Open Access

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

• 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 platformBackground: 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.…