Department of Nephrology and Laboratory Medicine, Faculty of Medicine, Institute of Medical, Pharmaceutical and Health Sciences, Graduate School of Medical Sciences, Kanazawa University, Kanazawa, Japan.
Nephrology Center, Toranomon Hospital, Tokyo, Japan.
Nephrol Dial Transplant. 2019 Jul 1;34(7):1182-1188. doi: 10.1093/ndt/gfy121.
Biopsy-based studies on nephrosclerosis are lacking and the clinicopathological predictors for progression of chronic kidney disease (CKD) are not well established.
We retrospectively assessed 401 patients with biopsy-proven nephrosclerosis in Japan. Progression of CKD was defined as new-onset end-stage renal disease, decrease of estimated glomerular filtration rate (eGFR) by ≥50% or doubling of serum creatinine, and the sub-distribution hazard ratio (SHR) with 95% confidence interval (CI) for CKD progression was determined for various clinical and histological characteristics in competing risks analysis. The incremental value of pathological information for predicting CKD progression was assessed by calculating Harrell's C-statistics, the Akaike information criterion (AIC), net reclassification improvement and integrated discrimination improvement.
During a median follow-up period of 5.3 years, 117 patients showed progression of CKD and 10 patients died before the defined kidney event. Multivariable sub-distribution hazards model identified serum albumin (SHR 0.48; 95% CI 0.35-0.67), hemoglobin A1c (SHR 0.71; 95% CI 0.54-0.94), eGFR (SHR 0.98; 95% CI 0.97-0.99), urinary albumin/creatinine ratio (UACR) (SHR 1.18; 95% CI 1.08-1.29), percentage of segmental/global glomerulosclerosis (%GS) (SHR 1.01; 95% CI 1.00-1.02) and interstitial fibrosis and tubular atrophy (IFTA) (SHR 1.52; 95% CI 1.20-1.92) as risk factors for CKD progression. The C-statistic of a model with only clinical variables was improved by adding %GS (0.790 versus 0.796, P < 0.01) and IFTA (0.790 versus 0.811, P < 0.01). The reclassification statistic was also improved after adding the biopsy data to the clinical data. The model including IFTA was superior, with the lowest AIC.
The study implies that in addition to the traditional markers of eGFR and UACR, we may explore the markers of serum albumin and hemoglobin A1c, which are widely available but not routinely measured in patients with nephrosclerosis, and the biopsy data, especially the data on the severity of interstitial damage, for the better prediction of CKD progression in patients with nephrosclerosis.
缺乏基于肾活检的肾血管性硬化症研究,慢性肾脏病(CKD)进展的临床病理预测因素尚未确定。
我们回顾性评估了日本 401 名经肾活检证实为肾血管性硬化症的患者。CKD 的进展定义为新发终末期肾病、估算肾小球滤过率(eGFR)下降≥50%或血清肌酐翻倍,并用竞争风险分析确定各种临床和组织学特征的慢性肾脏病进展的亚分布风险比(SHR)及其 95%置信区间(CI)。通过计算 Harrell 的 C 统计量、赤池信息量准则(AIC)、净重新分类改善和综合判别改善来评估病理信息对预测 CKD 进展的增量价值。
在中位随访 5.3 年期间,117 例患者出现 CKD 进展,10 例患者在规定的肾脏事件前死亡。多变量亚分布风险模型确定血清白蛋白(SHR 0.48;95%CI 0.35-0.67)、糖化血红蛋白 A1c(SHR 0.71;95%CI 0.54-0.94)、eGFR(SHR 0.98;95%CI 0.97-0.99)、尿白蛋白/肌酐比值(UACR)(SHR 1.18;95%CI 1.08-1.29)、节段性/全球肾小球硬化(%GS)(SHR 1.01;95%CI 1.00-1.02)和间质纤维化和肾小管萎缩(IFTA)(SHR 1.52;95%CI 1.20-1.92)是 CKD 进展的危险因素。仅包含临床变量的模型的 C 统计量通过添加 %GS(0.790 与 0.796,P<0.01)和 IFTA(0.790 与 0.811,P<0.01)得到改善。在将活检数据添加到临床数据后,重新分类统计数据也得到了改善。包含 IFTA 的模型更好,AIC 最低。
该研究表明,除了 eGFR 和 UACR 等传统标志物外,我们还可以探索血清白蛋白和糖化血红蛋白等标志物,这些标志物在肾血管性硬化症患者中广泛可用但未常规测量,以及活检数据,特别是间质损伤严重程度的数据,以便更好地预测肾血管性硬化症患者的 CKD 进展。