Machine-learning-based prediction of disability progression in multiple sclerosis: an observational, international, multi-center study
Authors: De Brouwer Edward , Becker Thijs , Werthen-Brabants Lorin , Dewulf Pieter , Iliadis Dimitrios , Dekeyser Cathérine , Laureys Guy , Van Wijmeersch Bart , Popescu Veronica , Dhaene Tom , Deschrijver Dirk , Waegeman Willem , De Baets Bernard , Stock Michiel , Horakova Dana , Patti Francesco , Izquierdo Guillermo , Eichau Sara , Girard Marc , Prat Alexandre , Lugaresi Alessandra , Grammond Pierre , Kalincik Tomas , Alroughani Raed , Grand’Maison Francois , Skibina Olga , Terzi Murat , Lechner-Scott Jeannette , Gerlach Oliver , Khoury Samia J. , Cartechini Elisabetta , Van Pesch Vincent , Sa Maria Jose , Weinstock-Guttman Bianca , Blanco Yolanda , Ampapa Radek , Spitaleri Daniele , Solaro Claudio , Maimone Davide , Soysal Aysun , Iuliano Gerardo , Gouider Riadh , Castillo-Triviño Tamara , Sanchez-Menoyo Jose Luis , Laureys Guy , van der Walt Anneke , Oh Jiwon , Aguera-Morales Eduardo , Altintas Ayse , Al-Asmi Abdullah , de Gans Koen , Fragoso Yara , Csepany Tunde , Hodgkinson Suzanne , Deri Norma , Al-Harbi Talal , Taylor Bruce , Gray Orla , Lalive Patrice , Rozsa Csilla , McGuigan Chris , Kermode Allan , Perez sempere Angel , Mihaela Simu , Simo Magdolna , Hardy Todd , Decoo Danny , Hughes Stella , Grigoriadis Nikolaos , Sas Attila , Vella Norbert , Moreau Yves , Peeters Liesbet 
Published: September 2022

Background

Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking.

Methods

Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. TRIPOD guidelines were followed.

Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expended disability status scale, treatment, relapse information, and MS course.

To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated on their area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error.

Findings

A temporal attention model was the best model. It achieved a ROC-AUC of 0·71 ± 0·01, an AUC-PR of 0·26 ± 0·02, a Brier score of 0·1 ± 0·01 and an expected calibration error of 0·07 ± 0·04. The history of disability progression is more predictive for future disability progression than the treatment or relapses.

Interpretation

Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This makes these models ready for a clinical impact study. All our preprocessing and model code is available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS.