Management

Magd Zakaria: Prognostic Factors in Multiple Sclerosis: Supporting Early Treatment Decisions

Magd Zakaria discusses how prognostic factors can support personalized treatment decisions in treatment-naïve patients with Multiple Sclerosis.

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Selecting the most appropriate treatment at diagnosis requires an accurate assessment of disease activity and prognosis. In this interview, Magd Zakaria discusses a practical scoring system that combines demographic, clinical and MRI characteristics to help stratify treatment-naïve patients according to their risk of disease progression and support informed therapeutic decisions.

Key points discussed include:

Why prognostic assessment remains essential when selecting an initial treatment strategy.
The development of a practical scoring system based on demographic, clinical and MRI prognostic factors.
The clinical and MRI features associated with a poorer prognosis.
How patients can be classified as active, highly active or aggressive based on their overall score.
The role of prognostic scoring in supporting equitable and sustainable treatment decisions across different healthcare settings.

Which early prognostic factors do you find most reliable when evaluating therapy options in treatment-naïve MS patients ?

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Key messages from Magd Zakaria

Early treatment decisions should be guided by an individual patient’s risk of disease progression rather than a one-size-fits-all approach.
A practical scoring system incorporating demographic, clinical and MRI characteristics can help classify treatment-naïve patients according to disease activity.
Important prognostic factors include age, sex, clinical presentation, relapse rate, baseline Expanded Disability Status Scale (EDSS) score, lesion burden, gadolinium-enhancing lesions, brainstem and spinal cord involvement, and brain atrophy.
Using readily available clinical and MRI information allows clinicians to stratify patients into active, highly active or aggressive disease categories without relying on advanced imaging techniques or emerging biomarkers.
Risk stratification supports treatment decisions by identifying patients who may benefit from early high-efficacy therapies while promoting equitable and sustainable use of healthcare resources.