Chen Y, Li S, Ge W, Jing J, Chen HY, Doherty D, Herman A, Kaleem S, Ding K, Osman G, Swisher CB, Smith C, Maciel CB, Alkhachroum A, Lee JW, Dhakar MB, Gilmore EJ, Sivaraju A, Hirsch LJ, Omay SB, Blumenfeld H, Sheth KN, Struck AF, Edlow BL, Westover MB, and Kim JA. Quantitative epileptiform burden and electroencephalography background features predict post-traumatic epilepsy. J Neurol Neurosurg Psychiatry 2022.
Journal of neurology, neurosurgery, and psychiatry
BACKGROUND: Post-traumatic epilepsy (PTE) is a severe complication of traumatic brain injury (TBI). Electroencephalography aids early post-traumatic seizure diagnosis, but its optimal utility for PTE prediction remains unknown. We aim to evaluate the contribution of quantitative electroencephalograms to predict first-year PTE (PTE(1)).
METHODS: We performed a multicentre, retrospective case-control study of patients with TBI. 63 PTE(1) patients were matched with 63 non-PTE(1) patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE(1) using logistic regressions and examined their predictive value relative to TBI mechanism and CT abnormalities.
RESULTS: In the matched cohort (n=126), greater epileptiform burden, suppression burden and beta variability were associated with 4.6 times higher PTE(1) risk based on multivariable logistic regression analysis (area under the receiver operating characteristic curve, AUC (95% CI) 0.69 (0.60 to 0.78)). Among 116 (92%) patients with available CT reports, adding quantitative electroencephalography features to a combined mechanism and CT model improved performance (AUC (95% CI), 0.71 (0.61 to 0.80) vs 0.61 (0.51 to 0.72)).
CONCLUSIONS: Epileptiform and spectral characteristics enhance covariates identified on TBI admission and CT abnormalities in PTE(1) prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.
ePub ahead of print