@article{01J5302CQG6N2K3AT6H4R1WDP5,
  abstract     = {{Background : Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability 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. Transparent Reporting for Individual Prognosis Or Diagnosis (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 expanded 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 with the 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. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS.

Findings : Machine learning models 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 was identified as being more predictive for future disability progression than the treatment or relapses history.

Conclusions : Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.}},
  articleno    = {{e0000533}},
  author       = {{De Brouwer, Edward and Becker, Thijs and Werthen-Brabants, Lorin and Dewulf, Pieter and Iliadis, Dimitrios and Dekeyser, Cathérine and Laureys, Guy and Van Wijmeersch, Bart and Popescu, Veronica and Dhaene, Tom and Deschrijver, Dirk and Waegeman, Willem and De Baets, Bernard and Stock, Michiel and Horakova, Dana and Patti, Francesco and Izquierdo, Guillermo and Eichau, Sara and Girard, Marc and Prat, Alexandre and Lugaresi, Alessandra and Grammond, Pierre and Kalincik, Tomas and Alroughani, Raed and Grand’Maison, Francois and Skibina, Olga and Terzi, Murat and Lechner-Scott, Jeannette and Gerlach, Oliver and Khoury, Samia J. and Cartechini, Elisabetta and Van Pesch, Vincent and Sà, Maria José and Weinstock-Guttman, Bianca and Blanco, Yolanda and Ampapa, Radek and Spitaleri, Daniele and Solaro, Claudio and Maimone, Davide and Soysal, Aysun and Iuliano, Gerardo and Gouider, Riadh and Castillo-Triviño, Tamara and Sánchez-Menoyo, José Luis and van der Walt, Anneke and Oh, Jiwon and Aguera-Morales, Eduardo and Altintas, Ayse and Al-Asmi, Abdullah and de Gans, Koen and Fragoso, Yara and Csepany, Tunde and Hodgkinson, Suzanne and Deri, Norma and Al-Harbi, Talal and Taylor, Bruce and Gray, Orla and Lalive, Patrice and Rozsa, Csilla and McGuigan, Chris and Kermode, Allan and Sempere, Angel Pérez and Mihaela, Simu and Simo, Magdolna and Hardy, Todd and Decoo, Danny and Hughes, Stella and Grigoriadis, Nikolaos and Sas, Attila and Vella, Norbert and Moreau, Pierre-Yves and Peeters, Liesbeth}},
  issn         = {{2767-3170}},
  journal      = {{PLOS DIGITAL HEALTH}},
  keywords     = {{PROGNOSTIC-FACTORS,MODEL}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{25}},
  title        = {{Machine-learning-based prediction of disability progression in multiple sclerosis : an observational, international, multi-center study}},
  url          = {{http://doi.org/10.1371/journal.pdig.0000533}},
  volume       = {{3}},
  year         = {{2024}},
}

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