LAUSR

OBJECTIVE To identify predictors in common between different clinical and MRI outcomes in multiple sclerosis (MS) by comparing predictive models. METHODS We analyzed 704 patients from our center seen at MS onset, measuring 37 baseline demographic, clinical, treatment, and MRI predictors, and 10-year outcomes. Our primary aim was identifying predictors in common between clinical outcomes: aggressive MS, benign MS, and secondary-progressive (SP)MS. We also investigated MRI outcomes: T2 lesion volume (T2LV) and brain parenchymal fraction (BPF). The performance of the full 37-predictor model was compared with a LASSO-selected model of predictors in common between each outcome by the area under the receiver operating characteristic curves (AUC). RESULTS The full 37-predictor model was highly predictive of clinical outcomes: in-sample AUC was 0.91 for aggressive MS, 0.81 for benign MS, and 0.81 for SPMS. After variable selection, 10 LASSO-selected predictors were in common between each clinical outcome: age, Expanded Disability Status Scale, pyramidal, cerebellar, sensory and bowel/bladder signs, timed 25-foot walk ≥6s, poor attack recovery, no sensory attacks, and time-to-treatment. This reduced model had comparable cross-validation AUC as the full 37-predictor model: 0.84 vs. 0.81 for aggressive MS, 0.75 vs. 0.73 for benign MS, and 0.76 vs. 0.75 for SPMS, respectively. In contrast, 10-year MRI outcomes were more strongly influenced by initial T2LV and BPF than clinical outcomes. INTERPRETATION Early prognostication of MS is possible using LASSO modeling to identify a limited set of accessible clinical features. These predictive models can be clinically useable in treatment decision-making once implemented into web-based calculators. This article is protected by copyright. All rights reserved.