Improving the prediction of epilepsy surgery outcomes using basic scalp EEG findings

Area of Science:

• Epilepsy research
• Neurosurgery
• Clinical neurology

Background:

• Predicting outcomes of epilepsy surgery is crucial for patient management.
• Scalp electroencephalography (EEG) provides valuable insights into seizure activity.
• Developing accurate predictive models can guide surgical decisions and improve patient prognosis.

Purpose of the Study:

• To evaluate the predictive value of scalp EEG (ictal and interictal patterns) for resective epilepsy surgery outcomes.
• To develop and validate a nomogram for predicting seizure freedom after epilepsy surgery.
• To compare the predictive performance of models with and without EEG data.

Main Methods:

• Retrospective analysis of scalp EEG findings and clinical data from 470 epilepsy surgery patients across three centers.
• Development of a multivariable Cox proportional hazards model incorporating clinical and EEG variables.
• Internal validation and comparison of models with and without EEG data using concordance index (c-index).

Main Results:

• The combined Clinical + EEG model achieved a higher optimism-corrected c-index (0.65) compared to the clinical-only model (0.59).
• Predictors of worse surgical outcomes included focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, and nonlocalizable seizures.
• Interactions revealed better outcomes for temporal lobe surgery in patients with unilateral interictal epileptiform discharges (IEDs).

Conclusions:

• Scalp EEG, particularly IEDs, plays a significant role in predicting epilepsy surgery outcomes.
• The developed nomogram offers individualized predictions of postoperative seizure freedom based on preoperative findings.
• Integrating EEG data enhances the accuracy of predicting surgical success in epilepsy patients.