Background: We aim to design a scoring model for differential diagnosis between diabetic nephropathy (DN) and nondiabetic renal disease (NDRD) in type 2 diabetic patients through a combination of clinical variables.
Methods: A total of 170 patients with type 2 diabetes who underwent kidney biopsies were included and divided into three groups according to pathological findings: DN group (n = 46), MIX group (DN + NDRD, n = 54), NDRD group (n = 70). Clinical characteristics and laboratory data were collected and compared among groups. Variables with a significant statistical difference between DN and NDRD patients were analyzed by logistic regression to predict the presence of NDRD; then a scoring model was established based on the regression coefficient and further validated in an independent cohort of 67 patients prospectively.
Results: On biopsy, 72.9% of patients had NDRD, and the most common pathological type was membranous nephropathy. The established scoring model for predicting NDRD included five predictors: age, systolic blood pressure, hemoglobin, duration of diabetes, and absence of diabetic retinopathy. The model demonstrated good discrimination and calibration (area under curve [AUC] 0.863, 95% CI, 0.800-0.925; Hosmer-Lemeshow [H-L] P = .062). Furthermore, high prediction accuracy (AUC = 0.900; 95% CI, 0.815-0.985) in the validation cohort proved the stability of the model.
Conclusions: We present a simple, robust scoring model for predicting the presence of NDRD with high accuracy (0.85) for the first time. This decision support tool provides a noninvasive method for differential diagnosis of DN and NDRD, which may help clinicians assess the risk-benefit ratio of kidney biopsy for type 2 diabetic patients with renal impairment.
背景: 本研究旨在通过整合相关临床指标, 建立2型糖尿病患者中糖尿病肾病(diabetic nephropathy, DN)和非糖尿病肾病(non-diabetic renal disease, NDRD)的鉴别诊断评分模型。 方法: 纳入170例接受肾脏穿刺活检术的2型糖尿病患者, 根据病理结果分为三组: DN 组(n=46), MIX 组(DN+NDRD, n=54), NDRD 组(n=70), 比较分析三组患者的临床及实验室数据, 将DN与NDRD两组患者有显著差异的变量通过 logistic多元回归分析预测 NDRD, 基于回归系数建立诊断评分模型, 并在另一独立验证队列(n=67)中前瞻性地验证模型准确度。 结果: 活检患者中NDRD的检出率为72.9%, 其中最常见的病理类型为膜性肾病。NDRD诊断评分模型包括5个预测因素:年龄、收缩压、血红蛋白、糖尿病病程和排除糖尿病视网膜病变。该模型具有良好的区分度(曲线下面积[area under the curve, AUC]= 0.863; 95%CI 0.800-0.925)和校准度(H-L检验 P=0.062)。应用于验证队列时依然呈现出较高的准确率(AUC=0.900; 95% CI, 0.815-0.985), 提示其预测稳定性较好。 结论: 本研究首次建立了用于预测NDRD诊断的评分模型, 准确率为85%, 可以作为一种无创性鉴别诊断方法有效地区分DN和NDRD的工具, 因此有助于医生评估有肾脏损害的2型糖尿病患者肾活检的风险-收益比。.
Keywords: 2型糖尿病; diabetic nephropathy; differential diagnosis; type 2 diabetes; 糖尿病肾病; 鉴别诊断.
© 2019 Ruijin Hospital, Shanghai Jiaotong University School of Medicine and John Wiley & Sons Australia, Ltd.