Disease phenotype prediction in multiple sclerosis.
Health technology
Neuroscience
Journal
iScience
ISSN: 2589-0042
Titre abrégé: iScience
Pays: United States
ID NLM: 101724038
Informations de publication
Date de publication:
16 Jun 2023
16 Jun 2023
Historique:
received:
23
11
2022
revised:
09
03
2023
accepted:
12
05
2023
medline:
19
6
2023
pubmed:
19
6
2023
entrez:
19
6
2023
Statut:
epublish
Résumé
Progressive multiple sclerosis (PMS) is currently diagnosed retrospectively. Here, we work toward a set of biomarkers that could assist in early diagnosis of PMS. A selection of cerebrospinal fluid metabolites (n = 15) was shown to differentiate between PMS and its preceding phenotype in an independent cohort (AUC = 0.93). Complementing the classifier with conformal prediction showed that highly confident predictions could be made, and that three out of eight patients developing PMS within three years of sample collection were predicted as PMS at that time point. Finally, this methodology was applied to PMS patients as part of a clinical trial for intrathecal treatment with rituximab. The methodology showed that 68% of the patients decreased their similarity to the PMS phenotype one year after treatment. In conclusion, the inclusion of confidence predictors contributes with more information compared to traditional machine learning, and this information is relevant for disease monitoring.
Identifiants
pubmed: 37332601
doi: 10.1016/j.isci.2023.106906
pii: S2589-0042(23)00983-5
pmc: PMC10275960
doi:
Types de publication
Journal Article
Langues
eng
Pagination
106906Informations de copyright
© 2023 The Authors.
Déclaration de conflit d'intérêts
The authors declare no competing interests.
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