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Prediction of Disability in Multiple System Atrophy Based on Machine Learning Algorithm: A Prospective Cohort Study
21 Pages Posted: 31 Mar 2022
More...Abstract
Background: The predictive factors for disability in patients with multiple system atrophy (MSA) are unclear. We aimed to explore the predictive factors for disability in patients with MSA focusing on clinical features and blood biomarkers.
Methods: This prospective cohort study included patients diagnosed with MSA between January 2014 and December 2019. At the deadline of October 2021, patients met the diagnosis of probable MSA were included in the analysis. Random forest (RF) was used to establish a predictive model for disability. Accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the model.
Findings: Altogether, 100 patients with MSA including 49 with disability and 51 without disability were enrolled in the RF model. Baseline plasma neurofilament light chain (NFL) levels were higher in patients with disability than in those without disability ( P =0.037). According to the Gini index, the five major predictive factors were disease duration, age of onset, Unified MSA Rating Scale (UMSARS)-II score, NFL, and UMSARS-I score, followed by C-reactive protein (CRP) levels, neutrophil-to-lymphocyte ratio (NLR), UMSARS-IV score, symptom onset, orthostatic hypotension, sex, urinary incontinence, and diagnosis subtype. The sensitivity, specificity, accuracy, and AUC of the RF model were 70.82%, 74.55%, 72.29%, and 0.72, respectively.
Interpretation: Besides clinical features, baseline features including NFL, CRP, and NLR were potential predictive biomarkers of disability in MSA. These findings provide new insights into the trials regarding early intervention in MSA.
Funding: The present study was supported by the funding of 1.3.5 project for disciplines of excellence, West China Hospital, Sichuan University (Grant No. ZYJC18038), 1.3.5 project for disciplines of excellence–Clinical Research Incubation Project, West China Hospital, Sichuan University (Grant No. 2019HXFH016), and Sichuan Science and Technology Program (Grant No. 2022ZDZX0023).
Declaration of Interest: The authors declare that they have no competing interests.
Ethical Approval: Approval was obtained from the Ethics Committee of West China Hospital of Sichuan University. Informed consent was obtained from all participates.
Keywords: multiple system atrophy, disability, random forest, machine learning algorithm
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