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Predictive performance of eGFR equations in South Africans of African and Indian ancestry compared with 99mTc-DTPA imaging

  • Nephrology – Original Paper
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Abstract

Background

South African guidelines for early detection and management of chronic kidney disease (CKD) recommend using the Cockcroft–Gault (CG) or Modification of Diet in Renal Disease (MDRD) equations for calculating estimated glomerular filtration rate (eGFR) with the correction factor, 1.212, included for MDRD-eGFR in black patients. We compared eGFR against technetium-99m-diethylenetriaminepentaacetic acid (99mTc-DTPA) imaging.

Methods

Using clinical records, we retrospectively recorded demographic, clinical, and laboratory data as well as 99mTc-DTPA-measured GFR (mGFR) results obtained from routine visits. Data from 148 patients of African (n = 91) and Indian (n = 57) ancestry were analyzed.

Results

Median (IQR) mGFR was 38.5 (44) ml/min/1.73 m2, with no statistical difference between African and Indian patients (P = 0. 573). In African patients with stage 3 CKD, MDRD-eGFR (unadjusted for black ethnicity) overestimated mGFR by 5.3% [2.0 (16.0) ml/min/1.73 m2] compared to CG-eGFR and MDRD-eGFR (corrected for black ethnicity) that overestimated mGFR by 17.7% [6.0 (15.0) ml/min/1.73 m2] and 17.1% [6.0 (17.5) ml/min/1.73 m2], respectively. In stage 1–2, CKD eGFR overestimated mGFR by 52.5, 38.0, and 19.3% for CG, MDRD (ethnicity-corrected), and MDRD (without correction), respectively. In Indian stage 3 CKD patients, MDRD-eGFR underestimated mGFR by 35.6% [−21.0 (6.5) ml/min/1.73 m2] and CG-eGFR by 4.4% [−2.0 (27.0) ml/min/1.73 m2], while in stage 1–2 CKD, CG-eGFR and MDRD-eGFR overestimated mGFR by 13.8 and 6.3%, respectively.

Conclusion

MDRD-eGFR calculated without the African-American correction factor improved GFR prediction in African CKD patients and using the MDRD correction factor of 1.0 in Indian patients as in Caucasians may be inappropriate.

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References

  1. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D (1999) A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation for the Modification of Diet in Renal Disease Study Group. Ann Intern Med 130:461–470

    PubMed  CAS  Google Scholar 

  2. Levey AS, Greene T, Kusek J et al (2000) A simplified equation to predict glomerular filtration rate from serum creatinine (abstract). J Am Soc Nephrol 11:155A

    Google Scholar 

  3. Levey AS, Stevens LA, Schmid CH (2009) A new equation to estimate glomerular filtration rate. Ann Intern Med 150:604–612

    PubMed  Google Scholar 

  4. South African Renal Society recommendations for Early Detection and Management of Chronic Kidney Disease. www.sa-renalsociety.org (accessed 12th November 2009)

  5. National Kidney Foundation—K/DOQI (2002) Clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification. Am J Kidney Dis 39:S1–S266

    Article  Google Scholar 

  6. van Deventer HE, George JA, Paiker JE, Becker PJ, Katz IJ (2008) Estimating glomerular filtration rate in black South Africans by use of the modification of diet in renal disease and Cockcroft-Gault equations. Clin Chem 54:1197–1202

    Article  PubMed  Google Scholar 

  7. Eastwood JB, Kerry SM, Plange-Rhule J et al (2010) Assessment of GFR by four methods in adults in Ashanti, Ghana: the need for an eGFR equation for lean African populations. Nephrol Dial Transpl 25:2178–2187

    Article  Google Scholar 

  8. Mid-year 2009 population estimates. www.statssa.gov.za (accessed 17th November 2009)

  9. Cockcroft DW, Gault MH (1976) Prediction of creatinine clearance from serum creatinine. Nephron 16:31–41

    Article  PubMed  CAS  Google Scholar 

  10. Bland JM, Altman DG (1986) Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1:307–310

    Article  PubMed  CAS  Google Scholar 

  11. Rule AD, Gussak HM, Pond GR et al (2004) Measured and estimated GFR in healthy potential kidney donors. Am J Kidney Dis 43:112–119

    Article  PubMed  Google Scholar 

  12. Rule AD, Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, Cosio FG (2004) Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann Intern Med 141:929–937

