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. 2022 Jul;92(1):87-96.
doi: 10.1002/ana.26370. Epub 2022 May 4.

Early Predictors of Clinical and MRI Outcomes Using Least Absolute Shrinkage and Selection Operator (LASSO) in Multiple Sclerosis

Gauruv Bose  1   2 , Brian C Healy  1   2 , Hrishikesh A Lokhande  2 , Marinos G Sotiropoulos  1   2 , Mariann Polgar-Turcsanyi  1   2 , Mark Anderson  2 , Bonnie I Glanz  1   2 , Charles R G Guttman  1   3 , Rohit Bakshi  1   2 , Howard L Weiner  1   2 , Tanuja Chitnis  1   2
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Early Predictors of Clinical and MRI Outcomes Using Least Absolute Shrinkage and Selection Operator (LASSO) in Multiple Sclerosis

Gauruv Bose et al. Ann Neurol. 2022 Jul.

Abstract

Objective: The objective of this study was to identify predictors in common between different clinical and magnetic resonance imaging (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 among 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 least absolute shrinkage and selection operator (LASSO)-selected model of predictors in common between each outcome by the area under the receiver operating characteristic curves (AUCs).

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 ≥6 seconds, 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 versus 0.81 for aggressive MS, 0.75 versus 0.73 for benign MS, and 0.76 versus 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 usable in treatment decision making once implemented into web-based calculators. ANN NEUROL 2022;92:87-96.

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References

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