Cargando…

Potential Fluid Biomarkers and a Prediction Model for Better Recognition Between Multiple System Atrophy-Cerebellar Type and Spinocerebellar Ataxia

OBJECTIVE: This study screened potential fluid biomarkers and developed a prediction model based on the easily obtained information at initial inspection to identify ataxia patients more likely to have multiple system atrophy-cerebellar type (MSA-C). METHODS: We established a retrospective cohort wi...

Descripción completa

Detalles Bibliográficos
Autores principales: Guo, Shuo, Zhao, Bi, An, Yunfei, Zhang, Yu, Meng, Zirui, Zhou, Yanbing, Zheng, Mingxue, Yang, Dan, Wang, Minjin, Ying, Binwu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8093568/
https://www.ncbi.nlm.nih.gov/pubmed/33958996
http://dx.doi.org/10.3389/fnagi.2021.644699
_version_ 1783687837217980416
author Guo, Shuo
Zhao, Bi
An, Yunfei
Zhang, Yu
Meng, Zirui
Zhou, Yanbing
Zheng, Mingxue
Yang, Dan
Wang, Minjin
Ying, Binwu
author_facet Guo, Shuo
Zhao, Bi
An, Yunfei
Zhang, Yu
Meng, Zirui
Zhou, Yanbing
Zheng, Mingxue
Yang, Dan
Wang, Minjin
Ying, Binwu
author_sort Guo, Shuo
collection PubMed
description OBJECTIVE: This study screened potential fluid biomarkers and developed a prediction model based on the easily obtained information at initial inspection to identify ataxia patients more likely to have multiple system atrophy-cerebellar type (MSA-C). METHODS: We established a retrospective cohort with 125 ataxia patients from southwest China between April 2018 and June 2020. Demographic and laboratory variables obtained at the time of hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression to construct a diagnosis score. The receiver operating characteristic (ROC) and decision curve analyses were performed to assess the accuracy and net benefit of the model. Also, independent validation using 25 additional ataxia patients was carried out to verify the model efficiency. Then the model was translated into a visual and operable web application using the R studio and Shiny package. RESULTS: From 47 indicators, five variables were selected and integrated into the prediction model, including the age of onset (AO), direct bilirubin (DBIL), aspartate aminotransferase (AST), eGFR, and synuclein-alpha. The prediction model exhibited an area under the curve (AUC) of 0.929 for the training cohort and an AUC of 0.917 for the testing cohort. The decision curve analysis (DCA) plot displayed a good net benefit for this model, and external validation confirmed its reliability. The model also was translated into a web application that is freely available to the public. CONCLUSION: The prediction model that was developed based on laboratory and demographic variables obtained from ataxia patients at admission to the hospital might help improve the ability to differentiate MSA-C from spinocerebellar ataxia clinically.
format Online
Article
Text
id pubmed-8093568
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-80935682021-05-05 Potential Fluid Biomarkers and a Prediction Model for Better Recognition Between Multiple System Atrophy-Cerebellar Type and Spinocerebellar Ataxia Guo, Shuo Zhao, Bi An, Yunfei Zhang, Yu Meng, Zirui Zhou, Yanbing Zheng, Mingxue Yang, Dan Wang, Minjin Ying, Binwu Front Aging Neurosci Neuroscience OBJECTIVE: This study screened potential fluid biomarkers and developed a prediction model based on the easily obtained information at initial inspection to identify ataxia patients more likely to have multiple system atrophy-cerebellar type (MSA-C). METHODS: We established a retrospective cohort with 125 ataxia patients from southwest China between April 2018 and June 2020. Demographic and laboratory variables obtained at the time of hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression to construct a diagnosis score. The receiver operating characteristic (ROC) and decision curve analyses were performed to assess the accuracy and net benefit of the model. Also, independent validation using 25 additional ataxia patients was carried out to verify the model efficiency. Then the model was translated into a visual and operable web application using the R studio and Shiny package. RESULTS: From 47 indicators, five variables were selected and integrated into the prediction model, including the age of onset (AO), direct bilirubin (DBIL), aspartate aminotransferase (AST), eGFR, and synuclein-alpha. The prediction model exhibited an area under the curve (AUC) of 0.929 for the training cohort and an AUC of 0.917 for the testing cohort. The decision curve analysis (DCA) plot displayed a good net benefit for this model, and external validation confirmed its reliability. The model also was translated into a web application that is freely available to the public. CONCLUSION: The prediction model that was developed based on laboratory and demographic variables obtained from ataxia patients at admission to the hospital might help improve the ability to differentiate MSA-C from spinocerebellar ataxia clinically. Frontiers Media S.A. 2021-04-20 /pmc/articles/PMC8093568/ /pubmed/33958996 http://dx.doi.org/10.3389/fnagi.2021.644699 Text en Copyright © 2021 Guo, Zhao, An, Zhang, Meng, Zhou, Zheng, Yang, Wang and Ying. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Guo, Shuo
Zhao, Bi
An, Yunfei
Zhang, Yu
Meng, Zirui
Zhou, Yanbing
Zheng, Mingxue
Yang, Dan
Wang, Minjin
Ying, Binwu
Potential Fluid Biomarkers and a Prediction Model for Better Recognition Between Multiple System Atrophy-Cerebellar Type and Spinocerebellar Ataxia
title Potential Fluid Biomarkers and a Prediction Model for Better Recognition Between Multiple System Atrophy-Cerebellar Type and Spinocerebellar Ataxia
title_full Potential Fluid Biomarkers and a Prediction Model for Better Recognition Between Multiple System Atrophy-Cerebellar Type and Spinocerebellar Ataxia
title_fullStr Potential Fluid Biomarkers and a Prediction Model for Better Recognition Between Multiple System Atrophy-Cerebellar Type and Spinocerebellar Ataxia
title_full_unstemmed Potential Fluid Biomarkers and a Prediction Model for Better Recognition Between Multiple System Atrophy-Cerebellar Type and Spinocerebellar Ataxia
title_short Potential Fluid Biomarkers and a Prediction Model for Better Recognition Between Multiple System Atrophy-Cerebellar Type and Spinocerebellar Ataxia
title_sort potential fluid biomarkers and a prediction model for better recognition between multiple system atrophy-cerebellar type and spinocerebellar ataxia
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8093568/
https://www.ncbi.nlm.nih.gov/pubmed/33958996
http://dx.doi.org/10.3389/fnagi.2021.644699
work_keys_str_mv AT guoshuo potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia
AT zhaobi potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia
AT anyunfei potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia
AT zhangyu potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia
AT mengzirui potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia
AT zhouyanbing potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia
AT zhengmingxue potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia
AT yangdan potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia
AT wangminjin potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia
AT yingbinwu potentialfluidbiomarkersandapredictionmodelforbetterrecognitionbetweenmultiplesystematrophycerebellartypeandspinocerebellarataxia