Introduction: Over the years, disease registers have been increasingly considered a source of reliable and valuable population studies. However, the validity and reliability of data from registers may be limited by missing data, selection bias or data quality not adequately evaluated or checked. This study reports the analysis of the consistency and completeness of the data in the Italian Multiple Sclerosis and Related Disorders Register. Methods: The Register collects, through a standardized Web-based Application, unique patients. Data are exported bimonthly and evaluated to assess the updating and completeness, and to check the quality and consistency. Eight clinical indicators are evaluated. Results: The Register counts 77,628 patients registered by 126 centres. The number of centres has increased over time, as their capacity to collect patients. The percentages of updated patients (with at least one visit in the last 24 months) have increased from 33% (enrolment period 2000–2015) to 60% (enrolment period 2016–2022). In the cohort of patients registered after 2016, there were ≥ 75% updated patients in 30% of the small centres (33), in 9% of the medium centres (11), and in all the large centres (2). Clinical indicators show significant improvement for the active patients, expanded disability status scale every 6 months or once every 12 months, visits every 6 months, first visit within 1 year and MRI every 12 months. Conclusions: Data from disease registers provide guidance for evidence-based health policies and research, so methods and strategies ensuring their quality and reliability are crucial and have several potential applications.

In multiple sclerosis (MS), there is significant evidence indicating that both progression independent of relapse activity (PIRA) and relapse-related worsening events contribute to the accumulation of progressive disability from the onset of the disease and throughout its course. Understanding the compartmentalized pathophysiology of MS would enhance comprehension of disease progression mechanisms, overcoming the traditional distinction in phenotypes. Smoldering MS activity is thought to be maintained by a continuous interaction between the parenchymal chronic processes of neuroinflammation and neurodegeneration and the intrathecal compartment. This review provides a comprehensive and up-to-date overview of the neuropathological and immunological evidence related to the mechanisms underlying PIRA phenomena in MS, with a focus on studies investigating the impact of currently available therapies on these complex mechanisms.

Multiple sclerosis (MS) is characterized by a continuum of diverse neuroinflammatory and neurodegenerative processes that contribute to disease progression from the earliest stages. This leads to a highly heterogeneous clinical course, requiring early and accurate prognostic assessment: the identification of reliable prognostic biomarkers is crucial to support therapeutic decision-making and guide personalized disease management. In this narrative review, we critically examined the current MS literature, investigating prognostic factors associated with disease progression and irreversible disability in adult-onset MS, with a focus on different clinical, radiological, and molecular biomarkers. Particular attention is directed toward the prognostic value of baseline clinical and neuroimaging factors, emerging biomarkers of smoldering disease, and progression independent of relapse activity (PIRA) events. Additionally, we discussed the role of integrated prognostic tools and risk scores, as well as their potential impact on clinical practice. We aim to provide a comprehensive and clinically oriented synthesis of available evidence in the MS biomarkers field, supporting multifaceted prognostication strategies to improve long-term outcomes in people with MS.

