ArticleDiagnostic and prognostic research2021
Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis.
Article in Diagnostic and prognostic research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 2 citations in OpenAlex.
- Relapse prediction and individualized treatment-effect modeling in relapsing multiple sclerosis: a systematic review.Frontiers in public health · 2026Pooled it
- External validation of a multiple sclerosis treatment decision score using data from the ProVal-MS cohort study.Therapeutic advances in neurological disorders · 2025Article
- Scoping review of clinical decision support systems for multiple sclerosis management: Leveraging information technology and massive health data.European journal of neurology · 2025Article
- Developing clinical prediction models: a step-by-step guide.BMJ (Clinical research ed.) · 2024Article
- Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study.PLOS digital health · 2024Article
- Retrospective cohort study to devise a treatment decision score predicting adverse 24-month radiological activity in early multiple sclerosis.Therapeutic advances in neurological disorders · 2023Article
Corrections and comments
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Authors and funding
11 authors at 5 institutions in 2 countries.
Funding
Abstract
backgroundPrognosis 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.
objectivesWe 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.
methodsWe 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.
resultsWe 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.
conclusionsThe 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.
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