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

Abstract

Objective:This study aims to evaluate the role of scalp electroencephalography (EEG; ictal and interictal patterns) in predicting resective epilepsy surgery outcomes. We use the data to further develop a nomogram to predict seizure freedom.Methods:We retrospectively reviewed the scalp EEG findings and clinical data of patients who underwent surgical resection at three epilepsy centers. Using both EEG and clinical variables categorized into 13 isolated candidate predictors and 6 interaction terms, we built a multivariable Cox proportional hazards model to predict seizure freedom 2 years after surgery. Harrell's step-down procedure was used to sequentially eliminate the least-informative variables from the model until the change in the concordance index (c-index) with variable removal was less than 0.01. We created a separate model using only clinical variables. Discrimination of the two models was compared to evaluate the role of scalp EEG in seizure-freedom prediction.Results:Four hundred seventy patient records were analyzed. Following internal validation, the full Clinical + EEG model achieved an optimism-corrected c-index of 0.65, whereas the c-index of the model without EEG data was 0.59. The presence of focal to bilateral tonic-clonic seizures (FBTCS), high preoperative seizure frequency, absence of hippocampal sclerosis, and presence of nonlocalizable seizures predicted worse outcome. The presence of FBTCS had the largest impact for predicting outcome. The analysis of the models' interactions showed that in patients with unilateral interictal epileptiform discharges (IEDs), temporal lobe surgery cases had a better outcome. In cases with bilateral IEDs, abnormal magnetic resonance imaging (MRI) predicted worse outcomes, and in cases without IEDs, patients with extratemporal epilepsy and abnormal MRI had better outcomes.

A prediction model integrating synchronization biomarkers and clinical features to identify responders to vagus nerve stimulation among pediatric patients with drug‐resistant epilepsy

“…Although the accuracy of validation cohort was lower than that of discovery cohort, the deviation was within tolerance, considering the limitation of sample size and heterogeneity of patients in two separate institutions. Furthermore, the application of SVM has been used in outcome prediction for epilepsy surgery, 40 patient‐specific seizure prediction, 41 epilepsy diagnosis, 42 and the identification of the epileptogenic zone. 43 With further enlargement of sample size and model optimization, the efficacy and generality of SVM prediction model would be ulteriorly improved to meet the need to identify responders to VNS in clinical practice.…”

“…We could tell from the selected principal component that children with DRE who had fewer current ASMs before implantation, multiple seizure types, and negative MRI findings were more likely to be VNS responders, which also places great emphasis on multidimensional combinations in individualized prediction.Although the accuracy of validation cohort was lower than that of discovery cohort, the deviation was within tolerance, considering the limitation of sample size and heterogeneity of patients in two separate institutions. Furthermore, the application of SVM has been used in outcome prediction for epilepsy surgery,40 patient-specific seizure prediction,41 epilepsy diagnosis,42 and the identification of the epileptogenic zone 43. With further enlargement of sample size and model optimization, the efficacy and generality of SVM prediction model would be ulteriorly improved to meet the need to identify responders to VNS in clinical practice.Our study had some limitations.…”

VEP for VIP Care: Patient-Specific Modeling in Epilepsy Surgery

“…9 Adding further clinical data points to the model may also be useful; one recent study demonstrated that a combination of electrographic and clinical data may improve performance in modeling surgical outcomes. 10 So, in surgical epilepsy, which is it: the focus or the network? Clearly, both concepts have merit, as focal epilepsy is a network disorder that can often be treated successfully by targeting critical nodes involved in seizure generation and propagation.…”

“… 9 Adding further clinical data points to the model may also be useful; one recent study demonstrated that a combination of electrographic and clinical data may improve performance in modeling surgical outcomes. 10 …”

Quantitative analysis of visually reviewed normal scalp EEG predicts seizure freedom following anterior temporal lobectomy

“…5,6 However, the potential value of quantitative analysis of normal interictal scalp EEG, that is, in the absence of interictal epileptiform abnormalities or focal slowing, remains unknown for such prognostication. Because nearly 50% of routine EEG studies and 10% of prolonged video-EEG studies in patients with known epilepsy do not contain any recognizable epileptiform activity, 7,8 the ability to predict ATL outcomes using normal scalp EEG findings could prove useful as a cost-effective and expeditious additional feature for evaluating surgical candidacy and predicting outcomes.…”