Multivariable prognostic prediction of efficacy and safety outcomes and response to fingolimod in people with relapsing-remitting multiple sclerosis

Background: The individual treatment response in people with relapsing-remitting multiple sclerosis (RRMS) remain unpredictable. In order to support medical decisions, we aimed to predict response to fingolimod compared to placebo, by developing and validating prognostic multivariable models. Methods: We included two-year follow-up from intention-to-treat populations of two multi-country placebocontrolled randomized controlled trials (RCT) of daily fingolimod 0.5 mg. The data was accessed via ClinicalStudyDataRequest.com (Proposal Number: 11223) The RCTs were in adult RRMS patients with active disease. We used four Cox proportional hazards based penalized (elastic net and grouped lasso) and tree methods (transformation tree and forest) to predict time-to relapse and other relevant efficacy and safety endpoints in data from the RCT FREEDOMS. Treatment arm, 80 baseline variables and their interaction with treatment were considered as candidate predictors in the models. A nested cross-validation scheme ensured independent tuning parameter optimization and internal model performance evaluation. The generalizability of the models with the highest cross-validated time-dependent area under the receiver operating curve (AUC) was further evaluated in terms of discrimination (AUC), calibration (plots, intercept, slope), clinical utility (decision curve analysis), and treatment response plots by external validation in data from the RCT FREEDOMS II. Results: The best performing model predicting relapse risk (331 events) in the development sample (n=843) was an elastic net regression with main terms for four predictors alongside treatment: EDSS score, volume of Gadolinium enhanced T1 lesions, number of relapses in the last 2 years, and number of prior MS treatments. In external validation (n=713), it had an AUC of 0.68 (95% CI 0.63-0.72), but the predictions were overestimating the actual risk (358 events) with a calibration-in-the-large of -0.17 (-0.3 - -0.04) and a slope of 1.06 (0.78-1.35). Almost no heterogeneity (variability 0.001) was detected in the predicted relapse risk change in response to fingolimod. FREEDOMS II participants were predicted to have 0.21 to 0.31 absolute relapse risk reduction with fingolimod compared to placebo. The selected model predicting new or enlarging T2 magnetic resonance imaging (MRI) lesions had an AUC of 0.74 (0.70-0.78), moderate calibration, but no treatment response variability. The final model predicting confirmed disability progression had an AUC of 0.59 (0.54-0.64) and the predicted treatment response heterogeneity was not significant. The overall safety outcome could not be predicted with sufficient discrimination. However, the final model predicting infections or neoplasms had an AUC of 0.69 (0.63-0.74) and non-significant treatment response heterogeneity. For the efficacy outcomes, important predictors were related to (para)clinical disease activity or disability. Unexpected influential predictors included concomitant disorders. Conclusion: Relapse and new or enlarging T2 MRI lesions were moderately predictable in an independent sample with the developed prognostic models. Fingolimod was expected to decrease the risk of these events for all patients, with no predictable heterogeneity. Disability and safety outcomes could not be well-predicted and it is yet unresolved whether the change in their risk as response to fingolimod is heterogeneous or not.