Prediction of seizure risk after repetitive mild traumatic brain injury in childhood.

OBJECTIVE: Despite the known negative physiological impact of repeated mild head trauma events, their multiplicative impact on long-term seizure risk remains unclear. The objective of this study was to evaluate how multiple mild traumatic brain injuries (mTBIs) impact long-term seizure risk by testing 3 distinct machine learning approaches. Baseline and injury-specific characteristics were incorporated to enhance prognostication of individual seizure risk. METHODS: Children with at least 1 mTBI event without prior evidence of seizure or antiepileptic drug treatment, from 2003 to 2021, were identified from a nationally sourced administrative claims database. The primary outcome of interest was a seizure event after mTBI, defined by qualifying principal diagnosis codes. Time-varying multivariable Cox regression was used to assess the impact of repeated mTBI. RESULTS: A total of 156,118 children (mean age 11.7 ± 4.7 years) were included, with a median follow-up duration of 22.6 months (IQR 9.2-45.4 months). Among patients who experienced seizure after mTBI, the median time to seizure was 306 days. Seizures among those with radiographic findings and/or loss of consciousness occurred earlier (median time to seizure 112.5 days [imaging findings only, IQR 5-526.25 days], 80 days [loss of consciousness only, IQR 7-652 days], 22 days [both, IQR 5-192 days]). Both mTBI without and with short-term loss of consciousness resulted in increasing seizure risk with repeated trauma (HR 1.196, 95% CI 1.082-1.322; HR 2.025, 95% CI 1.828-2.244; respectively). The random survival forest approach achieved fixed-time areas under the receiver operating characteristic curve of 0.780 and 0.777 at 30 and 90 days after mTBI, and children predicted at high risk by the final model experienced a significantly higher burden of early seizure after mTBI (46.7% within the first 30 days vs 17.7% and 19.9% of children at low and medium risk). A simplified model using the top 12 contributing features achieved 95% of the full model's performance in the validation set. CONCLUSIONS: A novel machine learning model was developed and validated for personalized prediction of long-term seizure risk following multiple mTBIs. Model performance remained robust with a limited feature set, suggesting the feasibility of real-time incorporation into clinical workflows for individualized prognostication following each repeat mTBI event. In children predicted to be at high risk, early intervention should be considered.

Duke Scholars

Author Gerald Arthur Grant Neurosurgery