Aim
We evaluated the performance of the Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) and Cockcroft–Gault (CG) equations against creatinine ...clearance (CrCl) to estimate glomerular filtration rate (GFR) in 51 patients with Type 2 diabetes.
Methods
The CrCl value was obtained from the average of two consecutive 24‐h urine samples. Results were adjusted for body surface area using the Dubois formula. Serum creatinine was measured using the kinetic Jaffe method and was calibrated to standardized levels. Bland–Altman analysis and kappa statistic were used to examine agreement between measured and estimated GFR.
Results
Estimates of GFR from the CrCl, MDRD, CKD‐EPI and CG equations were similar (overall P = 0.298), and MDRD (r = 0.58; 95% CI: 0.36–0.74), CKD‐EPI (r = 0.55; 95% CI: 0.33–0.72) and CG (r = 0.61; 95% CI: 0.39–0.75) showed modest correlation with CrCl (all P < 0.001). Bias was −0.3 for MDRD, 1.7 for CKD‐EPI and −5.4 for CG. All three equations showed fair‐to‐moderate agreement with CrCl (kappa: 0.38–0.51). The c‐statistic for all three equations ranged between 0.75 and 0.77 with no significant difference (P = 0.639 for c‐statistic comparison).
Conclusions
The MDRD equation seems to have a modest advantage over CKD‐EPI and CG in estimating GFR and detecting impaired renal function in sub‐Saharan African patients with Type 2 diabetes. The overall relatively modest correlation with CrCl, however, suggests the need for context‐specific estimators of GFR or context adaptation of existing estimators.
What's new?
Glomerular filtration rate (GFR) equations for estimating kidney function are routinely used in sub‐Saharan Africa. There is, however, a paucity of data on the performance of these equations in patients with diabetes mellitus who are, for the most part, undiagnosed and unaware of their condition.
We investigated the performance of the Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD‐EPI) and Cockcroft–Gault (CG) equations in patients with Type 2 diabetes.
Our results suggest that the lowest bias is observed with the MDRD formula compared with the CKD‐EPI and CG equations. However, all three equations performed similarly in predicting altered kidney function based on low GFR.
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Abstract Haemodialysis improves uraemia-induced insulin sensitivity and is therefore likely to induce significant changes in circulating glucose concentrations in end-stage renal disease (ESRD). We ...aimed to assess clinically relevant circulating glucose changes in patients undergoing chronic maintenance haemodialysis using continuous interstitial monitoring. We investigated 14 non-diabetic ESRD subjects aged 40.6 ± 2.4 years. Participants were examined 24-h day pre-dialysis, during the index dialysis session and 24-h post-dialysis with simultaneous measurement of capillary blood glucose and continuous interstitial glucose (CGMS). Participants performed five capillary blood glucose measurements the day before dialysis, and 10 during and after dialysis. Mean capillary blood glucose was 128 ± 20 mg/dl the day before, 93 ± 8 mg/dl during haemodialysis, and 105 ± 13 mg/dl after haemodialysis. There was a significant trend towards lower blood glucose during the session from 105 ± 16 mg/dl to a 3rd hour nadir of 83 ± 15 mg/dl (Anova F = 2.89, p = 0.029). No hypoglycaemia was recorded. Interstitial glucose profile was comparable to capillary glucose profile. Glucose concentrations varied significantly from 126 ± 13 mg/dl before to 112 ± 12 mg/dl after haemodialysis respectively ( p = 0.006). This study provides evidence for the use of CGMS in ESRD and haemodialysis, and demonstrates significant changes in glucose concentrations during and after haemodialysis that would guide treatment monitoring and adjustments.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK