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Can a computer tell patients how their multiple sclerosis will progress?

Date:
July 25, 2024
Source:
PLOS
Summary:
Machine learning models can reliably inform clinicians about the disability progression of multiple sclerosis, according to a new study published this week in the open-access journal PLOS Digital Health by Edward De Brouwer of KU Leuven, Belgium, and colleagues.
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Machine learning models can reliably inform clinicians about the disability progression of multiple sclerosis, according to a new study published this week in the open-access journal PLOS Digital Health by Edward De Brouwer of KU Leuven, Belgium, and colleagues.

Multiple sclerosis (MS) is a chronic progressive autoimmune disease that leads to severe disability over time through a complex pattern of progression, recovery, and relapse. Its global prevalence has increased by more than 30% over the last decade. Yet there are few tools that can predict the progression of MS to help clinicians and patients make life planning and treatment decision-making.

In the new study, De Brouwer and colleagues used data on 15,240 adults with at least three years of MS history who were being treated at 146 MS centers in 40 countries. Data on two years of each patient's disease progression was used to train state-of-the-art machine learning models to predict the probability of disease progression over the subsequent months and years. The models were trained and validated using strict clinical guidelines, promoting applicability of the models in clinical practice. While individual models varied in performance across different patient subgroups, the models had an average area under the ROC curve (ROC-AUC) of 0.71 ± 0.01. The study found that the history of disability progression was more predictive for future disability progression than treatment or relapse history.

The authors conclude that the models developed in the study have the potential to greatly enhance planning for individuals with MS and could be evaluated in a clinical impact study.

De Brouwer adds: "Using the clinical history of more than 15,000 people with multiple sclerosis, we trained a machine learning model capable of reliably predicting the probability of disability progression in the next two years. The model only uses routinely collected clinical variables, which makes it widely applicable. Our rigorous benchmarking and external validation support the vast potential of machine learning models for helping patients planning their lives and clinicians optimizing treatment strategies."


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Journal Reference:

  1. Edward De Brouwer, Thijs Becker, Lorin Werthen-Brabants, Pieter Dewulf, Dimitrios Iliadis, Cathérine Dekeyser, Guy Laureys, Bart Van Wijmeersch, Veronica Popescu, Tom Dhaene, Dirk Deschrijver, Willem Waegeman, Bernard De Baets, Michiel Stock, Dana Horakova, Francesco Patti, Guillermo Izquierdo, Sara Eichau, Marc Girard, Alexandre Prat, Alessandra Lugaresi, Pierre Grammond, Tomas Kalincik, Raed Alroughani, Francois Grand’Maison, Olga Skibina, Murat Terzi, Jeannette Lechner-Scott, Oliver Gerlach, Samia J. Khoury, Elisabetta Cartechini, Vincent Van Pesch, Maria José Sà, Bianca Weinstock-Guttman, Yolanda Blanco, Radek Ampapa, Daniele Spitaleri, Claudio Solaro, Davide Maimone, Aysun Soysal, Gerardo Iuliano, Riadh Gouider, Tamara Castillo-Triviño, José Luis Sánchez-Menoyo, Guy Laureys, Anneke van der Walt, Jiwon Oh, Eduardo Aguera-Morales, Ayse Altintas, Abdullah Al-Asmi, Koen de Gans, Yara Fragoso, Tunde Csepany, Suzanne Hodgkinson, Norma Deri, Talal Al-Harbi, Bruce Taylor, Orla Gray, Patrice Lalive, Csilla Rozsa, Chris McGuigan, Allan Kermode, Angel Pérez Sempere, Simu Mihaela, Magdolna Simo, Todd Hardy, Danny Decoo, Stella Hughes, Nikolaos Grigoriadis, Attila Sas, Norbert Vella, Yves Moreau, Liesbet Peeters. Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study. PLOS Digital Health, 2024; 3 (7): e0000533 DOI: 10.1371/journal.pdig.0000533

Cite This Page:

PLOS. "Can a computer tell patients how their multiple sclerosis will progress?." ScienceDaily. ScienceDaily, 25 July 2024. <www.sciencedaily.com/releases/2024/07/240725154636.htm>.
PLOS. (2024, July 25). Can a computer tell patients how their multiple sclerosis will progress?. ScienceDaily. Retrieved December 21, 2024 from www.sciencedaily.com/releases/2024/07/240725154636.htm
PLOS. "Can a computer tell patients how their multiple sclerosis will progress?." ScienceDaily. www.sciencedaily.com/releases/2024/07/240725154636.htm (accessed December 21, 2024).

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