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Abstract #4873

Gray Matter-Based Models Predict No Evidence of Disease Activity Status in Patients with Relapsing-Remitting Multiple Sclerosis

Zichun Yan1, Bing Lin2, Xiaolin Yang3, Qiyuan Zhu1, Zhuowei Shi1, Jinzhou Feng3, and Yongmei Li1
1Department of Radiology, the First Affiliated Hospital of Chognqing Medical University, Chongqing, China, 2College of Public Health, Chongqing Medical University, Chongqing, China, 3Department of Neurology, the First Affiliated Hospital of Chognqing Medical University, Chongqing, China

Synopsis

Keywords: Multiple Sclerosis, Neuroscience

Motivation: Prediction of achieving no evidence of disease activity (NEDA) status in relapsing-remitting multiple sclerosis (RRMS) patients on oral disease-modifying therapies (DMTs) is crucial for clinical decision-making and patient management.

Goal(s): To develop a clinical and regional gray matter atrophy variables-based machine learning model to predict achieving NEDA status in RRMS patients on oral DMTs.

Approach: Composite performance scores were calculated based on eight standard performance metrics ranking to screen out the optimal model among five models, and risk stratification analysis was performed.

Results: The logistic regression (LR) model demonstrated the best performance and can distinguish well between high- and low-risk RRMS individuals.

Impact: Baseline clinical and regional gray matter atrophy variables-based machine learning models can help physicians predict NEDA status in RRMS patients on oral DMTs for clinical decision-making and patient management.

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