Department of Nephrology, The Key Laboratory for the Prevention and Treatment of Chronic Kidney Disease of Chongqing, Chongqing Clinical Research Center of Kidney and Urology Diseases, Xinqiao Hospital, Army Medical University (Third Military Medical University), Chongqing, China.
Front Endocrinol (Lausanne). 2022 Jan 14;12:731187. doi: 10.3389/fendo.2021.731187. eCollection 2021.
To investigate the potential role of renal arterial resistance index (RI) in the differential diagnosis between diabetic kidney disease (DKD) and non-diabetic kidney disease (NDKD) and establish a better-quantified differential diagnostic model.
We consecutively reviewed 469 type 2 diabetes patients who underwent renal biopsy in our center. According to the renal biopsy results, eligible patients were classified into the DKD group and the NDKD group. The diagnostic significance of RI was evaluated by receiver operating characteristic (ROC) curve analysis. Logistic regression analysis was used to search for independent risk factors associated with DKD. Then a novel diagnostic model was established using multivariate logistic regression analysis.
A total of 332 DKD and 137 NDKD patients were enrolled for analysis. RI was significantly higher in the DKD group compared with those in the NDKD group (0.70 vs. 0.63, < 0.001). The optimum cutoff value of RI for predicting DKD was 0.66 with sensitivity (69.2%) and specificity (80.9%). Diabetic retinopathy, diabetes duration ≥ 60 months, HbA1c ≥ 7.0(%), RI ≥ 0.66, and body mass index showed statistical significance in the multivariate logistic regression analysis. Then, we constructed a new diagnostic model based on these results. And the validation tests indicated that the new model had good sensitivity (81.5%) and specificity (78.6%).
RI has a potential role in discriminating DKD from NDKD. The RI-based predicting model can be helpful for differential diagnosis of DKD and NDKD.
探讨肾动脉阻力指数(RI)在糖尿病肾病(DKD)与非糖尿病肾病(NDKD)鉴别诊断中的潜在作用,并建立一个更好量化的鉴别诊断模型。
我们连续回顾了在我院接受肾活检的 469 例 2 型糖尿病患者。根据肾活检结果,将符合条件的患者分为 DKD 组和 NDKD 组。通过受试者工作特征(ROC)曲线分析评估 RI 的诊断意义。使用逻辑回归分析寻找与 DKD 相关的独立危险因素。然后使用多元逻辑回归分析建立新的诊断模型。
共纳入 332 例 DKD 和 137 例 NDKD 患者进行分析。与 NDKD 组相比,DKD 组 RI 显著升高(0.70 比 0.63,<0.001)。RI 预测 DKD 的最佳截断值为 0.66,其敏感性(69.2%)和特异性(80.9%)。糖尿病视网膜病变、糖尿病病程≥60 个月、HbA1c≥7.0%、RI≥0.66 和体重指数在多因素逻辑回归分析中具有统计学意义。然后,我们根据这些结果构建了一个新的诊断模型。验证试验表明,新模型具有良好的敏感性(81.5%)和特异性(78.6%)。
RI 对鉴别 DKD 与 NDKD 具有一定作用。基于 RI 的预测模型有助于 DKD 和 NDKD 的鉴别诊断。