    PubMed  CAS  Google Scholar 

  13. Bostom AG, Kronenberg F, Ritz E (2002) Predictive performance of renal function equations for patients with chronic kidney disease and normal serum creatinine levels. J Am Soc Nephrol 13:2140–2144

    Article  PubMed  CAS  Google Scholar 

  14. Poggio ED, Wang X, Greene T et al (2005) Performance of the Modification of Diet in Renal Disease and Cockcroft-Gault equations in the estimation of GFR in health and in chronic kidney disease. J Am Soc Nephrol 16:459–466

    Article  PubMed  Google Scholar 

  15. Mahajan S, Mukhiya GK, Singh R et al (2005) Assessing glomerular filtration rate in healthy Indian adults: a comparison of various prediction equations. J Nephrol 18:257–261

    PubMed  Google Scholar 

  16. Myers GL, Miller WG, Coresh J et al (2006) Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program. Clin Chem 52:5–18

    Article  PubMed  CAS  Google Scholar 

  17. Hallan S, Asberg A, Lindberg M, Johnsen H (2004) Validation of the Modification of Diet in Renal Disease formula for estimating GFR with special emphasis on calibration of the serum creatinine assay. Am J Kidney Dis 44:84–93

    Article  PubMed  Google Scholar 

  18. Kuan Y, Hossain M, Surman J, El Nahas AM, Haylor J (2005) GFR prediction using the MDRD and Cockcroft and Gault equations in patients with end-stage renal disease. Nephrol Dial Transpl 20:2394–2401

    Article  Google Scholar 

  19. Botev R, Mallié JP, Couchoud C (2009) Estimating glomerular filtration rate: Cockcroft-Gault and Modification of Diet in Renal Disease formulas compared to renal inulin clearance. Clin J Am Soc Nephrol 4:899–906

    Article  PubMed  CAS  Google Scholar 

  20. Comty CM (1968) A longitudinal study of body composition in terminal uremics treated by regular hemodialysis. I. Body composition before treatment. Can Med Assoc J 98:482–491

    PubMed  CAS  Google Scholar 

  21. Coles GA (1972) Body composition in chronic renal failure. Q J Med 41:25–47

    PubMed  CAS  Google Scholar 

  22. Mitch WE, May RC, Maroni BJ (1989) Review: mechanisms for abnormal protein metabolism in uremia. J Am Coll Nutr 8:305–309

    PubMed  CAS  Google Scholar 

  23. Zuo L, Ma YC, Zhou YH, Wang M, Xu GB, Wang HY (2005) Application of GFR-estimating equations in Chinese patients with chronic kidney disease. Am J Kidney Dis 45:463–472

    Article  PubMed  Google Scholar 

  24. Jafar TH, Schmid CH, Levey AS (2005) Serum creatinine as marker of kidney function in South Asians: a study of reduced GFR in adults in Pakistan. J Am Soc Nephrol 16:1413–1419

    Article  PubMed  Google Scholar 

  25. Imai E, Horio M, Nitta K et al (2007) Estimation of glomerular filtration rate by the MDRD study equation modified for Japanese patients with chronic kidney disease. Clin Exp Nephrol 11:41–50

    Article  PubMed  Google Scholar 

  26. Ma YC, Zuo L, Chen JH et al (2006) Modified glomerular filtration rate estimating equation for Chinese patients with chronic kidney disease. J Am Soc Nephrol 17:2937–2944

    Article  PubMed  Google Scholar 

  27. Manjunath G, Sarnak MJ, Levey AS (2001) Estimating the glomerular filtration rate: do’s and don’ts for assessing kidney function. Postgrad Med 110:55–62

    Article  PubMed  CAS  Google Scholar 

  28. O’Keefe SJ, Kidd M, Espitalier-Noel G, Owira P (1999) Rarity of colon cancer in Africans is associated with low animal product consumption, not fiber. Am J Gastroenterol 94:1373–1380

    Article  PubMed  Google Scholar 

  29. O’Keefe SJ, Chung D, Mahmoud N et al (2007) Why do African Americans get more colon cancer than Native Africans? J Nutr 137:175S–182S

    PubMed  Google Scholar 

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Correspondence to Nomandla D. Madala.

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Madala, N.D., Nkwanyana, N., Dubula, T. et al. Predictive performance of eGFR equations in South Africans of African and Indian ancestry compared with 99mTc-DTPA imaging. Int Urol Nephrol 44, 847–855 (2012). https://doi.org/10.1007/s11255-011-9928-7

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  • DOI: https://doi.org/10.1007/s11255-011-9928-7

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