Open AccessPoint‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model
Author(s) -
Kamiya Hideki,
Shibata Yuka,
Himeno Tatsuhito,
Tani Hiroya,
Nakayama Takayuki,
Murotani Kenta,
Hirai Nobuhiro,
Kawai Miyuka,
AsadaYamada Yuriko,
AsanoHayami Emi,
NakaiShimoda Hiromi,
Yamada Yuichiro,
Ishikawa Takahiro,
Morishita Yoshiaki,
Kondo Masaki,
Tsunekawa Shin,
Kato Yoshiro,
Baba Masayuki,
Nakamura Jiro
Publication year - 2021
Publication title -
journal of diabetes investigation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.089
H-Index - 50
eISSN - 2040-1124
pISSN - 2040-1116
DOI - 10.1111/jdi.13386
Subject(s) - medicine , receiver operating characteristic , electromyography , nerve conduction velocity , area under the curve , polyneuropathy , nerve conduction study , likelihood ratios in diagnostic testing , gold standard (test) , nerve conduction , sural nerve , diabetes mellitus , cardiology , surgery , physical medicine and rehabilitation , endocrinology
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.