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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.
Afiliación
  • Kamiya H; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Shibata Y; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Himeno T; Department of Laboratory, The Medical Clinic of Aichi Medical University, Nagoya, Japan.
  • Tani H; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Nakayama T; Department of Clinical Laboratory, Aichi Medical University Hospital, Nagakute, Japan.
  • Murotani K; Department of Clinical Laboratory, Aichi Medical University Hospital, Nagakute, Japan.
  • Hirai N; Biostatistics Center, Kurume University Graduate School of Medicine, Kurume, Japan.
  • Kawai M; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Asada-Yamada Y; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Asano-Hayami E; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Nakai-Shimoda H; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Yamada Y; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Ishikawa T; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Morishita Y; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Kondo M; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Tsunekawa S; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Kato Y; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Baba M; Division of Diabetes, Department of Internal Medicine, Aichi Medical University School of Medicine, Nagakute, Japan.
  • Nakamura J; Department of Neurology, Aomori Prefectural Central Hospital, Aomori, Japan.
J Diabetes Investig ; 12(4): 583-591, 2021 Apr.
Article en En | MEDLINE | ID: mdl-32799422
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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neuropatías Diabéticas / Pruebas en el Punto de Atención / Conducción Nerviosa Tipo de estudio: Clinical_trials / Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Diabetes Investig Año: 2021 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Japón
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neuropatías Diabéticas / Pruebas en el Punto de Atención / Conducción Nerviosa Tipo de estudio: Clinical_trials / Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Adult / Aged / Aged80 / Female / Humans / Male / Middle aged Idioma: En Revista: J Diabetes Investig Año: 2021 Tipo del documento: Article País de afiliación: Japón Pais de publicación: Japón