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Predicting disease severity in multiple sclerosis using multimodal data and machine learning.

Magi Andorra¹, Ana Freire^2,3, Irati Zubizarreta¹

¹Institut d'Investigacions Biomediques August Pi Sunyer (IDIBAPS) and Hospital Clinic Barcelona, Barcelona, Spain.

Journal of Neurology

|December 22, 2023

Summary

This summary is machine-generated.

Machine learning models integrating clinical, imaging, and omics data can predict multiple sclerosis (MS) disease activity. These algorithms help identify patients at risk of disability worsening, improving personalized treatment strategies.

Keywords:

Imaging Machine learning Multiple sclerosis Omics Precision medicine

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Area of Science:

Neurology
Biomedical Informatics
Data Science

Background:

Multiple sclerosis (MS) patient management can be improved by machine learning (ML) algorithms.
Integrating clinical, imaging, and multimodal biomarkers is crucial for defining MS disease activity risk.

Purpose of the Study:

To develop and validate ML algorithms for predicting clinical outcomes in MS patients.
To assess the predictive performance of algorithms using various data modalities.

Main Methods:

Analysis of a prospective multi-centric cohort of 322 MS patients and 98 controls.
Collection of disability scales, brain MRI, optical coherence tomography, genotyping, cytomics, and phosphoproteomic data.
Application of Random Forest algorithms to identify predictors of clinical outcomes, validated in an independent cohort.

Main Results:

Algorithms accurately predicted confirmed disability accumulation, no evidence of disease activity (NEDA), immunotherapy onset, and therapy escalation.
High accuracy was achieved using clinical and imaging data, with omics data offering slight improvements in some cases.
Algorithm performance was consistent across independent validation cohorts.

Conclusions:

Combining clinical, imaging, and omics data with ML effectively identifies MS patients at risk of disability worsening.
This approach aids in personalized risk stratification and treatment decisions for multiple sclerosis.