Multiple Sclerosis Severity Score (MSSS) Improves the Accuracy of Individualized Prediction in MS

Abstract

Background: The MSBase prediction model of treatment response leverages multiple demographic and clinical characteristics to estimate hazards of relapses, confirmed disability accumulation (CDA), and confirmed disability improvement (CDI). The model did not include Multiple Sclerosis Severity Score (MSSS), a disease duration-adjusted ranked score of disability. Objective: To incorporate MSSS into the MSBase prediction model and compare model accuracy with and without MSSS. Methods: The associations between MSSS and relapse, CDA, and CDI were evaluated with marginal proportional hazards models adjusted for three principal components representative of patients' demographic and clinical characteristics. The model fit with and without MSSS was assessed with penalized r2 and Harrell C. Results: A total of 5866 MS patients were started on disease-modifying therapy during prospective follow-up (age 38.4 +/- 10.6 years; 72% female; disease duration 8.5 +/- 7.7 years). Including MSSS into the model improved the accuracy of individual prediction of relapses by 31%, of CDA by 23%, and of CDI by 24% (Harrell C) and increased the amount of variance explained for relapses by 49%, for CDI by 11%, and for CDA by 10% as compared with the original model. Conclusion: Addition of a single, readily available metric, MSSS, to the comprehensive MSBase prediction model considerably improved the individual accuracy of prognostics in MS.