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A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data
BACKGROUND: Irreversible neurological dysfunction (IND) is an adverse event after cervical spinal cord injury (CSCI). However, there is still a shortage of objective criteria for the early prediction of neurological function. We aimed to screen independent predictors of IND and use these findings to...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240743/ https://www.ncbi.nlm.nih.gov/pubmed/37277760 http://dx.doi.org/10.1186/s12891-023-06570-z |
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author | Chen, Si Li, Guangzhou Li, Feng Wang, Gaoju Wang, Qing |
author_facet | Chen, Si Li, Guangzhou Li, Feng Wang, Gaoju Wang, Qing |
author_sort | Chen, Si |
collection | PubMed |
description | BACKGROUND: Irreversible neurological dysfunction (IND) is an adverse event after cervical spinal cord injury (CSCI). However, there is still a shortage of objective criteria for the early prediction of neurological function. We aimed to screen independent predictors of IND and use these findings to construct a nomogram that could predict the development of neurological function in CSCI patients. METHODS: Patients with CSCI attending the Affiliated Hospital of Southwest Medical University between January 2014 and March 2021 were included in this study. We divided the patients into two groups: reversible neurological dysfunction (RND) and IND. The independent predictors of IND in CSCI patients were screened using the regularization technique to construct a nomogram, which was finally converted into an online calculator. Concordance index (C-index), calibration curves analysis and decision curve analysis (DCA) evaluated the model's discrimination, calibration, and clinical applicability. We tested the nomogram in an external validation cohort and performed internal validation using the bootstrap method. RESULTS: We enrolled 193 individuals with CSCI in this study, including IND (n = 75) and RND (n = 118). Six features, including age, American spinal injury association Impairment Scale (AIS) grade, signal of spinal cord (SC), maximum canal compromise (MCC), intramedullary lesion length (IMLL), and specialized institution-based rehabilitation (SIBR), were included in the model. The C-index of 0.882 from the training set and its externally validated value of 0.827 demonstrated the model's prediction accuracy. Meanwhile, the model has satisfactory actual consistency and clinical applicability, verified in the calibration curve and DCA. CONCLUSION: We constructed a prediction model based on six clinical and MRI features that can be used to assess the probability of developing IND in patients with CSCI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-023-06570-z. |
format | Online Article Text |
id | pubmed-10240743 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-102407432023-06-06 A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data Chen, Si Li, Guangzhou Li, Feng Wang, Gaoju Wang, Qing BMC Musculoskelet Disord Research BACKGROUND: Irreversible neurological dysfunction (IND) is an adverse event after cervical spinal cord injury (CSCI). However, there is still a shortage of objective criteria for the early prediction of neurological function. We aimed to screen independent predictors of IND and use these findings to construct a nomogram that could predict the development of neurological function in CSCI patients. METHODS: Patients with CSCI attending the Affiliated Hospital of Southwest Medical University between January 2014 and March 2021 were included in this study. We divided the patients into two groups: reversible neurological dysfunction (RND) and IND. The independent predictors of IND in CSCI patients were screened using the regularization technique to construct a nomogram, which was finally converted into an online calculator. Concordance index (C-index), calibration curves analysis and decision curve analysis (DCA) evaluated the model's discrimination, calibration, and clinical applicability. We tested the nomogram in an external validation cohort and performed internal validation using the bootstrap method. RESULTS: We enrolled 193 individuals with CSCI in this study, including IND (n = 75) and RND (n = 118). Six features, including age, American spinal injury association Impairment Scale (AIS) grade, signal of spinal cord (SC), maximum canal compromise (MCC), intramedullary lesion length (IMLL), and specialized institution-based rehabilitation (SIBR), were included in the model. The C-index of 0.882 from the training set and its externally validated value of 0.827 demonstrated the model's prediction accuracy. Meanwhile, the model has satisfactory actual consistency and clinical applicability, verified in the calibration curve and DCA. CONCLUSION: We constructed a prediction model based on six clinical and MRI features that can be used to assess the probability of developing IND in patients with CSCI. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12891-023-06570-z. BioMed Central 2023-06-05 /pmc/articles/PMC10240743/ /pubmed/37277760 http://dx.doi.org/10.1186/s12891-023-06570-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Chen, Si Li, Guangzhou Li, Feng Wang, Gaoju Wang, Qing A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data |
title | A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data |
title_full | A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data |
title_fullStr | A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data |
title_full_unstemmed | A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data |
title_short | A dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and MRI data |
title_sort | dynamic nomogram for predicting the probability of irreversible neurological dysfunction after cervical spinal cord injury: research based on clinical features and mri data |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10240743/ https://www.ncbi.nlm.nih.gov/pubmed/37277760 http://dx.doi.org/10.1186/s12891-023-06570-z |
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