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Prognostic factors and predictive model for severe multiple sclerosis at first onset in a pediatric French cohort
Aliénor de Chalus1, Gonzalo Barraza2, Nicolas Tchitchek3
1Pediatric Neurology Department, Bicêtre Hospital, Assistance Publique-Hôpitaux de Paris, Paris Saclay University Hospitals, Le Kremlin-Bicêtre, France; National Referral Center for Rare Brain and Spinal Diseases, Le Kremlin-Bicêtre, France; Sorbonne Université, INSERM, UMR_S 959, Immunology-Immunopathology- Immunotherapy (i3), F-75651, Paris, France.
Insights
Researchers identified key factors for predicting severe pediatric-onset multiple sclerosis (POMS). A new model uses clinical and radiological markers to identify high-risk children, enabling early, personalized treatment for POMS.
Area of Science:
- Neurology
- Pediatrics
- Immunology
Background:
- Pediatric-onset multiple sclerosis (POMS) presents unique challenges in predicting disease severity.
- Early identification of severe POMS is crucial for timely intervention and management.
Purpose of the Study:
- To identify prognostic factors for severe POMS in a French cohort.
- To develop a predictive model for early identification of high-risk POMS patients.
Main Methods:
- Retrospective analysis of 70 children with POMS (2000-2017).
- Defined severe POMS by relapse count and/or Expanded Disability Status Scale (EDSS) score.
- Compared clinical, biological, and radiological features to identify prognostic factors and build a predictive model.
Main Results:
- 70% of patients had severe POMS based on combined criteria.
- Key prognostic factors included shorter interval between first two relapses, older age at onset, and higher initial MRI lesion load.
- The final predictive model incorporated time between attacks, age, sex, juxtacortical lesions, and CSF pleocytosis, demonstrating high specificity and sensitivity.
Conclusions:
- Clinical and radiological markers can predict severe POMS.
- A developed predictive model aids in early identification of high-risk POMS patients.
- Further validation in independent cohorts is needed for clinical application.
Objective:
To identify key prognostic factors and develop a predictive model for poor-prognosis forms of pediatric-onset multiple sclerosis (POMS) in a French cohort.
Methods:
Between 2000 and 2017, 70 children with POMS were included. Severe disease was defined as ≥3 relapses and/or a final Expanded Disability Status Scale (EDSS) score above 0. Because EDSS may underestimate disability in children, a sensitivity analysis was performed using ≥3 relapses alone to define severity. Clinical, biological, and radiological characteristics were compared between severe and non-severe cases to identify prognostic factors and construct a predictive model.
Results:
At the last follow-up, 49 of 70 patients (70 %) were classified as having severe POMS. Using the alternative definition based solely on relapse count, 32 patients (46 %) were considered severe. The main prognostic factors-shorter interval between the first two relapses, older age at onset, and higher lesion load on initial MRI-were consistent across both definitions, confirming the robustness of the model. The final predictive model included time between the first two attacks, age, sex, number of juxtacortical lesions, and CSF pleocytosis. The prognostic tree achieved a specificity of 89 % and 84 %, and a sensitivity of 76 % and 68 % under the two respective definitions.
Conclusion:
Specific clinical and radiological markers may allow early identification of severe POMS. Based on these markers, we developed a predictive model that could help identify high-risk patients and guide early, individualized treatment strategies. Validation in an independent cohort is warranted to confirm its clinical applicability.
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