Cystatin C enhances glomerular filtration rate estimating equations in kidney transplant recipients

Abstract

Background: The glomerular filtration rate (GFR) estimating equation incorporating both cystatin C and creatinine perform better than those using creatinine or cystatin C alone in patients with reduced GFR. Whether this equation performs well in kidney transplant recipients cross-sectionally, and more importantly, over time has not been addressed.

Methods: We analyzed four GFR estimating equations in participants of the Angiotensin II Blockade for Chronic Allograft Nephropathy Trial (NCT 00067990): Chronic Kidney Disease Epidemiology Collaboration equations based on serum cystatin C and creatinine (eGFR (CKD-EPI-Creat+CysC)), cystatin C alone (eGFR (CKD-EPI-CysC)), creatinine alone (eGFR (CKD-EPI-Creat)) and the Modification of Diet in Renal Disease study equation (eGFR (MDRD)). Iothalamate GFR served as a standard (mGFR).

Results: mGFR, serum creatinine, and cystatin C shortly after transplant were 56.1 ± 17.0 ml/min/1.73 m(2), 1.2 ± 0.4 mg/dl, and 1.2 ± 0.3 mg/l respectively. eGFR (CKD-EPI-Creat+CysC) was most precise (R(2) = 0.50) but slightly more biased than eGFR (MDRD); 9.0 ± 12.7 versus 6.4 ± 15.8 ml/min/1.73 m(2), respectively. This improved precision was most evident in recipients with mGFR >60 ml/min/1.73 m(2). For relative accuracy, eGFR (MDRD) and eGFR (CKD-EPI-Creat+CysC) had the highest percentage of estimates falling within 30% of mGFR; 75.8 and 68.9%, respectively. Longitudinally, equations incorporating cystatin C most closely paralleled the change in mGFR.

Conclusion: eGFR (CKD-EPI-Creat+CysC) is more precise and reflects GFR change over time reasonably well. eGFR (MDRD) had superior performance in recipients with mGFR between 30 and 60 ml/min/1.73 m(2).

Trial registration: ClinicalTrials.gov NCT00067990.

Conflict of interest statement

All authors declare no conflicts of interest

Figures

Figure 1

Bland-Altman plots showing the distribution of errors in estimation of mGFR with eGFR, Deming regression plots with precision (R2 and RMSE), histograms and density curves of the distribution of the difference (bias) between eGFR and mGFR for A) CKD-EPI-Creat+CysC B) CKD-EPI-CysC C) CKD-EPI-Creat D) MDRD.

Figure 1

Bland-Altman plots showing the distribution of errors in estimation of mGFR with eGFR, Deming regression plots with precision (R2 and RMSE), histograms and density curves of the distribution of the difference (bias) between eGFR and mGFR for A) CKD-EPI-Creat+CysC B) CKD-EPI-CysC C) CKD-EPI-Creat D) MDRD.

Figure 1

Bland-Altman plots showing the distribution of errors in estimation of mGFR with eGFR, Deming regression plots with precision (R2 and RMSE), histograms and density curves of the distribution of the difference (bias) between eGFR and mGFR for A) CKD-EPI-Creat+CysC B) CKD-EPI-CysC C) CKD-EPI-Creat D) MDRD.

Figure 1

Bland-Altman plots showing the distribution of errors in estimation of mGFR with eGFR, Deming regression plots with precision (R2 and RMSE), histograms and density curves of the distribution of the difference (bias) between eGFR and mGFR for A) CKD-EPI-Creat+CysC B) CKD-EPI-CysC C) CKD-EPI-Creat D) MDRD.

Figure 2

Mean measured and estimated GFR (along with 95% confidence intervals) at baseline and yearly post-transplant (the distance between the plot for each eGFR and mGFR represents the bias at the given year).