Journal of Diabetes Investigation (Jun 2024)

Simplified electrophysiological approach combining a point‐of‐care nerve conduction device and an electrocardiogram produces an accurate diagnosis of diabetic polyneuropathy

  • Yusuke Hayashi,
  • Tatsuhito Himeno,
  • Yuka Shibata,
  • Nobuhiro Hirai,
  • Yuriko Asada‐Yamada,
  • Sachiko Sasajima,
  • Emi Asano‐Hayami,
  • Mikio Motegi,
  • Saeko Asano,
  • Makoto Kato,
  • Hiromi Nakai‐Shimoda,
  • Hiroya Tani,
  • Emiri Miura‐Yura,
  • Yoshiaki Morishita,
  • Masaki Kondo,
  • Shin Tsunekawa,
  • Takayuki Nakayama,
  • Jiro Nakamura,
  • Hideki Kamiya

DOI
https://doi.org/10.1111/jdi.14174
Journal volume & issue
Vol. 15, no. 6
pp. 736 – 742

Abstract

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Abstract Aims/Introduction This 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 Methods Inpatients 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. Results Based 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%. Conclusions These findings suggest that DPN diagnosis using DPNCheck™ and CVR‐R could improve diagnostic efficiency and accessibility for DPN assessment in patients with diabetes.

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