Correct use of non-indexed eGFR for drug dosing and renal drug-related problems at hospital admission

PURPOSE Two to seven percent of the German adult population has a renal impairment (RI) with an estimated glomerular filtration rate (eGFR) < 60 ml/min/1.73m2. This often remains unrecognized and adjustment of drug therapy is lacking. To determine renal function in clinical routine, the CKD-EPI equation is used to calculate an indexed eGFR (ml/min/1.73m2). For drug dosing, it has to be individualized to a non-indexed eGFR (ml/min) by the patient's body surface area. Here, we investigated the number of patients admitted to urological wards of a teaching hospital with RI between July and December 2016. Additionally, we correctly used the eGFRnon-indexed for drug and dosage adjustments and to analyse the use of renal risk drugs (RRD) and renal drug-related problems (rDRP). METHODS In a retrospective observational study, urological patients with pharmacist-led medication reconciliation at hospital admission and eGFRindexed (CKD-EPI) of 15-59 ml/min/1.73m2 were identified. Indexed eGFR (ml/min/1.73m2) was recalculated with body surface area to non-indexed eGFR (ml/min) for correct drug dosing. Medication at admission was reviewed for RRD and based on the eGFRnon-indexed for rDRP, e.g. inappropriate dose or contraindication. RESULTS Of 1320 screened patients, 270 (20.5%) presented with an eGFRindexed of 15–59 ml/min/1.73m2. After readjustment, 203 (15.4%) patients had an eGFRnon-indexed of 15–59 ml/min. Of these, 190 (93.6%) used ≥ 1 drugs at admission with 660 of 1209 (54.7%) drugs classified as RRD. At least one rDRP was identified in 115 (60.5%) patients concerning 264 (21.8%) drugs. CONCLUSION Renal impairment is a common risk factor for medication safety in urologic patients admitted to a hospital. Considerable shifts were seen in eGFR-categories when correctly calculating eGFRnon-indexed for drug dosing purposes. The fact that more than half of the study patients showed rDRP at hospital admission underlines the need to consider this risk factor appropriately

Factors affecting glomerular filtration rate, as measured by iohexol disappearance, in men with or at risk for HIV infection

Objective: Formulae used to estimate glomerular filtration rate (GFR) underestimate higher GFRs and have not been well-studied in HIV-infected (HIV(+)) people; we evaluated the relationships of HIV infection and known or potential risk factors for kidney disease with directly measured GFR and the presence of chronic kidney disease (CKD). Design: Cross-sectional measurement of iohexol-based GFR (iGFR) in HIV(+) men (n = 455) receiving antiretroviral therapy, and HIV-uninfected (HIV(-)) men (n = 258) in the Multicenter AIDS Cohort Study. Methods: iGFR was calculated from disappearance of infused iohexol from plasma. Determinants of GFR and the presence of CKD were compared using iGFR and GFR estimated by the CKD-Epi equation (eGFR). Results: Median iGFR was higher among HIV(+) than HIV(-) men (109 vs. 106 ml/min/1.73 m2, respectively, p = .046), and was 7 ml/min higher than median eGFR. Mean iGFR was lower in men who were older, had chronic hepatitis C virus (HCV) infection, or had a history of AIDS. Low iGFR (≤90 ml/min/1.73 m2) was associated with these factors and with black race. Other than age, factors associated with low iGFR were not observed with low eGFR. CKD was more common in HIV(+) than HIV(-) men; predictors of CKD were similar using iGFR and eGFR. Conclusions: iGFR was higher than eGFR in this population of HIV-infected and -uninfected men who have sex with men. Presence of CKD was predicted equally well by iGFR and eGFR, but associations of chronic HCV infection and history of clinically-defined AIDS with mildly decreased GFR were seen only with iGFR. © 2014 Margolick et al

Acute Treatment Effects on GFR in Randomized Clinical Trials of Kidney Disease Progression

Background Acute changes in GFR can occur after initiation of interventions targeting progression of CKD. These acute changes complicate the interpretation of long-term treatment effects. Methods To assess the magnitude and consistency of acute effects in randomized clinical trials and explore factors that might affect them, we performed a meta-analysis of 53 randomized clinical trials for CKD progression, enrolling 56,413 participants with at least one estimated GFR measurement by 6 months after randomization. We defined acute treatment effects as the mean difference in GFR slope from baseline to 3 months between randomized groups. We performed univariable and multivariable metaregression to assess the effect of intervention type, disease state, baseline GFR, and albuminuria on the magnitude of acute effects. Results The mean acute effect across all studies was 20.21 ml/min per 1.73 m2 (95% confidence interval, 20.63 to 0.22) over 3 months, with substantial heterogeneity across interventions (95% coverage interval across studies, 22.50 to 12.08 ml/min per 1.73 m2). We observed negative average acute effects in renin angiotensin system blockade, BP lowering, and sodium-glucose cotransporter 2 inhibitor trials, and positive acute effects in trials of immunosuppressive agents. Larger negative acute effects were observed in trials with a higher mean baseline GFR. Conclusion The magnitude and consistency of acute GFR effects vary across different interventions, and are larger at higher baseline GFR. Understanding the nature and magnitude of acute effects can help inform the optimal design of randomized clinical trials evaluating disease progression in CKD

Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes

Aims/hypothesis: As part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts. Methods: We used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case–control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30–75 ml min−1 [1.73 m]−2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function. Results: Twelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets. Conclusions/interpretation: Serum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30–75 ml min−1 [1.73 m]−2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers