Retrospective cohort study to devise a treatment decision score predicting adverse 24-month radiological activity in early multiple sclerosis

Alexander Hapfelmeier, Begum Irmak On, Mark Mühlau, Jan S. Kirschke, Achim Berthele, Christiane Gasperi, Ulrich Mansmann, Alexander Wuschek, Matthias Bussas, Martin Boeker, Antonios Bayas, Makbule Senel, Joachim Havla, Markus C. Kowarik, Klaus Kuhn, Ingrid Gatz, Helmut Spengler, Benedikt Wiestler, Lioba Grundl, Dominik SeppBernhard Hemmer

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Background: Multiple sclerosis (MS) is a chronic neuroinflammatory disease affecting about 2.8 million people worldwide. Disease course after the most common diagnoses of relapsing-remitting multiple sclerosis (RRMS) and clinically isolated syndrome (CIS) is highly variable and cannot be reliably predicted. This impairs early personalized treatment decisions. Objectives: The main objective of this study was to algorithmically support clinical decision-making regarding the options of early platform medication or no immediate treatment of patients with early RRMS and CIS. Design: Retrospective monocentric cohort study within the Data Integration for Future Medicine (DIFUTURE) Consortium. Methods: Multiple data sources of routine clinical, imaging and laboratory data derived from a large and deeply characterized cohort of patients with MS were integrated to conduct a retrospective study to create and internally validate a treatment decision score [Multiple Sclerosis Treatment Decision Score (MS-TDS)] through model-based random forests (RFs). The MS-TDS predicts the probability of no new or enlarging lesions in cerebral magnetic resonance images (cMRIs) between 6 and 24 months after the first cMRI. Results: Data from 65 predictors collected for 475 patients between 2008 and 2017 were included. No medication and platform medication were administered to 277 (58.3%) and 198 (41.7%) patients. The MS-TDS predicted individual outcomes with a cross-validated area under the receiver operating characteristics curve (AUROC) of 0.624. The respective RF prediction model provides patient-specific MS-TDS and probabilities of treatment success. The latter may increase by 5–20% for half of the patients if the treatment considered superior by the MS-TDS is used. Conclusion: Routine clinical data from multiple sources can be successfully integrated to build prediction models to support treatment decision-making. In this study, the resulting MS-TDS estimates individualized treatment success probabilities that can identify patients who benefit from early platform medication. External validation of the MS-TDS is required, and a prospective study is currently being conducted. In addition, the clinical relevance of the MS-TDS needs to be established.

Original languageEnglish
JournalTherapeutic Advances in Neurological Disorders
StatePublished - 1 Jan 2023


  • machine learning
  • multiple sclerosis
  • personalized medicine
  • predictive factor
  • predictive model
  • treatment effect


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