Development, validation and clinical usefulness of a prognostic model for relapse in relapsing-remitting multiple sclerosis

TL;DR

This study developed and validated a prognostic model for predicting relapses in relapsing-remitting multiple sclerosis, providing a web tool for personalized disease management and clinical decision support.

Contribution

It presents a new, methodologically sound prognostic model with clinical validation and a web application for individualized relapse risk prediction in RRMS.

Findings

Model has a c-statistic of 0.65 indicating moderate discrimination.

Calibration slope of 0.92 suggests good calibration.

Model shows potential clinical benefit within 15-30% relapse risk threshold.

Abstract

Prognosis on 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 in 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. Finally, we evaluated the potential clinical benefit of the developed prognostic model using decision curve analysis. 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), and gadolinium enhanced lesions. We also developed a web application where the personalized probabilities to relapse within two years are calculated automatically. The optimism-corrected c-statistic is 0.65 and the optimism-corrected calibration slope was 0.92. The model appears to be clinically useful between the range 15% and 30% of the threshold probability to relapse. 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 to relapse within two years and to inform the management of their disease.