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

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...

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Autores principales: 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
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015817/
https://www.ncbi.nlm.nih.gov/pubmed/32799422
http://dx.doi.org/10.1111/jdi.13386
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author 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
author_facet 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
author_sort Kamiya, Hideki
collection PubMed
description 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.
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spelling pubmed-80158172021-04-02 Point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model 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 J Diabetes Investig Articles 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. John Wiley and Sons Inc. 2020-09-14 2021-04 /pmc/articles/PMC8015817/ /pubmed/32799422 http://dx.doi.org/10.1111/jdi.13386 Text en © 2020 The Authors. Journal of Diabetes Investigation published by Asian Association for the Study of Diabetes (AASD) and John Wiley & Sons Australia, Ltd This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.
spellingShingle Articles
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
Point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model
title Point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model
title_full Point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model
title_fullStr Point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model
title_full_unstemmed Point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model
title_short Point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: A quantitative, but easy‐to‐use, prediction model
title_sort point‐of‐care nerve conduction device predicts the severity of diabetic polyneuropathy: a quantitative, but easy‐to‐use, prediction model
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8015817/
https://www.ncbi.nlm.nih.gov/pubmed/32799422
http://dx.doi.org/10.1111/jdi.13386
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