AUTHOR=Guo Shuo , Zhao Bi , An Yunfei , Zhang Yu , Meng Zirui , Zhou Yanbing , Zheng Mingxue , Yang Dan , Wang Minjin , Ying Binwu 

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

JOURNAL=Frontiers in Aging Neuroscience

VOLUME=Volume 13 - 2021

YEAR=2021

URL=https://www.frontiersin.org/journals/aging-neuroscience/articles/10.3389/fnagi.2021.644699

DOI=10.3389/fnagi.2021.644699

ISSN=1663-4365

ABSTRACT=OBJECTIVE: To screened potential liquid biomarkers and develop a prediction model based on clinical and laboratory variables for identification of the ataxia patients who are more likely to be MSA-C.
METHODS: We established a retrospective cohort with 125 ataxia patients in southwest China between April 2018 to June 2020. Epidemiological, clinical and laboratory variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator regression and logistic regression to construct a diagnosis score. The receiver operating characteristic analysis and decision curve analysis were performed to assess the accuracy and net benefit of the model. Besides, an independent validation of 25 extra ataxia patients was also carried out to verify the model efficiency. The model was then translated into a visual and operable Web application by R studio and Shiny package.
RESULTS: From a total of 47 indicators, 5 variables were selected out and integrated into the prediction model including age of onset, direct bilirubin, aspartate aminotransferase, eGFR and synuclein-alpha. The prediction model showed an AUC of 0.929 in training cohort and AUC of 0.917 in the testing cohort. The DCA plot displayed a good net benefit for this model and external validation also confirmed its reliability as well. The model has been further translated into a Web that is freely available to the public.
CONCLUSIONS: The prediction model developed based on clinical and laboratory variables of ataxia patients at the time of admission to the hospital may help to improve the ability of clinical identification of MSA-C.