Importance: Large multiple sclerosis (MS) registries provide crucial real-world evidence but often suffer from missing data, inconsistencies, and privacy limitations that restrict data sharing. The use of generative AI to create synthetic data (SD) is an emerging strategy to enhance real-world evidence research potentially overcoming these challenges. Objective: To evaluate the validity of AI-generated synthetic data (SD) in replicating real data collected in the Italian MS and Related Disorders Register (RISM), and to compare the risk of progression independent of relapse activity (PIRA) between early intensive treatment (EIT) versus escalation treatment strategy (ESC) in both real and synthetic MS cohorts. Design, Setting, and Participants: This validation study analyzed data from RISM. AI-based generative models were trained on a sub-cohort of 1,666 patients with tabularized MRI data to generate a synthetic dataset of 4,878 patients. SD was evaluated using the Synthetic vAlidation FramEwork powered by Train (SAFE), assessing fidelity, utility, and privacy. Clinical Synthetic Fidelity (CSF) and Nearest Neighbor Distance Ratio (NNDR) were used for statistical and privacy validation. Treatment outcome comparisons between EIT and ESC strategies were conducted for clinical validation using both real and synthetic datasets, focusing on the risk of PIRA. Exposures: Initial disease-modifying therapy strategy, categorized as EIT versus ESC. Main Outcomes and Measures: Primary outcome was the occurrence of PIRA, defined as confirmed disability accrual independent of relapses. Validation metrics included Clinical Synthetic Fidelity (CSF ≥90 optimal) and Nearest Neighbor Distance Ratio (NNDR, range 0.60–0.85 for privacy). Results: The synthetic dataset demonstrated high fidelity (CSF=97%) and privacy preservation (NNDR=0.61). Treatment effect estimates for ESCs vs EIT were consistent across real and synthetic datasets, with largely comparable trends, with increased statistical significance in SD. Cox proportional hazards models confirmed the robustness of synthetic data in estimating the risk of the first PIRA event. Conclusions and Relevance: AI-generated synthetic data reliably replicated treatment effect outcomes from real-world RISM data, overcoming missing data and providing a privacy-preserving alternative for data sharing and clinical research. Key points: Question: Can Artificial Intelligence (AI)-generated synthetic data (SD) reliably replicate multiple sclerosis (MS) registry data and provide robust insights into progression independent of relapse activity (PIRA) phenomena under different treatment strategies? Findings: In a cohort of 4,878 relapsing-onset MS patients from the Italian MS Register, AI-generated SD achieved high fidelity (CSF = 97%), and reproduced treatment effect outcomes. Both real and synthetic cohorts consistently showed that early intensive therapy reduced the risk of PIRA compared with an escalation strategy. Meaning: SD can complement and enhance registry-based research by addressing missing data and supporting reproducible analyses in MS.

Dengue fever (DF) is an endemic infectious disease in tropical and subtropical regions. Ocrelizumab is a humanized monoclonal antibody that targets the CD20 antigen on B cells, which is approved for the treatment of both relapsing-remitting multiple sclerosis (RRMS) and primary-progressive multiple sclerosis (PPMS). We describe the favorable clinical outcome of DF in an RRMS patient treated with Ocrelizumab, who neither presented hemorrhagic or systemic shock symptoms nor reported neurological worsening.

Aims: To identify a correlation between unmet needs and HL levels in people with Multiple Sclerosis (pwMS) and to evaluate how sociodemographic characteristics influence HL levels and unmet needs. Design: A cross‐sectional study. Methods: The study was conducted using a questionnaire including the HLS ₁₉ ‐Q12 to assess HL and the Long‐term Unmet Needs in Multiple Sclerosis tool, which evaluates five domains (neuropsychological, ambulation, physical, interpersonal relationship, and informational) and identifies whether needs are met or unmet and the desire for support. Results: Among the 116 participants included in the study, the overall HL level was sufficient. Mean scores across unmet needs domains were comparable. A significant difference in HL emerged in the informational domain, where participants reporting informational needs and a desire for support showed higher mean ranks. Although not significant, participants who acknowledged a need and expressed a desire for help showed higher mean ranks in HLS ₁₉ ‐Q12 scores across several domains. No significant correlations were found between HL and unmet needs domains. Conclusion: HL levels may enhance patients' ability to recognize and express needs without necessarily ensuring that these needs are met. Implications for the Profession and/or Patient Care: Routine assessment of both HL and unmet needs may help healthcare professionals identify patients who recognize problems but lack the structural support to address them. Impact: The impact of HL on need recognition and communication, together with the complexity and interconnectedness of unmet needs, highlights the need for healthcare systems to implement organizational, systemic, and multidimensional interventions aimed at promoting HL and effectively addressing patients' needs. Such strategies may support better disease management and improve quality of life in pwMS. Reporting Method: This study was reported according to STROBE checklist. Patient or Public Contribution: None.