Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis.
Clinical benefit
Clinical usefulness
Prognosis
Prognostic model
Relapsing-remitting multiple sclerosis
Journal
Diagnostic and prognostic research
ISSN: 2397-7523
Titre abrégé: Diagn Progn Res
Pays: England
ID NLM: 101718985
Informations de publication
Date de publication:
27 Oct 2021
27 Oct 2021
Historique:
received:
11
05
2021
accepted:
05
10
2021
entrez:
28
10
2021
pubmed:
29
10
2021
medline:
29
10
2021
Statut:
epublish
Résumé
Prognosis for the occurrence of relapses in individuals with relapsing-remitting multiple sclerosis (RRMS), the most common subtype of multiple sclerosis (MS), could support individualized decisions and disease management and could be helpful for efficiently selecting patients for future randomized clinical trials. There are only three previously published prognostic models on this, all of them with important methodological shortcomings. We aim to present the development, internal validation, and evaluation of the potential clinical benefit of a prognostic model for relapses for individuals with RRMS using real-world data. We followed seven steps to develop and validate the prognostic model: (1) selection of prognostic factors via a review of the literature, (2) development of a generalized linear mixed-effects model in a Bayesian framework, (3) examination of sample size efficiency, (4) shrinkage of the coefficients, (5) dealing with missing data using multiple imputations, (6) internal validation of the model. Finally, we evaluated the potential clinical benefit of the developed prognostic model using decision curve analysis. For the development and the validation of our prognostic model, we followed the TRIPOD statement. We selected eight baseline prognostic factors: age, sex, prior MS treatment, months since last relapse, disease duration, number of prior relapses, expanded disability status scale (EDSS) score, and number of gadolinium-enhanced lesions. We also developed a web application that calculates an individual's probability of relapsing within the next 2 years. The optimism-corrected c-statistic is 0.65 and the optimism-corrected calibration slope is 0.92. For threshold probabilities between 15 and 30%, the "treat based on the prognostic model" strategy leads to the highest net benefit and hence is considered the most clinically useful strategy. The prognostic model we developed offers several advantages in comparison to previously published prognostic models on RRMS. Importantly, we assessed the potential clinical benefit to better quantify the clinical impact of the model. Our web application, once externally validated in the future, could be used by patients and doctors to calculate the individualized probability of relapsing within 2 years and to inform the management of their disease.
Sections du résumé
BACKGROUND
BACKGROUND
Prognosis for the occurrence of relapses in individuals with relapsing-remitting multiple sclerosis (RRMS), the most common subtype of multiple sclerosis (MS), could support individualized decisions and disease management and could be helpful for efficiently selecting patients for future randomized clinical trials. There are only three previously published prognostic models on this, all of them with important methodological shortcomings.
OBJECTIVES
OBJECTIVE
We aim to present the development, internal validation, and evaluation of the potential clinical benefit of a prognostic model for relapses for individuals with RRMS using real-world data.
METHODS
METHODS
We followed seven steps to develop and validate the prognostic model: (1) selection of prognostic factors via a review of the literature, (2) development of a generalized linear mixed-effects model in a Bayesian framework, (3) examination of sample size efficiency, (4) shrinkage of the coefficients, (5) dealing with missing data using multiple imputations, (6) internal validation of the model. Finally, we evaluated the potential clinical benefit of the developed prognostic model using decision curve analysis. For the development and the validation of our prognostic model, we followed the TRIPOD statement.
RESULTS
RESULTS
We selected eight baseline prognostic factors: age, sex, prior MS treatment, months since last relapse, disease duration, number of prior relapses, expanded disability status scale (EDSS) score, and number of gadolinium-enhanced lesions. We also developed a web application that calculates an individual's probability of relapsing within the next 2 years. The optimism-corrected c-statistic is 0.65 and the optimism-corrected calibration slope is 0.92. For threshold probabilities between 15 and 30%, the "treat based on the prognostic model" strategy leads to the highest net benefit and hence is considered the most clinically useful strategy.
CONCLUSIONS
CONCLUSIONS
The prognostic model we developed offers several advantages in comparison to previously published prognostic models on RRMS. Importantly, we assessed the potential clinical benefit to better quantify the clinical impact of the model. Our web application, once externally validated in the future, could be used by patients and doctors to calculate the individualized probability of relapsing within 2 years and to inform the management of their disease.
Identifiants
pubmed: 34706759
doi: 10.1186/s41512-021-00106-6
pii: 10.1186/s41512-021-00106-6
pmc: PMC8549310
doi:
Types de publication
Journal Article
Langues
eng
Pagination
17Subventions
Organisme : European Union's Horizon 2020
ID : No 825162
Informations de copyright
© 2021. The Author(s).
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