Comparison between the Beta-2 Microglobulin-Based Equation and the CKD-EPI Equation for Estimating GFR in CKD Patients in China: ES-CKD Study
Authors: Yue Lili , Pan Binbin , Shi Xiumin , Du Xin 
Published: February 2020

<b><i>Background:</i></b> Beta-2 microglobulin (B2M) and cystatin C are novel glomerular filtration markers that have a stronger association with adverse outcomes than creatinine. The B2M-based glomerular filtration rate (GFR) estimating equation was built in 2016. Several new creatinine and cystatin C equations were developed in 2019 in China. However, external validation of these new equations remains to be seen. <b><i>Methods:</i></b> This is a prospective cohort study. The equations were validated in a population totaling 830 participants (median age 62 years). These equations include the B2M-based equation (built in 2016), three CKD-EPI equations (built in 2009 and 2012), three Yang-Du equations (C-CKD-EPIscr, C-CKD-EPIcys, and C-CKD-EPIscr-cys equations, all of which were Chinese-modified CKD-EPI equations developed by Yang et al. in 2019), and a Xiangya equation (a creatinine-based equation built in the Third Xiangya Hospital in 2019). The estimated GFR (eGFR) calculated separately by 8 equations (B2M GFR, CKD-EPIscr, CKD-EPIcys, CKD-EPIscr-cys, C-CKD-EPIscr, C-CKD-EPIcys, C-CKD-EPIscr-cys, and Xiangya equations) was compared with the reference GFR (rGFR) measured by the <sup>99m</sup>Tc-DTPA renal dynamic imaging method. Participants were divided into CKD stage 1–5 specific subgroups. The primary outcomes of this study were bias, precision (interquartile range of difference, IQR), and accuracy (the proportion of eGFR within 30% of rGFR [P30] and root mean square error [RMSE]) of eGFR versus rGFR. <b><i>Results:</i></b> The B2M-based equation was worse than CKD-EPI equations and Yang-Du equations in most outcomes. CKD-EPIscr and C-CKD-EPIscr equations had a larger area under the receiver operating characteristic curve (ROC<sup>AUC</sup>). The CKD-EPIscr equation had the highest sensitivity (83.3%) and the Xiangya equation the highest specificity (89.5%) to diagnose CKD. The bias was the lowest in CKD-EPIcys and C-CKD-EPIscr-cys equations by median and mean difference (1.23 and –1.42, respectively). The Xiangya equation yielded the highest bias by both median and mean difference (8.29 and 6.52, respectively). The C-CKD-EPIscr equation was the most accurate with the highest P30 value (68.1%) and most precise with the lowest IQR (19). The Xiangya equation had the best RMSE (lowest RMSE, 0.56), and gave the best performance in the CKD stage 2 subgroup. The C-CKD-EPIscr-cys equation achieved the lowest bias in CKD stage 3–5 (<i>p</i> = 0.663, 0.104, and 0.130, respectively, compared with rGFR). <b><i>Conclusion:</i></b> The B2M-based equation was worse than CKD-EPI and Yang-Du equations on the whole. CKD-EPIcys and C-CKD-EPIscr-cys equations had the lowest bias, whereas the Xiangya equation yielded the highest bias. The Xiangya equation gave the best performance in the CKD stage 2 subgroup, while the C-CKD-EPIscr-cys equation achieved the lowest bias in CKD stage 3–5. Further work to improve the performance of the GFR estimating equation is needed.