A quantitative tool for seizure severity: diagnostic and therapeutic applications

Abstract

AbstractObjectiveMore than one-third of the people with focal epilepsy do not achieve seizure freedom with medication, neuromodulation, or neurosurgery therapies. Palliative care with the goal of reducing epilepsy burden is an alternative for these patients. Minimizing severe seizures is essential for reducing morbidity. Existing seizure severity scales are qualitative and rely on patient reports, limiting our ability to rigorously track and intervene to curb severe seizures. The goal of this study is to develop and validate a quantitative metric for seizure severity.MethodsWe retrospectively analyzed preictal and ictal intracranial-EEG (iEEG) recordings from 54 people with drug-resistant epilepsy undergoing pre-surgical evaluation. We developed a new metric that objectively combines seizure duration, spread, and semiology to quantify seizure severity. We calculated preictal iEEG network features and fit a linear mixed-effects model to quantify patient-specific associations between preictal networks and seizure severity.ResultsWe evaluated 256 seizures from 54 patients using the quantitative seizure severity score. Seizure severity was consistent with clinical seizure type. Medication taper strategy was associated with seizure severity (p = 0.018, 97.5% confidence interval = [-1.242, -0.116]) and lower pre-surgical seizure severity was associated with better post-surgical seizure outcome (U = 465,p= 0.042). A linear mixed-effects model with preictal network features as regressors and seizure severity as response revealed a group-level positive trend. In 12 out of 14 patients with multiple types of seizures, more severe seizures were preceded by more abnormal preictal networks.SignificanceWe present a quantitative metric for seizure severity that correlates with clinical and electrographic features. We found that the seizure severity score was associated with abnormal preictal networks. We propose this measure to holistically capture patient condition and guide incremental changes in therapy to improve patient outcome over time.