Short-term prediction of secondary progression in a sliding window: A test of a predicting algorithm in a validation cohort. Issue 3 (September 2019) - Skoog, B; Link, J; Tedeholm, H; Longfils, M; Nerman, O; Fagius, J; Andersen, O
Introduction: The Multiple Sclerosis Prediction Score (MSPS, www.msprediction.com ) estimates, for any month during the course of relapsing–remitting multiple sclerosis (MS), the individual risk of transition to secondary progression (SP) during the following year. Objective: Internal verification of the MSPS algorithm in a derivation cohort, the Gothenburg Incidence Cohort (GIC, n = 144) and external verification in the Uppsala MS cohort (UMS, n = 145). Methods: Starting from their second relapse, patients were included and followed for 25 years. A matrix of MSPS values was created. From this matrix, a goodness-of-fit test and suitable diagnostic plots were derived to compare MSPS-calculated and observed outcomes (i.e. transition to SP). Results: The median time to SP was slightly longer in the UMS than in the GIC, 15 vs. 11.5 years ( p = 0.19). The MSPS was calibrated with multiplicative factors: 0.599 for the UMS and 0.829 for the GIC; the calibrated MSPS provided a good fit between expected and observed outcomes (chi-square p = 0.61 for the UMS), which indicated the model was not rejected. Conclusion: The results suggest that the MSPS has clinically relevant generalizability in new cohorts, provided that the MSPS was calibrated to the actual overall SP incidence in the cohort.