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We generated parsimonious prediction models to quantify individual epilepsy risk between acute inpatient rehabilitation for individuals 2\u2009years after msTBI.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We used data from 6089 prospectively enrolled participants (\u226516\u2009years) in the TBI Model Systems National Database. Of these, 4126 individuals had complete seizure data collected over a 2\u2010year period post\u2010injury. We performed a case\u2010complete analysis to generate multiple prediction models using least absolute shrinkage and selection operator logistic regression. Baseline predictors were used to assess 2\u2010year seizure risk (Model 1). Then a 2\u2010year 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\u2010msTBI (Model 3) and predicting new seizures only during Year 2 (Model 4). We assessed model sensitivity when keeping specificity \u2265.60, area under the receiver\u2010operating characteristic curve (AUROC), and AUROC model performance through 5\u2010fold cross\u2010validation (CV).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Model 1 (73.8% men, 44.1\u2009\u00b1\u200919.7\u2009years, 76.1% moderate TBI) had a model sensitivity\u2009=\u200976.00% and average AUROC\u2009=\u2009.73\u2009\u00b1\u2009.02 in 5\u2010fold CV. Model 2 had a model sensitivity\u2009=\u200972.16% and average AUROC\u2009=\u2009.70\u2009\u00b1\u2009.02 in 5\u2010fold CV. Model 3 had a sensitivity\u2009=\u200986.63% and average AUROC\u2009=\u2009.84\u2009\u00b1\u2009.03 in 5\u2010fold CV. Model 4 had a sensitivity\u2009=\u200973.68% and average AUROC\u2009=\u2009.67\u2009\u00b1\u2009.03 in 5\u2010fold 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Significance<\/jats:title><jats:p>Using information available, acute\u2010care, and year\u20101 post\u2010injury data, parsimonious quantitative epilepsy prediction models following msTBI may facilitate timely evidence\u2010based PTE prognostication within a 2\u2010year period. We developed interactive web\u2010based 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.<\/jats:p><\/jats:sec>","DOI":"10.1111\/epi.18210","type":"journal-article","created":{"date-parts":[[2024,12,10]],"date-time":"2024-12-10T13:25:11Z","timestamp":1733837111000},"page":"482-498","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Development of individualized risk assessment models for predicting post\u2010traumatic epilepsy 1 and 2\u2009years after moderate\u2010to\u2010severe traumatic brain injury: A traumatic brain injury model system study"],"prefix":"10.1111","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6396-9095","authenticated-orcid":false,"given":"Nabil","family":"Awan","sequence":"first","affiliation":[{"name":"Department of Physical Medicine and Rehabilitation University of Pittsburgh  Pittsburgh Pennsylvania USA"},{"name":"Department of 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