Accès ouvert déclaré 2025 article

External validation of a multiple sclerosis treatment decision score using data from the ProVal-MS cohort study

Stefan Buchka, Alexander Hapfelmeier, Jan Stefan Kirschke, Verena Steuerwald, Markus K. Naumann, Iñaki Soto‐Rey, Sven Olaf Rohr, Frank Krämer, Lars Behrens, Eva J. Oswald, Tania Kümpfel, Hanna Zimmermann, Verena Sophia Hoffmann, Marlien Hagedorn, Fady Albashiti, Markus Krumbholz, Ulf Ziemann, Oliver Kohlbacher, Benjamin Sailer, Viola Braunmüller, Stephanie D. Biergans, Marius de Arruda Botelho Herr, Ulrike Ernemann, Eva Bürkle, Benjamin Bender, Andreas Daul, Christer Ruff, Jörg Römhild, Benedikt Wiestler, Dominik Sepp, Helmut Spengler, Peter Pallaoro, Martin Boeker, Florian Kohlmayer, Vera Dehmelt, Achim Berthele, Mark Mühlau, Paula Uibel, Josephine Wauschkuhn, Klaus A. Kuhn, Makbule Şenel, Ioannis Vardakas, Daniela Taranu, Hans A. Kestler, Nico Sollmann, Begüm Irmak Ön, Sandra Bilger, Ulrich Mansmann, Antonios Bayas, Joachim B. Havla, Markus Christian Kowarik, Hayrettin Tumani, Bernhard Hemmer

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2Pays d’affiliation déclarés

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Le résumé fourni par la source

Background: The course of relapsing-remitting multiple sclerosis (RRMS), frequently preceded by the clinically isolated syndrome (CIS), is variable and challenging to predict. Given many treatment options available, prognostic algorithms are gaining importance in informing initial treatment decisions. However, to date, only a few externally validated exists. External validation, which involves the application of a model to independent data, is essential. Privacy-preserving federated analyses of individual-level data facilitate external validation using clinical datasets that are typically difficult to access. Objectives: Using data from the ProVal-MS study to externally validate the multiple sclerosis treatment decision score (MS-TDS), a predictive algorithm for early RRMS and CIS. The MS-TDS predicts the probability of the occurrence of at least one new or enlarging T2 lesion within 6-24 months following the onset of the disease and supports choosing between initiating platform treatment or a 'wait-and-see' approach. A secondary objective is to demonstrate the feasibility of privacy-preserving federated concepts within the Data Integration for Future Medicine (DIFUTURE) consortium. Design: Prospective, multicentric, non-interventional cohort study (ProVal-MS) within DIFUTURE. Methods: The calibrated MS-TDS was evaluated using the area under the receiver operating characteristic curve (AUROC) and the Brier score in both pooled and distributed settings. A decision curve analysis (DCA) was used to evaluate the net benefit of treatment decisions made by the MS-TDS in comparison to those made by treating neurologists. Results: Of the 271 individuals diagnosed with CIS or early RRMS, 202 (78.2%) received platform treatment, while 59 (21.8%) did not receive treatment. The AUROC was 0.561 (95% CI: 0.492-0.630) in the pooled analysis and 0.567 (95% CI: 0.496-0.634) in the distributed analysis. DCA demonstrated a net benefit that was commensurate with that achieved by decisions made by experienced neurologists. Conclusion: The external validation of the MS-TDS demonstrated low, non-significant predictive performance; however, it may serve as a useful complement, particularly for less-experienced neurologists. The distributed validation was found to be both feasible and compliant with data protection regulations.

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Le contrôle bibliographique ouvert

DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref: External validation of a multiple sclerosis treatment decision score using data from the ProVal-MS cohort study
Date Crossref: 01/01/2025
Éditeur: SAGE Publications
Type: journal-article

Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.

Où se fait cette recherche

Zimmer Biomet (Netherlands) pays non établi dans la notice
Entreprise
TUM Klinikum pays non établi dans la notice
Établissement de santé
University of Augsburg IT-Infrastructure for Translational Medical Research pays non établi dans la notice
Université ou école supérieure
University Hospital Augsburg Institute of Clinical Neuroimmunology pays non établi dans la notice
Établissement de santé
LMU Klinikum pays non établi dans la notice
Établissement de santé
Institut für Klinische Neuroimmunologie pays non établi dans la notice
Structure de recherche
Ludwig-Maximilians-Universität München pays non établi dans la notice
Université ou école supérieure
Neu-Ulm University of Applied Sciences Institute of Medical Systems Biology pays non établi dans la notice
Université ou école supérieure
Hertie Institute for Clinical Brain Research pays non établi dans la notice
Institution
University of Tübingen Department of Neurology and Stroke pays non établi dans la notice
Université ou école supérieure
University Children's Hospital Tübingen pays non établi dans la notice
Établissement de santé
Technical University of Munich pays non établi dans la notice
Université ou école supérieure

Zimmer Biomet (Netherlands), TUM Klinikum et IT-Infrastructure for Translational Medical Research — University of Augsburg, avec 9 autres affiliations.

Une affiliation ne permet pas de déduire la nationalité d’un auteur.

Les sujets associés

Multiple Sclerosis Research StudiesStatistical Methods in Clinical TrialsHealth Systems, Economic Evaluations, Quality of Life

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