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Data Driven Approaches for Optimizing Antiseizure Medication Management in Epilepsy Nina Jasmine Ghosn
- Format:
- Book
- Thesis/Dissertation
- Author/Creator:
- Ghosn, Nina Jasmine, author.
- Language:
- English
- Subjects (All):
- Bioengineering.
- Neurosciences.
- Pharmaceutical sciences.
- 0202.
- 0317.
- 0572.
- Local Subjects:
- Bioengineering.
- Neurosciences.
- Pharmaceutical sciences.
- 0202.
- 0317.
- 0572.
- Physical Description:
- 1 electronic resource (177 pages)
- Contained In:
- Dissertations Abstracts International 86-07B
- Place of Publication:
- Ann Arbor : ProQuest Dissertations and Theses, 2024
- Language Note:
- English
- Summary:
- Epilepsy affects 50 million people worldwide, and only one third are seizure free with anti-seizure medication (ASM) therapy. For this group of people, a missed dose of medication is the leading cause of breakthrough seizures, and for people with epilepsy who are not seizure-free on ASM therapy, ASMs are the primary mode of seizure management. Thus, it is important to understand the relationship between ASMs and seizures, as well as ASMs and seizure risk, in order to improve epilepsy management and seizure induction strategies using medication for epilepsy diagnosis. Patients with drug-resistant epilepsy often undergo evaluation in the epilepsy monitoring unit (EMU) where ASM tapering is used to induce seizures(1). However, this process is not standard, and may pose safety risks by triggering adverse events such as convulsions or prolonged seizures(2,3). This setting also provides an ideal environment to investigate biomarkers of seizure risk due to decreased ASM levels; the ability to detect changes in interictal biomarkers that reflect ASM load may provide a controllable signal to manage seizure risk following missed medications in patients with implantable recording devices. Previous work has investigated the relationship between ASM taper, seizure severity, and EEG biomarkers independently, but has neglected the pharmacokinetic properties of each ASM and the effects of baseline patient characteristics. I have addressed these gaps in three aims. First, I have developed and validated a robust model of ASM load that considers the specific pharmacokinetic profiles of individual medications and determined the relationship between ASM load and seizure timing and severity to establish the connection between ASM load and seizure risk. Second, I applied this model to investigate candidate intracranial EEG (iEEG) biomarkers that reflect changes in ASM load, specifically to detect low ASM loads. Third, I have leveraged a larger dataset of patients undergoing scalp EEG monitoring to determine the optimal taper strategy for inducing safe, diagnostically useful seizures for epilepsy localization
- Notes:
- Source: Dissertations Abstracts International, Volume: 86-07, Section: B.
- Advisors: Litt, Brian; Vitale, Flavia Committee members: Conrad, Erin; Wagenaar, Joost; Fox, Emily
- Ph.D. University of Pennsylvania 2024
- Local Notes:
- School code: 0175
- ISBN:
- 9798302183811
- Access Restriction:
- Restricted for use by site license
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