A multi-outcome prognostic score for therapeutic responses in multiple sclerosis.
Authors
Affiliations (9)
Affiliations (9)
- Nantes University, Nantes University Hospital, École Centrale Nantes, Inserm, Center for Research in Transplantation and Translational Immunology, UMR 1064, 44000 Nantes, France.
- Neurology, Merck Santé S.A.S., an affiliate of Merck KGaA, Lyon, France.
- Biogen France S.A.S, Paris, France.
- Université de Lyon, Université Claude-Bernard Lyon 1, 69000 Lyon, France; Hospices Civils de Lyon, Service de Neurologie, sclérose en plaques, pathologies de la myéline et neuro-inflammation, 69677 Bron, France; Observatoire Français de la Sclérose en Plaques, Centre de Recherche en Neurosciences de Lyon, Inserm 1028, CNRS UMR 5292, 69003 Lyon, France; EUGENE DEVIC EDMUS Foundation against multiple sclerosis, State-Approved Foundation, 69677 Bron, France.
- Inserm, Clinical Investigation Center (CIC1434), University of Strasbourg, Strasbourg, France; Department of Neurology, University Hospital of Strasbourg, Strasbourg, France.
- Nantes University, Nantes University Hospital, École Centrale Nantes, Inserm, Center for Research in Transplantation and Translational Immunology, UMR 1064, 44000 Nantes, France; Department of Neurology, University Hospital of Nantes, Nantes, France.
- Department of Neurology, University Hospital of Rennes, Rennes, France.
- Nantes University, Nantes University Hospital, École Centrale Nantes, Inserm, Center for Research in Transplantation and Translational Immunology, UMR 1064, 44000 Nantes, France; Clinique des Données, Pôle Hospitalo-Universitaire 11: Santé Publique, CHU de Nantes, Nantes Université, Nantes, France. Electronic address: [email protected].
- Nantes University, Nantes University Hospital, École Centrale Nantes, Inserm, Center for Research in Transplantation and Translational Immunology, UMR 1064, 44000 Nantes, France; Inserm, Clinical Investigation Center (CIC1434), University of Strasbourg, Strasbourg, France.
Abstract
Multiple sclerosis (MS) is marked by heterogeneous disease activity, progression, and therapeutic response. Here, we developed a prognostic score based on machine learning using randomized clinical trials (RCTs) and observational datasets. The score's ability to predict short-term prognosis, expressed as absolute risk, was assessed on the French population in the context of all common MS therapeutic scenarios. Nine industrial RCTs and one prospective cohort from the French MS registry were used to develop several types of multilabel binary classifiers designed to predict the two-year risk of relapse, advent of new brain T2 lesions, and sustained disability worsening, as well as the respective yearly risks. Model evaluation prioritized calibration of probabilistic predictions over discriminatory capacity. Virtual cohorts simulated from the model predictions were analyzed to determine how the predictive score captured clinically meaningful information, such as the efficacy of different therapeutic classes. The model with the best calibration was evaluated externally on the population-based cohort of the French MS registry. Random forest modeling optimally captured the time-course of MS risks. In the evaluation dataset, calibration shifted with underconfident predictions of relapse and overconfident predictions of new brain T2 lesions. Nevertheless, unadjusted average therapeutic class efficacy on MRI activity generalized well. At external validation, discriminatory capacities were modest: AUC=0.67, 0.75, and 0.58 for relapse, new brain T2 lesions, and sustained disability worsening, respectively. Based on a panel of variables currently available during routine care for MS patients, we propose a score predictive of short-term therapeutic response to commonly prescribed therapeutic classes. Predictions of MRI activity generalized well across the common therapeutic scenarios. The model's probabilistic approach, emphasizing prediction certainty rather than the prediction itself, captured clinically useful information for the selection of disease-modifying treatments.