Association between structural brain MRI abnormalities and epilepsy in older adults.

OBJECTIVE: To determine the association between brain MRI abnormalities and incident epilepsy in older adults. METHODS: Men and women (ages 45-64 years) from the Atherosclerosis Risk in Communities study were followed up from 1987 to 2018 with brain MRI performed between 2011 and 2013. We identified cases of incident late-onset epilepsy (LOE) with onset of seizures occurring after the acquisition of brain MRI. We evaluated the relative pattern of cortical thickness, subcortical volume, and white matter integrity among participants with incident LOE after MRI in comparison with participants without seizures. We examined the association between MRI abnormalities and incident LOE using Cox proportional hazards regression. Models were adjusted for demographics, hypertension, diabetes, smoking, stroke, and dementia status. RESULTS: Among 1251 participants with brain MRI data, 27 (2.2%) developed LOE after MRI over a median of 6.4 years (25-75 percentile 5.8-6.9) of follow-up. Participants with incident LOE after MRI had higher levels of cortical thinning and white matter microstructural abnormalities before seizure onset compared to those without seizures. In longitudinal analyses, greater number of abnormalities was associated with incident LOE after controlling for demographic factors, risk factors for cardiovascular disease, stroke, and dementia (gray matter: hazard ratio [HR]: 2.3, 95% confidence interval [CI]: 1.0-4.9; white matter diffusivity: HR: 3.0, 95% CI: 1.2-7.3). INTERPRETATION: This study demonstrates considerable gray and white matter pathology among individuals with LOE, which is present prior to the onset of seizures and provides important insights into the role of neurodegeneration, both of gray and white matter, and the risk of LOE.

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

OBJECTIVE: Evaluating patients with drug-resistant epilepsy often requires inducing seizures by tapering antiseizure medications (ASMs) in the epilepsy monitoring unit (EMU). The relationship between ASM taper strategy, seizure timing, and severity remains unclear. In this study, we developed and validated a pharmacokinetic model of total ASM load and tested its association with seizure occurrence and severity in the EMU. METHODS: We studied 80 patients who underwent intracranial electroencephalographic recording for epilepsy surgery planning. We developed a first order pharmacokinetic model of the ASMs administered in the EMU to generate a continuous metric of overall ASM load. We then related modeled ASM load to seizure likelihood and severity. We determined the association between the rate of ASM load reduction, the length of hospital stay, and the probability of having a severe seizure. Finally, we used modeled ASM load to predict oncoming seizures. RESULTS: Seizures occurred in the bottom 50th percentile of sampled ASM loads across the cohort (p < .0001, Wilcoxon signed-rank test), and seizures requiring rescue therapy occurred at lower ASM loads than seizures that did not require rescue therapy (logistic regression mixed effects model, odds ratio = .27, p = .01). Greater ASM decrease early in the EMU was not associated with an increased likelihood of having a severe seizure, nor with a shorter length of stay. SIGNIFICANCE: A pharmacokinetic model can accurately estimate ASM levels for patients in the EMU. Lower modeled ASM levels are associated with increased seizure likelihood and seizure severity. We show that ASM load, rather than ASM taper speed, is associated with severe seizures. ASM modeling has the potential to help optimize taper strategy to minimize severe seizures while maximizing diagnostic yield.