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

Medientyp: E-Artikel
Titel: Retrospective cohort study to devise a treatment decision score predicting adverse 24-month radiological activity in early multiple sclerosis
Beteiligte: Hapfelmeier, Alexander; On, Begum Irmak; Mühlau, Mark; Kirschke, Jan S.; Berthele, Achim; Gasperi, Christiane; Mansmann, Ulrich; Wuschek, Alexander; Bussas, Matthias; Boeker, Martin; Bayas, Antonios; Senel, Makbule; Havla, Joachim; Kowarik, Markus C.; Kuhn, Klaus; Gatz, Ingrid; Spengler, Helmut; Wiestler, Benedikt; Grundl, Lioba; Sepp, Dominik; Hemmer, Bernhard
Erschienen: SAGE Publications, 2023
Erschienen in: Therapeutic Advances in Neurological Disorders
Sprache: Englisch
DOI: 10.1177/17562864231161892
ISSN: 1756-2864
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Beschreibung: <jats:sec><jats:title>Background:</jats:title><jats:p> 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. </jats:p></jats:sec><jats:sec><jats:title>Objectives:</jats:title><jats:p> 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. </jats:p></jats:sec><jats:sec><jats:title>Design:</jats:title><jats:p> Retrospective monocentric cohort study within the Data Integration for Future Medicine (DIFUTURE) Consortium. </jats:p></jats:sec><jats:sec><jats:title>Methods:</jats:title><jats:p> 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. </jats:p></jats:sec><jats:sec><jats:title>Results:</jats:title><jats:p> 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. </jats:p></jats:sec><jats:sec><jats:title>Conclusion:</jats:title><jats:p> 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. </jats:p></jats:sec>
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