An algorithm using clinical data to predict the optimal individual glucocorticoid dosage to treat multiple sclerosis relapses

Abstract

Background: Glucocorticoid (GC) pulse therapy is used for multiple sclerosis (MS) relapse treatment; however, GC resistance is a common problem. Considering that GC dosing is individual with several response-influencing factors, establishing a predictive model, which supports clinicians to estimate the maximum GC dose above which no additional therapeutic value can be expected presents a huge clinical need. Method: We established two, independent retrospective cohorts of MS patients. The first was an explorative cohort for model generation, while the second was established for its validation. Using the explorative cohort, a multivariate regression analysis with the GC dose used as the dependent variable and serum vitamin D (25D) concentration, sex, age, EDSS, contrast enhancement on cranial magnetic resonance imaging (MRI), immune therapy, and the involvement of the optic nerve as independent variables was established. Results: In the explorative cohort, 113 MS patients were included. 25-hydroxyvitamin D (25D) serum concentration and the presence of optic neuritis were independent predictors of the GC dose needed to treat MS relapses [(25D): −25.95 (95% confidence interval (CI)): −47.40 to −4.49; p = 0.018; optic neuritis: 2040.51 (95% CI: 584.64–3496.36), p = 0.006]. Validation of the multivariate linear regression model was performed within a second cohort. Here, the predicted GC dose did not differ significantly from the dose administered in clinical routine (mean difference: −843.54; 95% CI: −2078.08–391.00; n = 30, p = 0.173). Conclusion: Our model could predict the GC dose given in clinical, routine MS relapse care, above which clinicians estimate no further benefit. Further studies should validate and improve our algorithm to help the implementation of predictive models in GC dosing.