Lv Liangjing, Chen Xiangjun, Hu Jinbo, Wu Jinshan, Luo Wenjin, Shen Yan, Lan Rui, Li Xue, Wang Yue, Luo Ting, Yang Shumin, Li Qifu, Wang Zhihong
Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Front Endocrinol (Lausanne). 2022 Jun 3;13:873318. doi: 10.3389/fendo.2022.873318. eCollection 2022.
The study aimed to evaluate the performance of a predictive model using the kidney failure risk equation (KFRE) for end-stage renal disease (ESRD) in diabetes and to investigate the impact of glomerular filtration rate (GFR) as estimated by different equations on the performance of the KFRE model in diabetes.
A total of 18,928 individuals with diabetes without ESRD history from the UK Biobank, a prospective cohort study initiated in 2006-2010, were included in this study. Modification of diet in renal disease (MDRD), chronic kidney disease epidemiology collaboration (CKD-EPI) or revised Lund-Malmö (r-LM) were used to estimate GFR in the KFRE model. Cox proportional risk regression was used to determine the correlation coefficients between each variable and ESRD risk in each model. Harrell's C-index and net reclassification improvement (NRI) index were used to evaluate the differentiation of the models. Analysis was repeated in subgroups based on albuminuria and hemoglobin A1C (HbA1c) levels.
Overall, 132 of the 18,928 patients developed ESRD after a median follow-up of 12 years. The Harrell's C-index based on GFR estimated by CKD-EPI, MDRD, and r-LM was 0.914 (95% CI = 0.8812-0.9459), 0.908 (95% CI = 0.8727-0.9423), and 0.917 (95% CI = 0.8837-0.9496), respectively. Subgroup analysis revealed that in diabetic patients with macroalbuminuria, the KFRE model based on GFR estimated by r-LM (KFRE-eGFR) had better differentiation compared to the KFRE model based on GFR estimated by CKD-EPI (KFRE-eGFR) with a KFRE-eGFR C-index of 0.846 (95% CI = 0.797-0.894, p = 0.025), while the KFRE model based on GFR estimated by MDRD (KFRE-eGFR) showed no significant difference compared to the KFRE-eGFR (KFRE-eGFR C-index of 0.837, 95% CI = 0.785-0.889, p = 0.765). Subgroup analysis of poor glycemic control (HbA1c >8.5%) demonstrated the same trend. Compared to KFRE-eGFR (C-index = 0.925, 95% CI = 0.874-0.976), KFRE-eGFR had a C-index of 0.935 (95% CI = 0.888-0.982, p = 0.071), and KFRE-eGFR had a C-index of 0.925 (95% CI = 0.874-0.976, p = 0.498).
In adults with diabetes, the r-LM equation performs better than the CKD-EPI and MDRD equations in the KFRE model for predicting ESRD, especially for those with macroalbuminuria and poor glycemic control (HbA1c >8.5%).
本研究旨在评估使用肾衰竭风险方程(KFRE)预测糖尿病终末期肾病(ESRD)的模型性能,并探讨不同方程估算的肾小球滤过率(GFR)对糖尿病患者KFRE模型性能的影响。
设计、研究地点、参与者与测量方法:本研究纳入了英国生物银行中18928例无ESRD病史的糖尿病患者,该前瞻性队列研究始于2006 - 2010年。在KFRE模型中,采用肾病饮食改良(MDRD)、慢性肾脏病流行病学协作组(CKD - EPI)或修订的隆德 - 马尔默(r - LM)方程估算GFR。采用Cox比例风险回归确定各模型中每个变量与ESRD风险之间的相关系数。使用Harrell's C指数和净重新分类改善(NRI)指数评估模型的区分度。根据蛋白尿和糖化血红蛋白(HbA1c)水平在亚组中重复进行分析。
总体而言,18928例患者中132例在中位随访12年后发生ESRD。基于CKD - EPI、MDRD和r - LM估算的GFR的Harrell's C指数分别为0.914(95%CI = 0.8812 - 0.9459)、0.908(95%CI = 0.8727 - 0.9423)和0.917(95%CI = 0.8837 - 0.9496)。亚组分析显示,在患有大量蛋白尿的糖尿病患者中,基于r - LM估算的GFR的KFRE模型(KFRE - eGFR)与基于CKD - EPI估算的GFR的KFRE模型相比,具有更好的区分度,KFRE - eGFR的C指数为0.846(95%CI = 0.797 - 0.894,p = 0.025),而基于MDRD估算的GFR的KFRE模型(KFRE - eGFR)与KFRE - eGFR相比无显著差异(KFRE - eGFR的C指数为0.837,95%CI = 0.785 - 0.889,p = 0.765)。血糖控制不佳(HbA1c > 8.5%)的亚组分析显示相同趋势。与KFRE - eGFR(C指数 = 0.925,95%CI = 0.874 - 0.976)相比,KFRE - eGFR的C指数为0.935(95%CI = 0.888 - 0.982,p = 0.071),KFRE - eGFR的C指数为0.925(95%CI = 0.874 - 0.976,p = 0.498)。
在患有糖尿病的成年人中,在预测ESRD的KFRE模型中,r - LM方程比CKD - EPI和MDRD方程表现更好,尤其是对于那些患有大量蛋白尿和血糖控制不佳(HbA1c > 8.5%)的患者。