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Spotting progression in MS: What are you missing?

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Test your knowledge

Why has PIRA become more clinically relevant in the modern disease-modifying therapy (DMT) era?

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Relapses and progression are now understood to be identical processes
   
Relapse activity may be reduced while disability accrual still continues
   
PIRA is only seen after conversion to secondary progressive MS
   
PIRA can be diagnosed using MRI alone
   

Test your knowledge

Which clinical change may be missed by an EDSS-centred approach?

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New gadolinium-enhancing lesions
   
Subtle cognitive or upper limb impairment
   
Acute optic neuritis
   
New spinal cord lesions
   

Test your knowledge

What is a key current limitation of AI-supported tools in MS care?

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AI eliminates the need for human clinical oversight
   
AI tools are already fully validated for routine MS progression monitoring
   
AI cannot be applied to MRI, EHR or wearable data
   
Real-time, scalable and clinically interpretable use remains challenging
   

Test your knowledge

Which biological mechanism is discussed as contributing to PIRA?

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Acute relapse activity alone
   
Compartmentalised CNS inflammation involving microglia and astrocytes
   
Peripheral infection
   
Vascular disease only
   
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Modules

Introduction

Dr Marcello Moccia introduces this microlearning module series exploring modalities to uncover MS progression without overt relapses.
Dr Marcello Moccia
Start Time: 0:00

Module 1: Beyond relapses: Uncovering PIRA

Even when patients seem clinically stable, progression can be unnoticed. This module unpacks PIRA: covering what it is, how to spot it, and why recognizing it is key to halting silent disease advancement.
Dr Georgina Arrambide
Start Time: 1:17

Module 2: Tracking MS: Tools that work and those that don’t

This module breaks down the tools HCPs rely on to track MS progression (digital apps, MRI markers, blood based markers, neuro exams). The module will explore what works, what falls short, and how to tailor tracking strategies to individual patient care.
Prof. Friedemann Paul
Start Time: 9:10

Module 3: Machine minds: How can AI be applied successfully?

This module will explore how AI and machine learning are being applied in MS care. From prediction models to image analysis, discover where tech can help, and why human clinical judgment still leads.
Dr Riley Bove
Start Time: 14:40

Module 4: Small clues, big changes: Biology-guided treatment decisions in MS

This module explores biology-guided treatment decision-making in MS, with a particular focus on identifying and managing disease progression.
Dr Ahmed Abdelhak
Start Time: 22:24

Learning Objectives & Overview

Faculty & Disclosures

Transcript

Overview
Faculty
Transcript
  • Recognize early and subtle signs of progression in multiple sclerosis (MS), including progression independent of relapse activity (PIRA), and understand the limitations of relying on relapse activity alone.
  • Evaluate the strengths and limitations of current tools used to monitor MS progression, including clinical assessments, imaging, biomarkers, patient-reported outcomes, and emerging digital technologies.
  • Describe how emerging approaches, including AI-driven tools and biology-guided treatment strategies, may support earlier detection, personalized decision-making, and improved management of MS progression.

Recognizing progression in MS

Progression in MS is not always readily apparent. Subtle changes in cognition, physical function, imaging findings, or biological markers may emerge long before overt disability accumulation or clinical relapses become evident. As our understanding of PIRA continues to evolve, clinicians face increasing challenges in identifying progression earlier and making informed treatment decisions.

Expert perspectives

This microlearning modular series brings together leading international experts to examine how MS progression can be identified, monitored, and managed more effectively in clinical practice.

Across four focused modules, experts explore early and subtle signs of progression in MS, limitations of traditional monitoring approaches, the evolving role of biomarkers and emerging technologies, and the potential of artificial intelligence and biology-guided strategies to support more personalized MS care.

Practical insights for clinical practice

Designed for busy neurologists and MS specialists, this modular series delivers concise, practical insights to support earlier recognition of disease progression and enhance confidence in clinical decision-making.

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Tur C, Carbonell-Mirabent P, Cobo-Calvo Á, et al. Progression independent of relapse activity in multiple sclerosis. Mult Scler. 2024;30(10):1265–1276.

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Guerra T, Magliozzi R, Calabrese M. A window into new insights on progression independent of relapse activity in multiple sclerosis. Int J Mol Sci. 2025;26(3):884.

3
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Meyer-Moock S, Feng YS, Maeurer M, Dippel FW, Kohlmann T. Systematic literature review and validity evaluation of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) in patients with multiple sclerosis. BMC Neurol. 2014;14:58.

4
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Müller J, Cagol A, Lorscheider J, et al. Standardized definition of progression independent of relapse activity in relapsing-remitting multiple sclerosis. JAMA Neurol. 2025.

5
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Richert ND, Hemmer B, Korn T, et al. Artificial intelligence and machine learning in multiple sclerosis: opportunities, challenges and clinical implications. Nat Rev Neurol. 2024;20:95–110.

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Bsteh G, Portaccio E, Preziosa P, et al. Biomarkers of progression independent of relapse activity in multiple sclerosis. Int J Mol Sci. 2025;26(10):4704.

Dr Marcello Moccia

University of Naples, Naples, Italy
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Dr Marcello Moccia

University of Naples, Naples, Italy
Italy

Biography

Disclosures

Dr Ahmed Abdelhak

Dr Georgina Arrambide

Dr Georgina Arrambide

Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain
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Dr Georgina Arrambide

Multiple Sclerosis Centre of Catalonia (Cemcat), Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain
Spain

Biography

Disclosures

Dr Marcello Moccia

Prof. Friedemann Paul

Prof. Friedemann Paul

Max Delbrück Center, Berlin, Germany
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Prof. Friedemann Paul

Max Delbrück Center, Berlin, Germany
Germany

Biography

Disclosures

Dr Georgina Arrambide

Dr Riley Bove

Dr Riley Bove

UCSF Weill Institute for Neurosciences, San Francisco, CA, USA
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Dr Riley Bove

UCSF Weill Institute for Neurosciences, San Francisco, CA, USA
USA

Biography

Prof. Friedemann Paul

Dr Ahmed Abdelhak

Dr Ahmed Abdelhak

University of California, San Francisco, CA, USA
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Dr Ahmed Abdelhak

University of California, San Francisco, CA, USA
USA

Biography

Disclosures

Dr Riley Bove

Dr Marcello Moccia

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Modules

Introduction

Dr Marcello Moccia introduces this microlearning module series exploring modalities to uncover MS progression without overt relapses.
Dr Marcello Moccia
Start Time: 0:00

Module 1: Beyond relapses: Uncovering PIRA

Even when patients seem clinically stable, progression can be unnoticed. This module unpacks PIRA: covering what it is, how to spot it, and why recognizing it is key to halting silent disease advancement.
Dr Georgina Arrambide
Start Time: 1:17

Module 2: Tracking MS: Tools that work and those that don’t

This module breaks down the tools HCPs rely on to track MS progression (digital apps, MRI markers, blood based markers, neuro exams). The module will explore what works, what falls short, and how to tailor tracking strategies to individual patient care.
Prof. Friedemann Paul
Start Time: 9:10

Module 3: Machine minds: How can AI be applied successfully?

This module will explore how AI and machine learning are being applied in MS care. From prediction models to image analysis, discover where tech can help, and why human clinical judgment still leads.
Dr Riley Bove
Start Time: 14:40

Module 4: Small clues, big changes: Biology-guided treatment decisions in MS

This module explores biology-guided treatment decision-making in MS, with a particular focus on identifying and managing disease progression.
Dr Ahmed Abdelhak
Start Time: 22:24

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