Cargando…

Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy

Background: Diabetic peripheral neuropathy (DPN) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of DPN is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DPN. Methods: 3012 patients with...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Yongsheng, Li, Yongnan, Deng, Ning, Shi, Haonan, Caika, Siqingaowa, Sen, Gan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093299/
https://www.ncbi.nlm.nih.gov/pubmed/37046484
http://dx.doi.org/10.3390/diagnostics13071265
_version_ 1785023552411402240
author Li, Yongsheng
Li, Yongnan
Deng, Ning
Shi, Haonan
Caika, Siqingaowa
Sen, Gan
author_facet Li, Yongsheng
Li, Yongnan
Deng, Ning
Shi, Haonan
Caika, Siqingaowa
Sen, Gan
author_sort Li, Yongsheng
collection PubMed
description Background: Diabetic peripheral neuropathy (DPN) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of DPN is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DPN. Methods: 3012 patients with T2DM were retrospectively studied. These patients were hospitalized between 1 January 2017 and 31 December 2020 in the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China. A total of 901 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China who were hospitalized between 1 January 2019 and 31 December 2020 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were performed to identify independent predictors and establish a nomogram to predict the occurrence of DPN. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and a decision curve analysis (DCA). Findings: Age, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), and fasting blood glucose (FBG) were used to establish a nomogram model for predicting the risk of DPN. In the training and validation cohorts, the areas under the curve of the nomogram constructed from the above six factors were 0.8256 (95% CI: 0.8104–0.8408) and 0.8608 (95% CI: 0.8376–0.8840), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. Interpretation: This study has developed and externally validated a nomogram model which exhibits good predictive ability in assessing DPN risk among the type 2 diabetes population. It provided clinicians with an accurate and effective tool for the early prediction and timely management of DPN.
format Online
Article
Text
id pubmed-10093299
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-100932992023-04-13 Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy Li, Yongsheng Li, Yongnan Deng, Ning Shi, Haonan Caika, Siqingaowa Sen, Gan Diagnostics (Basel) Article Background: Diabetic peripheral neuropathy (DPN) is a critical clinical disease with high disability and mortality rates. Early identification and treatment of DPN is critical. Our aim was to train and externally validate a prediction nomogram for early prediction of DPN. Methods: 3012 patients with T2DM were retrospectively studied. These patients were hospitalized between 1 January 2017 and 31 December 2020 in the First Affiliated Hospital of Xinjiang Medical University in Xinjiang, China. A total of 901 patients with T2DM from the Suzhou BenQ Hospital in Jiangsu, China who were hospitalized between 1 January 2019 and 31 December 2020 were considered for external validation. The least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were performed to identify independent predictors and establish a nomogram to predict the occurrence of DPN. The performance of the nomogram was evaluated using a receiver operating characteristic curve (ROC), a calibration curve, and a decision curve analysis (DCA). Findings: Age, 25-hydroxyvitamin D3 [25(OH)D3], Duration of T2DM, high-density lipoprotein (HDL), hemoglobin A1c (HbA1c), and fasting blood glucose (FBG) were used to establish a nomogram model for predicting the risk of DPN. In the training and validation cohorts, the areas under the curve of the nomogram constructed from the above six factors were 0.8256 (95% CI: 0.8104–0.8408) and 0.8608 (95% CI: 0.8376–0.8840), respectively. The nomogram demonstrated excellent performance in the calibration curve and DCA. Interpretation: This study has developed and externally validated a nomogram model which exhibits good predictive ability in assessing DPN risk among the type 2 diabetes population. It provided clinicians with an accurate and effective tool for the early prediction and timely management of DPN. MDPI 2023-03-27 /pmc/articles/PMC10093299/ /pubmed/37046484 http://dx.doi.org/10.3390/diagnostics13071265 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Li, Yongsheng
Li, Yongnan
Deng, Ning
Shi, Haonan
Caika, Siqingaowa
Sen, Gan
Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy
title Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy
title_full Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy
title_fullStr Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy
title_full_unstemmed Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy
title_short Training and External Validation of a Predict Nomogram for Type 2 Diabetic Peripheral Neuropathy
title_sort training and external validation of a predict nomogram for type 2 diabetic peripheral neuropathy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10093299/
https://www.ncbi.nlm.nih.gov/pubmed/37046484
http://dx.doi.org/10.3390/diagnostics13071265
work_keys_str_mv AT liyongsheng trainingandexternalvalidationofapredictnomogramfortype2diabeticperipheralneuropathy
AT liyongnan trainingandexternalvalidationofapredictnomogramfortype2diabeticperipheralneuropathy
AT dengning trainingandexternalvalidationofapredictnomogramfortype2diabeticperipheralneuropathy
AT shihaonan trainingandexternalvalidationofapredictnomogramfortype2diabeticperipheralneuropathy
AT caikasiqingaowa trainingandexternalvalidationofapredictnomogramfortype2diabeticperipheralneuropathy
AT sengan trainingandexternalvalidationofapredictnomogramfortype2diabeticperipheralneuropathy