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Literature review and protocol for a prospective multicentre cohort study on multimodal prediction of seizure
Brooke C Beattie1, Karla Batista García-Ramó1,2, Krista Biggs3
1Centre for Neuroscience Studies, Queen's University, Kingston, Ontario, Canada.
This study aims to predict epilepsy recurrence after a first seizure using a multimodal biomarker model. Identifying high-risk individuals will guide personalized treatment for better seizure management.
Area of Science:
- Neurology
- Biomarker Research
- Machine Learning
Background:
- Epilepsy is a common neurological disorder with recurrent seizures.
- Nearly half of patients with an unprovoked first seizure (UFS) develop epilepsy.
- Current models lack predictive power for seizure recurrence risk in UFS patients.
Purpose of the Study:
- To develop the first multimodal biomarker-based predictive model for seizure recurrence in adults with UFS.
- To identify individuals at higher risk of recurrence to guide treatment decisions.
- To investigate alterations in cognition, mood, and brain connectivity in UFS patients.
Main Methods:
- Recruiting 200 patients with UFS and 75 healthy controls (aged 18-65).
- Conducting neuropsychological assessments, MRI (structural and functional), and electroencephalography.
- Prospectively assessing seizure recurrence with follow-ups at 3, 6, 9, and 12 months.
- Training a multimodal machine-learning model to predict 12-month seizure recurrence.
Main Results:
- Comparisons between UFS patients and controls, and between recurrent and non-recurrent UFS patients.
- Development of a predictive model for seizure recurrence.
- Identification of key biomarkers associated with epilepsy development.
Conclusions:
- Establishing a novel predictive model for seizure recurrence in UFS.
- Enabling personalized treatment strategies for epilepsy.
- Advancing understanding of UFS pathophysiology through multimodal biomarkers.
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