Aims/IntroductionThis study aimed to investigate the diagnostic potential of two simplified tests, a point‐of‐care nerve conduction device (DPNCheck™) and a coefficient of variation of R‐R intervals (CVR‐R), as an alternative to traditional nerve conduction studies for the diagnosis of diabetic polyneuropathy (DPN) in patients with diabetes.Materials and MethodsInpatients with type 1 or type 2 diabetes (n = 167) were enrolled. The study population consisted of 101 men, with a mean age of 60.8 ± 14.8 years. DPN severity was assessed using traditional nerve conduction studies, and differentiated based on Baba's classification (BC). To examine the explanatory potential of variables in DPNCheck™ and CVR‐R regarding the severity of DPN according to BC, a multiple regression analysis was carried out, followed by a receiver operating characteristic analysis.ResultsBased on BC, 61 participants (36.5% of the total) were categorized as having DPN severity of stage 2 or more. The multiple regression analysis yielded a predictive formula with high predictive power for DPN diagnosis (estimated severity of DPN in BC = 2.258 – 0.026 × nerve conduction velocity [m/s] – 0.594 × ln[sensory nerve action potential amplitude (μV)] + 0.528In[age(years)] – 0.178 × ln[CVR‐R], r = 0.657). The area under the curve in receiver operating characteristic analysis was 0.880. Using the optimal cutoff value for DPN with severer than stage 2, the predictive formula showed good diagnostic efficacy: sensitivity of 83.6%, specificity of 79.2%, positive predictive value of 51.7% and negative predictive value of 76.1%.ConclusionsThese findings suggest that DPN diagnosis using DPNCheck™ and CVR‐R could improve diagnostic efficiency and accessibility for DPN assessment in patients with diabetes.

中文翻译：

---

简化的电生理学方法结合床旁神经传导装置和心电图可以准确诊断糖尿病性多发性神经病

目的/简介本研究旨在研究两种简化测试的诊断潜力：护理点神经传导装置 (DPNCheck™) 和 R-R 间期变异系数 (CVR-R），作为传统神经传导研究的替代方法，用于诊断糖尿病患者的糖尿病多发性神经病（DPN）。 材料和方法 1 型或 2 型糖尿病住院患者（n= 167）已注册。研究人群包括 101 名男性，平均年龄为 60.8 ± 14.8 岁。DPN 严重程度使用传统神经传导研究进行评估，并根据 Baba 分类 (BC) 进行区分。检查 DPNCheck™ 和 CV 中变量的解释潜力R-R根据BC，对DPN的严重程度进行多元回归分析，然后进行接受者操作特征分析。结果根据BC，61名参与者（占总数的36.5%）被分类为具有2级或以上的DPN严重程度。多元回归分析得出了对 DPN 诊断具有较高预测能力的预测公式（BC 中 DPN 的估计严重程度 = 2.258 – 0.026 × 神经传导速度 [m/s] – 0.594 × ln[感觉神经动作电位幅度 (μV)] + 0.528In[年龄（岁）] – 0.178 × ln[CVR-R],r= 0.657）。受试者工作特征分析的曲线下面积为 0.880。使用严重于2期的DPN的最佳截断值，预测公式显示出良好的诊断效果：敏感性为83.6％，特异性为79.2％，阳性预测值为51.7％，阴性预测值为76.1％。结论这些结果表明DPN使用 DPNCheck™ 和 CV 进行诊断R-R可以提高糖尿病患者 DPN 评估的诊断效率和可及性。