Abstract The current definition of epilepsy allows diagnosis after a single unprovoked seizure if the estimated 10‐year recurrence risk is ≥60%. While this framework is grounded in epidemiological evidence, it does not align with the shorter time horizons that guide many clinical and personal decisions. In acquired epilepsies, such as those following stroke, traumatic brain injury, or CNS infections, most recurrences occur within 1–2 years, with risk declining sharply thereafter. This temporal clustering challenges the use of static, long‐term risk thresholds in isolation. Dynamic tools, such as the Chance of an Occurrence of a Seizure in the Next Year (COSY) and validated prognostic models (e.g., SeLECT, CAVE, RISE), offer recalculable, near‐term estimates that reflect evolving patient status. These metrics can improve communication, inform treatment thresholds through Number Needed to Treat (NNT) calculations, and enhance clinical trial recruitment by targeting periods of highest risk. However, barriers remain, including limited integration into guidelines, gaps in external validation, and the “Oedipus effect,” where probabilistic predictions influence patient behavior, treatment decisions, and research outcomes. Incorporating individualized, time‐sensitive risk prediction into clinical frameworks may better align diagnostic definitions with patient needs, reduce overtreatment, and optimize both everyday care and research in epilepsy prevention and management.
Schubert et al. (2025) studied this question.