Background: Personalized healthcare promises to successfully advance the treatment of heterogeneous neurological disorders such as relapsing remitting multiple sclerosis by addressing the caveats of traditional healthcare. This study presents a framework for personalized prediction of treatment response based on real-world data from the NeuroTransData network. Methods: A framework for personalized prediction of response to various treatments currently available for relapsing remitting multiple sclerosis patients was proposed. Two indicators of therapy effectiveness were used: number of relapses, and confirmed disability progression. The following steps were performed: (1) Data preprocessing and selection of predictors according to quality and inclusion criteria; (2) Implementation of hierarchical Bayesian generalized linear models for estimating treatment response; (3) Validation of the resulting predictive models based on several performance measures and routines, together with additional analyses that focus on evaluating the usability in clinical practice, such as comparing predicted treatment response with the empirically observed course of multiple sclerosis for different adherence profiles. Results: The results revealed that the predictive models provide robust and accurate predictions and generalize to new patients and clinical sites. Three different out-of-sample validation schemes (10-fold cross-validation, leave-one-site-out cross-validation, and excluding a test set) were employed to assess generalizability based on three different statistical performance measures (mean squared error, Harrell's concordance statistic, and negative log-likelihood). Sensitivity to different choices of the priors, to the characteristics of the underlying patient population, and to the sample size, was assessed. Finally, it was shown that model predictions are clinically meaningful. Conclusions: Applying personalized predictive models in relapsing remitting multiple sclerosis patients is still new territory that is rapidly evolving and has many challenges. The proposed framework addresses the following challenges: robustness and accuracy of the predictions, generalizability to new patients and clinical sites and comparability of the predicted effectiveness of different therapies. The methodological and clinical soundness of the results builds the basis for a future support of patients and doctors when the current treatment is not generating the desired effect and they are considering a therapy switch.

Background Multiple sclerosis (MS) is a progressively debilitating neurologic disease that poses significant costs to the healthcare system and workforce. Objective To evaluate the impact of MS disease progression on societal costs and quality of life (QoL) using data from the German NeuroTransData (NTD) MS registry. Methods Cross-sectional cohort study. The cost cohort included patients with MS disability assessed using Expanded Disability Status Scale (EDSS) in 2019 while the QoL cohort included patients assessed using EDSS and EuroQol-5 Dimension 5-Levels between 2009 and 2019. Direct and indirect medical, and non-medical resource use was quantified and costs derived from public sources. Results Within the QoL cohort ( n = 9821), QoL worsened with increasing EDSS. Within the cost cohort ( n = 7286), increasing resource use with increasing EDSS was observed. Societal costs per patient, excluding or including disease-modifying therapies, increased from €5694 or €19,315 at EDSS 0 to 3.5 to €25,419 or €36,499 at EDSS 4 to 6.5, and €52,883 or €58,576 at EDSS 7 to 9.5. In multivariate modeling, each 0.5-step increase in EDSS was significantly associated with increasing costs, and worsening QoL. Conclusion This study confirms the major socioeconomic burden associated with MS disability progression. From a socioeconomic perspective, delaying disability progression may benefit patients and society.

BackgroundWith increasing availability of disease-modifying therapies (DMTs), treatment decisions in relapsing-remitting multiple sclerosis (RRMS) have become complex. Data-driven algorithms based on real-world outcomes may help clinicians optimize control of disease activity in routine praxis.ObjectivesWe previously introduced the PHREND® (Predictive-Healthcare-with-Real-World-Evidence-for-Neurological-Disorders) algorithm based on data from 2018 and now follow up on its robustness and utility to predict freedom of relapse and 3-months confirmed disability progression (3mCDP) during 1.5 years of clinical practice.MethodsThe impact of quarterly data updates on model robustness was investigated based on the model's C-index and credible intervals for coefficients. Model predictions were compared with results from randomized clinical trials (RCTs). Clinical relevance was evaluated by comparing outcomes of patients for whom model recommendations were followed with those choosing other treatments.ResultsModel robustness improved with the addition of 1.5 years of data. Comparison with RCTs revealed differences <10% of the model-based predictions in almost all trials. Treatment with the highest-ranked (by PHREND®) or the first-or-second-highest ranked DMT led to significantly fewer relapses (p < 0.001 and p < 0.001, respectively) and 3mCDP events (p = 0.007 and p = 0.035, respectively) compared to non-recommended DMTs.ConclusionThese results further support usefulness of PHREND® in a shared treatment-decision process between physicians and patients.

Background Primary progressive multiple sclerosis (PPMS) is characterised by gradual worsening of disability from symptom onset. Knowledge about the natural course of PPMS remains limited. Methods PPMS patients from the German NeuroTransData (NTD) MS registry with data from 56 outpatient practices were employed for retrospective cross-sectional and longitudinal analyses. The cross-sectional analysis included a contemporary PPMS cohort with a documented visit within the last 2 years before index date (1 Jan 2021). The longitudinal analysis included a disease modifying therapy (DMT)-naïve population and focused on the evolution of expanded disability status scale (EDSS) from the first available assessment at or after diagnosis within the NTD registry to index date. Outcome measures were estimated median time from first EDSS assessment to first 24-week confirmed EDSS ≥ 4 and ≥ 7. Besides EDSS change, the proportion of patients on disability pension were described over time. Results The cross-sectional analysis included 481 PPMS patients (59.9% female, mean [standard deviation, SD] age 60.5 [11.5] years, mean [SD] EDSS 4.9 [2.1]). Estimated median time from first EDSS assessment after diagnosis to reach 24-week confirmed EDSS ≥ 4 for DMT-naïve patients was 6.9 years. Median time to EDSS ≥ 7 was 9.7 years for 25% of the population. Over a decade mean (SD) EDSS scores increased from 4.6 (2.1) to 5.7 (2.0); the proportion of patients on disability pension increased from 18.9% to 33.3%. Conclusions This study provides first insights into the German NTD real-world cohort of PPMS patients. Findings confirm the steadily deteriorating course of PPMS accompanied by increasingly limited quality of life.