ObjectiveAlthough traumatic brain injury (TBI) and posttraumatic epilepsy (PTE) are common, there are no prospective models quantifying individual epilepsy risk after moderate‐to‐severe TBI (msTBI). We generated parsimonious prediction models to quantify individual epilepsy risk between acute inpatient rehabilitation for individuals 2 years after msTBI.MethodsWe used data from 6089 prospectively enrolled participants (≥16 years) in the TBI Model Systems National Database. Of these, 4126 individuals had complete seizure data collected over a 2‐year period post‐injury. We performed a case‐complete analysis to generate multiple prediction models using least absolute shrinkage and selection operator logistic regression. Baseline predictors were used to assess 2‐year seizure risk (Model 1). Then a 2‐year seizure risk was assessed excluding the acute care variables (Model 2). In addition, we generated prognostic models predicting new/recurrent seizures during Year 2 post‐msTBI (Model 3) and predicting new seizures only during Year 2 (Model 4). We assessed model sensitivity when keeping specificity ≥.60, area under the receiver‐operating characteristic curve (AUROC), and AUROC model performance through 5‐fold cross‐validation (CV).ResultsModel 1 (73.8% men, 44.1 ± 19.7 years, 76.1% moderate TBI) had a model sensitivity = 76.00% and average AUROC = .73 ± .02 in 5‐fold CV. Model 2 had a model sensitivity = 72.16% and average AUROC = .70 ± .02 in 5‐fold CV. Model 3 had a sensitivity = 86.63% and average AUROC = .84 ± .03 in 5‐fold CV. Model 4 had a sensitivity = 73.68% and average AUROC = .67 ± .03 in 5‐fold CV. Cranial surgeries, acute care seizures, intracranial fragments, and traumatic hemorrhages were consistent predictors across all models. Demographic and mental health variables contributed to some models. Simulated, clinical examples model individual PTE predictions.SignificanceUsing information available, acute‐care, and year‐1 post‐injury data, parsimonious quantitative epilepsy prediction models following msTBI may facilitate timely evidence‐based PTE prognostication within a 2‐year period. We developed interactive web‐based tools for testing prediction model external validity among independent cohorts. Individualized PTE risk may inform clinical trial development/design and clinical decision support tools for this population.
