 2018
                December 2018
                September 24 2018
                : 84
                : 12
                : 2811-2823
                Affiliations
                [1 ]CESP Centre for Research in Epidemiology and Population Health; Univ Paris-Saclay, Univ Paris Sud, UVSQ, UMRS 1018; F-94807 Villejuif France
                [2 ]Agence de la Biomédecine; Saint-Denis France
                [3 ]Service de Néphrologie Transplantation Dialyse Aphérèse; Centre Hospitalier Universitaire de Bordeaux; Bordeaux France
                [4 ]INSERM, U1026; Univ Bordeaux Segalen; Bordeaux France
                [5 ]Department of Nephrology; Centre Hospitalier Lyon Sud, Univ Lyon, UCBL, Carmen; F-69495 Pierre-Bénite France
                [6 ]Clinical Epidemiology; Inserm CIC-EC, CHU de Nancy; Vandoeuvre-lès-Nancy France
                [7 ]Nephrology Department; CHU de Nancy Vandoeuvre-lès-Nancy France
                [8 ]Arbor Research Collaborative for Health; Ann Arbor Michigan USA
                [9 ]Nephrology Department; CHU Ambroise Pare; Boulogne France
                [10 ]Pharmacology department; Amiens University Hospital; Amiens France
                [11 ]INSERM U1088; Jules Vernes University; Amiens France
                Article
                10.1111/bcp.13738
                6255993
                30110711
                240d72f7-2ccb-41c7-bd85-2accda27d7e5
                © 2018

                http://doi.wiley.com/10.1002/tdm_license_1.1

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