MS disease activity in RESTORE: a randomized 24-week natalizumab treatment interruption study

Objective: RESTORE was a randomized, partially placebo-controlled exploratory study evaluating multiple sclerosis (MS) disease activity during a 24-week interruption of natalizumab. Methods: eligible patients were relapse-free through the prior year on natalizumab and had no gadolinium-enhancing lesions on screening brain MRI. Patients were randomized 1:1:2 to continue natalizumab, to switch to placebo, or to receive alternative immunomodulatory therapy (other therapies: IM interferon β-1a [IM IFN-β-1a], glatiramer acetate [GA], or methylprednisolone [MP]). During the 24-week randomized treatment period, patients underwent clinical and MRI assessments every 4 weeks. Results: patients (n = 175) were randomized to natalizumab (n = 45), placebo (n = 42), or other therapies (n = 88: IM IFN-β-1a, n = 17; GA, n = 17; MP, n = 54). Of 167 patients evaluable for efficacy, 49 (29%) had MRI disease activity recurrence: 0/45 (0%) natalizumab, 19/41 (46%) placebo, 1/14 (7%) IM IFN-β-1a, 8/15 (53%) GA, and 21/52 (40%) MP. Relapse occurred in 4% of natalizumab patients and in 15%-29% of patients in the other treatment arms. MRI disease activity recurred starting at 12 weeks (n = 3 at week 12) while relapses were reported as early as 4-8 weeks (n = 2 in weeks 4-8) after the last natalizumab dose. Overall, 50/167 patients (30%), all in placebo or other-therapies groups, restarted natalizumab early because of disease activity. Conclusions: MRI and clinical disease activity recurred in some patients during natalizumab interruption, despite use of other therapies. Classification of evidence: this study provides Class II evidence that for patients with MS taking natalizumab who are relapse-free for 1 year, stopping natalizumab increases the risk of MS relapse or MRI disease activity as compared with continuing natalizumab

International delphi consensus on the management of AQP4-IgG+ NMOSD: recommendations for eculizumab, inebilizumab, and satralizumab

BACKGROUND AND OBJECTIVES: Neuromyelitis optica spectrum disorder (NMOSD) is a rare debilitating autoimmune disease of the CNS. Three monoclonal antibodies were recently approved as maintenance therapies for aquaporin-4 immunoglobulin G (AQP4-IgG)-seropositive NMOSD (eculizumab, inebilizumab, and satralizumab), prompting the need to consider best practice therapeutic decision-making for this indication. Our objective was to develop validated statements for the management of AQP4-IgG-seropositive NMOSD, through an evidence-based Delphi consensus process, with a focus on recommendations for eculizumab, inebilizumab, and satralizumab. METHODS: We recruited an international panel of clinical experts in NMOSD and asked them to complete a questionnaire on NMOSD management. Panel members received a summary of evidence identified through a targeted literature review and provided free-text responses to the questionnaire based on both the data provided and their clinical experience. Responses were used to generate draft statements on NMOSD-related themes. Statements were voted on over a maximum of 3 rounds; participation in at least 1 of the first 2 rounds was mandatory. Panel members anonymously provided their level of agreement (6-point Likert scale) on each statement. Statements that failed to reach a predefined consensus threshold (≥67%) were revised based on feedback and then voted on in the next round. Final statements were those that met the consensus threshold (≥67%). RESULTS: The Delphi panel comprised 24 experts, who completed the Delphi process in November 2021 after 2 voting rounds. In round 1, 23/25 statements reached consensus and were accepted as final. The 2 statements that failed to reach consensus were revised. In round 2, both revised statements reached consensus. Twenty-five statements were agreed in total: 11 on initiation of or switching between eculizumab, inebilizumab, and satralizumab; 3 on monotherapy/combination therapy; 7 on safety and patient population considerations; 3 on biomarkers/patient-reported outcomes; and 1 on research gaps. DISCUSSION: An established consensus method was used to develop statements relevant to the management of AQP4-IgG-seropositive NMOSD. These international statements will be valuable for informing individualized therapeutic decision-making and could form the basis for standardized practice guidelines

Clinical applications of deep learning in neuroinflammatory diseases: A scoping review

International audienceBackground: Deep learning (DL) is an artificial intelligence technology that has aroused much excitement for predictive medicine due to its ability to process raw data modalities such as images, text, and time series of signals.Objectives: Here, we intend to give the clinical reader elements to understand this technology, taking neuroinflammatory diseases as an illustrative use case of clinical translation efforts. We reviewed the scope of this rapidly evolving field to get quantitative insights about which clinical applications concentrate the efforts and which data modalities are most commonly used.Methods: We queried the PubMed database for articles reporting DL algorithms for clinical applications in neuroinflammatory diseases and the radiology.healthairegister.com website for commercial algorithms.Results: The review included 148 articles published between 2018 and 2024 and five commercial algorithms. The clinical applications could be grouped as computer-aided diagnosis, individual prognosis, functional assessment, the segmentation of radiological structures, and the optimization of data acquisition. Our review highlighted important discrepancies in efforts. The segmentation of radiological structures and computer-aided diagnosis currently concentrate most efforts with an overrepresentation of imaging. Various model architectures have addressed different applications, relatively low volume of data, and diverse data modalities. We report the high-level technical characteristics of the algorithms and synthesize narratively the clinical applications. Predictive performances and some common a priori on this topic are finally discussed.Conclusion: The currently reported efforts position DL as an information processing technology, enhancing existing modalities of paraclinical investigations and bringing perspectives to make innovative ones actionable for healthcare