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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.

Point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model