Objective: To develop an estimating formula for glomerular filtration Rate (GFR) based on serum cystatin C in patients with chronic kidney disease (CKD).
Methods: Clinical characteristics of 242 CKD patients were collected. The patients were randomly divided into modeling group and model validation group. The rGFR obtained from 99mTc-DTPA clearance rate was used as a reference value of GFR. s-cystatin C was detected by latex enhanced immunoturbidimetric method. Preliminary linear regression analysis followed by multiple linear regression were performed to investigate the association between s-cystatin C and rGFR. The validity of the estimation formula was tested in the model validation group in comparison with Hoek formula and Orebro formula.
Results: With standardised countdown conversion, s-cystatin showed linear correlation with rGFR, with a correlation coefficient of 0.773. The multiple correlation coefficient, determination coefficient, adjusted R square and std. error of the estimation model were 0.863, 0.745, 0.742, and 0.207, respectively. The residuals P-P probability plot analysis showed that the model residuals fitted into normal distribution with homogeneity of variance. Theeformula was: eGFR = 67/s-cystatin C +3. No significant difference was found between the distribution of eGFR and rGFR. Our formula had an accuracy of 30% and 50%, which were no less than those obtained from Hoek formula and Orebro formula. The new formula also had acceptable bias and high precision. The Bland-Altman analysis and ROC curve analysis showed good applicability of the new formula.
Conclusion: The GFR prediction formula we established has a good prediction performance as comparised with other formulae, which could be used in measuring GFR in CKD patients.