Anke Schwandt1, Michael Denkinger2, Peter Fasching3, Martin Pfeifer4, Christian Wagner5, Jörg Weiland6, Andrej Zeyfang7, Reinhard W Holl8. 1. Institute of Epidemiology and Medical Biometry, ZIBMT, University of Ulm, 89081 Ulm, Germany; German Center for Diabetes Research (DZD), 85764 Munich, Neuherberg, Germany. Electronic address: anke.schwandt@uni-ulm.de. 2. Geriatric Center Ulm/Alb-Donau, Geriatric Medicine at Ulm University, Agaplesion Bethesda Hospital Ulm, 89081 Ulm, Germany. 3. 5th Medical Department, Wilhelminenspital, 1116 Vienna, Austria. 4. Diabetes Center, Clinic Tettnang, 88069 Tettnang, Germany. 5. Outpatient Diabetes Center, 83416 Surheim, Germany. 6. Department of Internal Medicine, Hospital Bad Reichenhall, 83435 Bad Reichenhall, Germany. 7. Sana Hospital Bethesda Stuttgart, 70184 Stuttgart, Germany. 8. Institute of Epidemiology and Medical Biometry, ZIBMT, University of Ulm, 89081 Ulm, Germany; German Center for Diabetes Research (DZD), 85764 Munich, Neuherberg, Germany.
Abstract
AIMS: To analyze the performance of Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), Cockcroft-Gault (CG), and CG calculated with ideal bodyweight (CG-IBW) equations to estimate glomerular filtration rate (eGFR) based on serum creatinine in a large diabetic population. METHODS: 24,516 adults with type-1-diabetes or type-2-diabetes from the multicenter diabetes prospective follow-up registry DPV were analyzed. We compared eGFR and measured GFR (mGFR) based on 24-h urine collection by calculating mean bias (difference), precision (SD of this difference), accuracy (proportion of eGFR within ±10% of mGFR), Bland-Altman-plots. RESULTS: CG overestimates, whereas MDRD, CKD-EPI, and CG-IBW underestimate. Smallest mean bias and highest accuracy (75.3%) were observed for MDRD compared to the other equations (p<0.0001). MDRD and CKD-EPI estimated most accurately in stages 1 (MDRD:57.7%, CKD-EPI:57.3%) and 2 (MDRD:80.2%, CKD-EPI:80.7%). In stages 3 to 5, highest accuracy was observed for the MDRD (stage 3:82.3%, stage 4:77.8%, stage 5:71.0%). Among younger subjects, accuracy was higher using the CKD-EPI (18-<40years:63.7%, 40-<60years:72.8%). Above age 60years, MDRD estimated most accurately (60-<70years:77.3%, ≥70years:78.8%). In males and females, MDRD estimated most accurately (males:75.3%, females:75.3%). CONCLUSION: In this large diabetic cohort, smallest bias and highest accuracy were observed for the MDRD.
AIMS: To analyze the performance of Modification of Diet in Renal Disease (MDRD), Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI), Cockcroft-Gault (CG), and CG calculated with ideal bodyweight (CG-IBW) equations to estimate glomerular filtration rate (eGFR) based on serum creatinine in a large diabetic population. METHODS: 24,516 adults with type-1-diabetes or type-2-diabetes from the multicenter diabetes prospective follow-up registry DPV were analyzed. We compared eGFR and measured GFR (mGFR) based on 24-h urine collection by calculating mean bias (difference), precision (SD of this difference), accuracy (proportion of eGFR within ±10% of mGFR), Bland-Altman-plots. RESULTS:CG overestimates, whereas MDRD, CKD-EPI, and CG-IBW underestimate. Smallest mean bias and highest accuracy (75.3%) were observed for MDRD compared to the other equations (p<0.0001). MDRD and CKD-EPI estimated most accurately in stages 1 (MDRD:57.7%, CKD-EPI:57.3%) and 2 (MDRD:80.2%, CKD-EPI:80.7%). In stages 3 to 5, highest accuracy was observed for the MDRD (stage 3:82.3%, stage 4:77.8%, stage 5:71.0%). Among younger subjects, accuracy was higher using the CKD-EPI (18-<40years:63.7%, 40-<60years:72.8%). Above age 60years, MDRD estimated most accurately (60-<70years:77.3%, ≥70years:78.8%). In males and females, MDRD estimated most accurately (males:75.3%, females:75.3%). CONCLUSION: In this large diabetic cohort, smallest bias and highest accuracy were observed for the MDRD.
Authors: Peter Bramlage; Sascha R Tittel; Christian Wagner; Kerstin König; Dirk Raddatz; Rosmarie Weber-Lauffer; Diether Erath; Jost Hilgenberg; Carsten Spies; Thomas Danne; Maximilian Gabler; Johannes Foersch; Ludwin Ley; Jochen Seufert Journal: BMJ Open Diabetes Res Care Date: 2020-07
Authors: Peter Bramlage; Stefanie Lanzinger; Eva Hess; Simon Fahrner; Christoph H J Heyer; Mathias Friebe; Ivo Buschmann; Thomas Danne; Reinhard W Holl; Jochen Seufert Journal: BMC Nephrol Date: 2020-07-29 Impact factor: 2.388