A pharmacokinetic model of antiseizure medication load to guide care in the epilepsy monitoring unit

Ghosn, Nina J; Xie, Kevin; Pattnaik, Akash R; Gugger, James J; Ellis, Colin A; Sweeney, Elizabeth; Fox, Emily J.; Bernabei, John M.; Johnson, Jenaye; Boccanfuso, Jacqueline; Litt, Brian; Conrad, Erin C.

doi:10.1111/epi.17558

Cited by 5 publications

(1 citation statement)

References 35 publications

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“…One-tailed Spearman rank correlation was used to test the associations between epilepsy duration and patient-level severity, defined as the maximum seizure severity score across a patient's seizures. We converted the ASM load in patients for each day in the EMU to a continuous metric by applying a first-order pharmacokinetics model, thereby quantifying the average ASM load for up to one hour before seizure onset [36] (see supporting information). A linear mixed-effects model with ASM load as the fixed effect predictor, patient number as the random effect, and severity as the response was used to determine the effect of ASM load on severity.…”

Section: Discussionmentioning

confidence: 99%

The seizure severity score: a quantitative tool for comparing seizures and their response to therapy

Pattnaik,

Ghosn,

Ong

et al. 2023

J. Neural Eng.

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Objective: Epilepsy is a neurological disorder characterized by recurrent seizures which vary widely in severity, from clinically silent to prolonged convulsions. Measuring severity is crucial for guiding therapy, particularly when complete control is not possible. Seizure diaries, the current standard for guiding therapy, are insensitive to the duration of events or the propagation of seizure activity across the brain. We present a quantitative seizure severity score that incorporates EEG and clinical data and demonstrate how it can guide epilepsy therapies.Approach: We collected intracranial EEG and clinical semiology data from 54 epilepsy patients who had 256 seizures during invasive, in-hospital presurgical evaluation. We applied an absolute slope algorithm to EEG recordings to identify seizing channels. From this data, we developed a seizure severity score that combines seizure duration, spread, and semiology using non-negative matrix factorization. For validation, we assessed its correlation with independent measures of epilepsy burden: seizure types, epilepsy duration, a pharmacokinetic model of medication load, and response to epilepsy surgery. We investigated the association between the seizure severity score and pre-ictal network features.Main results: The seizure severity score augmented clinical classification by objectively delineating seizure duration and spread from recordings in available electrodes. Lower preictal medication loads were associated with higher seizure severity scores (p = 0.018, 97.5% CI = [-1.242, -0.116]) and lower pre-surgical severity was associated with better surgical outcome (p = 0.042). In 85% of patients with multiple seizure types, greater preictal change from baseline was associated with higher severity.Significance: We present a quantitative measure of seizure severity that includes EEG and clinical features, validated on gold standard in-patient recordings. We provide a framework for extending our tool’s utility to ambulatory EEG devices, for linking it to seizure semiology measured by wearable sensors, and as a tool to advance data-driven epilepsy care.

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Section: Discussionmentioning

confidence: 99%