Długoterminowe przewidywanie epilepsji po urazie mózgu u weteranów za pomocą rutynowych danych klinicznych

OBJECTIVE: Despite elevated risk for epilepsy following traumatic brain injury (TBI), there are limited tools to assess epilepsy risk following TBI using routine clinical data. The objective of this study was to develop and validate a machine learning approach to predict the onset of posttraumatic epilepsy (PTE) over varying time horizons following TBI, using only routine clinical data collected up to the month of TBI documentation. METHODS: This retrospective longitudinal cohort study included post-9/11 US veterans with a TBI diagnosis between 2008 and 2017 in US Department of War and Veterans Health Administration records. Machine learning models predicted PTE onset at 2, 5, and 10 years after the date of first TBI documentation. Only preinjury information was used for prediction. Model performance was evaluated on held-out test data (30%). Shapley additive explanations (SHAP) were used to quantify the contribution of predictive features. RESULTS: The cohort of 107 987 US veterans with TBI included 4930 (4.6%) incident epilepsy cases. Predicting epilepsy status, an optimized random forest model achieved area under curve [95% confidence interval] scores from receiver operating characteristic curves of .75 [.73-.77], .74 [.72-.75], and .73 [.72-.74] for 2-, 5-, and 10-year forecast windows postinjury, respectively. High-risk stratification identified 17.5% of all 5-year epilepsy cases at a false positive rate of just 2.3% in the TBI population. SHAP analysis identified TBI severity and a cumulative preinjury comorbidity index as important predictors of PTE risk. SIGNIFICANCE: Machine learning algorithms applied to routinely collected administrative health data can effectively stratify long-term risk for epilepsy following TBI over a wide range of time horizons. These models open the possibility for enrichment of PTE cases in future preventative clinical trials using widely available routine clinical data.