Point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model. Issue 4 (14th September 2020)

• Record Type: Journal Article
• Authors: Kamiya, Hideki; Shibata, Yuka; Himeno, Tatsuhito; Tani, Hiroya; Nakayama, Takayuki; Murotani, Kenta; Hirai, Nobuhiro; Kawai, Miyuka; Asada‐Yamada, Yuriko; Asano‐Hayami, Emi; Nakai‐Shimoda, Hiromi; Yamada, Yuichiro; Ishikawa, Takahiro; Morishita, Yoshiaki; Kondo, Masaki; Tsunekawa, Shin; Kato, Yoshiro; Baba, Masayuki; Nakamura, Jiro
• Is Part Of: Journal of diabetes investigation. Volume 12:Issue 4(2021)
• Page Start: 583
• Page End: 591
• Publication Date: 2020-09-14
• Subjects: Diabetic neuropathies -- Electromyography -- Point‐of‐care testing; Diabetes -- Periodicals; Diabetes -- Research -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1111/jdi.13386
• Languages: English
• ISSNs: 2040-1116

Abstract

Abstract: Aims/Introduction: A gold standard in the diagnosis of diabetic polyneuropathy (DPN) is a nerve conduction study. However, as a nerve conduction study requires expensive equipment and well‐trained technicians, it is largely avoided when diagnosing DPN in clinical settings. Here, we validated a novel diagnostic method for DPN using a point‐of‐care nerve conduction device as an alternative way of diagnosis using a standard electromyography system. Materials and Methods: We used a multiple regression analysis to examine associations of nerve conduction parameters obtained from the device, DPNCheck™, with the severity of DPN categorized by the Baba classification among 375 participants with type 2 diabetes. A nerve conduction study using a conventional electromyography system was implemented to differentiate the severity in the Baba classification. The diagnostic properties of the device were evaluated using a receiver operating characteristic curve. Results: A multiple regression model to predict the severity of DPN was generated using sural nerve conduction data obtained from the device as follows: the severity of DPN = 2.046 + 0.509 × ln(age [years]) − 0.033 × (nerve conduction velocity [m/s]) − 0.622 × ln(amplitude of sensory nerve action potential [µV]), r = 0.649. Using a cut‐off value of 1.3065 in the model, moderate‐to‐severe DPN was effectively diagnosed (area under the receiver operating characteristic curve 0.871, sensitivity 70.1%, specificity 87.7%, positive predictive value 83.0%, negative predictive value 77.3%, positive likelihood ratio 5.67, negative likelihood ratio 0.34). Conclusions: Nerve conduction parameters in the sural nerve acquired by the handheld device successfully predict the severity of DPN. Abstract : Currently, most diagnostic criteria for diabetic polyneuropathy consist of physical examinations; for example, Achilles tendon reflex or vibration sensation with a tuning fork. Therefore, the low diagnostic sensitivity of these criteria should be improved. Although the gold standard for quantitative evaluation of diabetic polyneuropathy is an electromyography system, these have not become widely used due to their high cost and necessity of an advanced examination technique. The current work verified the efficacy of a handheld nerve conduction device. If clinicians recognize the validity and reliability of the device, this simplified nerve conduction study could be carried out in various clinical settings, including clinics or hospitals in developing or developed countries. The worldwide utilization of the device would improve diagnostic sensitivity for diabetic polyneuropathy in the future.