Epilepsy Risk Prediction Model for Patients With Tuberous Sclerosis Complex

Abstract

Background: Individuals with tuberous sclerosis complex are at increased risk of epilepsy. Early seizure control improves developmental outcomes, making identifying at-risk patients critically important. Despite several identified risk factors, it remains difficult to predict. The purpose of the study was to evaluate the combined risk prediction of previously identified risk factors for epilepsy in individuals with tuberous sclerosis complex. Methods: The study group (n = 333) consisted of individuals with tuberous sclerosis complex who were enrolled in the Tuberous Sclerosis Complex Autism Center of Excellence Research Network and UT TSC Biobank. The outcome was defined as having an epilepsy diagnosis. Potential risk factors included sex, TSC genotype, and tuber presence. Logistic regression was used to calculate the odds ratio and P value for the association between each variable and epilepsy. A clinical risk prediction model incorporating all risk factors was built. Area under the curve was calculated to characterize the full model's ability to discriminate individuals with tuberous sclerosis complex with and without epilepsy. Results: The strongest risk for epilepsy was presence of tubers (95% confidence interval: 2.39 to 10.89). Individuals with pathogenic TSC2 variants were three times more likely (95% confidence interval: 1.55 to 6.36) to develop seizures compared with those with tuberous sclerosis complex from other causes. The combination of risk factors resulted in an area under the curve 0.73. Conclusions: Simple characteristics of patients with tuberous sclerosis complex can be combined to successfully predict epilepsy risk. A risk assessment model that incorporates sex, TSC genotype, protective TSC2 missense variant, and tuber presence correctly predicts epilepsy in 73% of patients with tuberous sclerosis complex.