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Autor
Aguera-Morales, Eduardo 1 Al-Asmi, Abdullah 1 Al-Harbi, Talal 1 Alroughani, Raed 1 Altintas, Ayse 1 Ampapa, Radek 1 Becker, Thijs 1 Blanco, Yolanda 1 Cartechini, Elisabetta 1 Castillo-Triviño, Tamara 1 Csepany, Tunde 1 De Baets, Bernard 1 De Brouwer, Edward 1 Decoo, Danny 1 Dekeyser, Cathérine 1 Deri, Norma 1 Deschrijver, Dirk 1 Dewulf, Pieter 1 Dhaene, Tom 1 Eichau, Sara 1
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Pracoviště
1 Biostat Hasselt University Belgium 1 AHEPA University Hospital Thessalonik... 1 AZ Alma Ziekenhuis Sijsele Damme Belgium 1 Academic MS Center Zuyderland Departm... 1 American University of Beirut Medical... 1 Amiri Hospital Sharq Kuwait 1 Azienda Ospedaliera di Rilievo Nazion... 1 Azienda Sanitaria Unica Regionale Mar... 1 BAZ County Hospital Miskolc Hungary 1 Bakirkoy Education and Research Hospi... 1 Biobix Department of Data Analysis an... 1 Biomedical Research Institute Hasselt... 1 Box Hill Hospital Melbourne Australia 1 Brain Ghent University Belgium 1 CHUM and Université de Montreal Montr... 1 CISSS Chaudière Appalache Levis Canada 1 CORe Department of Medicine Universit... 1 Centro Hospitalar Universitario de Sa... 1 Charles University Prague and General... 1 Cliniques Universitaires Saint Luc Br... 1
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Autor
Aguera-Morales, Eduardo 1 Al-Asmi, Abdullah 1 Al-Harbi, Talal 1 Alroughani, Raed 1 Altintas, Ayse 1 Ampapa, Radek 1 Becker, Thijs 1 Blanco, Yolanda 1 Cartechini, Elisabetta 1 Castillo-Triviño, Tamara 1 Csepany, Tunde 1 De Baets, Bernard 1 De Brouwer, Edward 1 Decoo, Danny 1 Dekeyser, Cathérine 1 Deri, Norma 1 Deschrijver, Dirk 1 Dewulf, Pieter 1 Dhaene, Tom 1 Eichau, Sara 1
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Pracoviště
1 Biostat Hasselt University Belgium 1 AHEPA University Hospital Thessalonik... 1 AZ Alma Ziekenhuis Sijsele Damme Belgium 1 Academic MS Center Zuyderland Departm... 1 American University of Beirut Medical... 1 Amiri Hospital Sharq Kuwait 1 Azienda Ospedaliera di Rilievo Nazion... 1 Azienda Sanitaria Unica Regionale Mar... 1 BAZ County Hospital Miskolc Hungary 1 Bakirkoy Education and Research Hospi... 1 Biobix Department of Data Analysis an... 1 Biomedical Research Institute Hasselt... 1 Box Hill Hospital Melbourne Australia 1 Brain Ghent University Belgium 1 CHUM and Université de Montreal Montr... 1 CISSS Chaudière Appalache Levis Canada 1 CORe Department of Medicine Universit... 1 Centro Hospitalar Universitario de Sa... 1 Charles University Prague and General... 1 Cliniques Universitaires Saint Luc Br... 1
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PubMed
39052668
PubMed Central
PMC11271865
DOI
10.1371/journal.pdig.0000533
PII: PDIG-D-23-00247
Knihovny.cz E-zdroje
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.
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Po ukončení testovacího provozu bude odkaz přesměrován adresu produkční verze portálu Medvik.