D D Agoons1, E V Balti1,2, F F Kaze3,4, M Azabji-Kenfack5, G Ashuntantang3,4, A P Kengne6,7,8, E Sobngwi1,3,9, J C Mbanya1,3,9. 1. National Obesity Center, Yaoundé Central Hospital, Yaoundé, Cameroon. 2. Diabetes Research Center, Brussels Free University-VUB, Brussels, Belgium. 3. Department of Internal Medicine and Specialties, University of Yaoundé I, Yaoundé, Cameroon. 4. Nephrology Unit, Yaoundé General Hospital, University of Yaoundé 1, Yaoundé, Cameroon. 5. Department of Physiological Sciences and Biochemistry, University of Yaoundé 1, Yaoundé, Cameroon. 6. Non-Communicable Diseases Research Unit, South African Medical Research Council, Cape Town, South Africa. 7. Department of Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa. 8. The George Institute for Global Health, the University of Sydney, Sydney, NSW, Australia. 9. Laboratory of Molecular Medicine and Metabolism, Biotechnology Centre, University of Yaoundé I, Yaoundé, Cameroon.
Abstract
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.
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.
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