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 (PTE1).
Methods We performed a multicentre, retrospective case–control study of patients with TBI. 63 PTE1 patients were matched with 63 non-PTE1 patients by admission Glasgow Coma Scale score, age and sex. We evaluated the association of quantitative electroencephalography features with PTE1 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 PTE1 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 PTE1 prediction. Future trials should incorporate quantitative electroencephalography features to validate this enhancement of PTE risk stratification models.
- TRAUMATIC BRAIN INJURY
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Twitter @YilunChen_, @neurologyrules, @AAlkhachroum, @JennKimMDPHD
Contributors YC, JAK, MBW and EJG contributed to the study design. SL, HYC, DD, AH, SK, KD, GO, CBS, CS, CBM, AA, JWL, MBD, EJG, AS, LJH, SBO, HB, KNS, AFS, BLE, MBW and JAK contributed to data acquisition. YC, SL, WG, JJ, MBW and JAK contributed to data analysis. YC, JAK, EJG, HB and MBW contributed to drafting and revising the manuscript. All authors contributed to editing and approval of the manuscript.
Funding KD received funding from the National Institute on Aging (NIA) (R34AG061304) and the National Institute of Neurological Disorders and Stroke (NINDS) (R01NS117904) of the National Institutes of Health (NIH). CS and CBM acknowledge the University of Florida Integrated Data Repository (IDR) and the UF Health Office of the Chief Data Officer for providing the analytic data set for this project. CS and CBM were supported by the National Center for Advancing Translational Sciences (NCATS) of the NIH under University of Florida Clinical and Translational Science Awards (UL1TR000064, UL1TR001427). CBM received funding from the American Heart Association (AHA). AA is supported by NCATS of the NIH through an institutional KL2 Career Development Award from the Miami Clinical and Translational Science Institute (UL1TR002736). MBD received funding from the NINDS of the NIH and the American Epilepsy Society. EJG received funding from NIH (R01NS117904). AFS received funding from the NINDS under the NIH (R01NS111022) and Ceribell. BE received funding from the NINDS (R21NS109627, RF1NS115268) and the Office of the Director (DP2HD101400) of the NIH, the James S. McDonnell Foundation, and the Tiny Blue Dot Foundation. MBW received funding from the Glenn Foundation for Medical Research, the American Federation for Aging Research (Breakthroughs in Gerontology), the American Academy of Sleep Medicine Strategic Research Award, and the NINDS (R01NS102190, R01NS102574, R01NS107291) and the NIA (RF1AG064312, R01AG062989, R01AG073410) of the NIH. JAK received funding from the NINDS (R25N065743, K23NS112596-01A1, R01NS117904), the American Academy of Neurology Clinical Research Training Scholarship, the AHA and the Bee Foundation.
Competing interests None declared.
Provenance and peer review Not commissioned; externally peer reviewed.
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