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Saudi Journal of Kidney Diseases and Transplantation
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Year : 2009  |  Volume : 20  |  Issue : 1  |  Page : 86-90
Utility of predicted creatinine clearance using MDRD formula compared with other predictive formulas in Nigerian patients


Renal and Cardiology Units, Department of Medicine, Obafemi Awolowo University Teaching Hospitals Complex, Ile-Ife, Nigeria

Correspondence Address:
Sanusi Abubakr Abefe
Department of Medicine, Obafemi Awolowo University Teaching Hospitals Complex, PMB 5538, Ile-Ife, Osun State, Post Code 220001
Nigeria
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PMID: 19112223

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The new predictive formula generated during the study of Modification of Diet in Renal Disease (MDRD) to estimate the glomerular filtration rate in chronic kidney disease (CKD) patients was found to be superior to existing predictive formulas in all races including black Americans. We had previously published a study evaluating and comparing 5 predictive formulas and their applicability in Nigerian CKD patients and normal subjects. The existing data from this study were re-analyzed and the 5 previous formulas compared with the MDRD formula. All the pre­dictive formulas including the MDRD formula correlated significantly with measured creatinine clearance in CKD subjects and controls. Correlation Coefficient, (r) ranged between 0.908-0.968 and Coefficient of Determination, (r 2 ), ranged between 0.826-0.936. There was also good corre­lation between the measured and predicted CrCl in healthy state, though the r and r 2 values were weaker (0.718-0.957) and (0.516-0.916). Specifically, MDRD formula was only superior to Jelliffe and Gates and not so to Cockcroft and Gault, Hull, and Mawer equations in CRF. MDRD formula yielded r= 0.93 and r 2 = 0.86 and the values for Cockcroft and Gault, Hull and Mawer ranged between 0.96-0.97 and 0.93-0.94 respectively. In conclusion, MDRD formula, though useful and applicable was not superior to existing formulas. Cockcroft and Gault equation can still be used due to the ease of recall and its high correlation coefficient in health and disease states.


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