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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...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2020
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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. |
format | Online Article Text |
id | pubmed-8015817 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
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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