Abstracts

Multivariate Prediction of Outcomes After Minimally Invasive Epilepsy Surgery

Abstract number : 2.279
Submission category : 9. Surgery / 9A. Adult
Year : 2022
Submission ID : 2205033
Source : www.aesnet.org
Presentation date : 12/4/2022 12:00:00 PM
Published date : Nov 22, 2022, 05:27 AM

Authors :
Adam Dickey, MD, PhD – Emory University; Katie Bullinger, MD, PhD – Emory University; Dayton Grogan, MD – Augusta University; Veeresh Shivamurthy, MD – Trinity Health of New England; Razan Faraj, BS – Emory University; Alex Greven, BS – Emory University; Daniel Drane, PhD – Emory University; Robert Gross, MD, PhD – Emory University

Rationale: Patients with drug-resistant temporal lobe epilepsy (TLE) should be considered for epilepsy surgery. Standard treatment for TLE is an anterior temporal lobectomy (ATL), but minimally invasive surgery has become a viable alternative, such as stereotactic laser amygdalohippocampotomy (SLAH). Predictors of surgery outcome after ATL have been identified, but less is known about predictors of outcome after SLAH. The most consistent predictor of good outcome after SLAH is the presence of mesial temporal sclerosis (MTS). Using a cohort of 101 patients who underwent SLAH at Emory University, we hypothesized that clinical variables other than MTS would independently predict outcomes.

Methods: Predictors of good outcome after ATL (Fitzgerald et al., 2021) include (1) pre-operative seizure frequency, (2) absence of generalized tonic-clonic seizures (GTCs), (3) presence of MTS, (4) unitemporal interictal epileptiform discharges (IEDs), and (5) well localized seizures. We collected these variables, as well as (6) unitemporal PET abnormality, (7) gender, (8) age of onset, and (9) age at surgery. We then used univariate logistic regression to see how well each clinical variable could predict surgical outcome (Engel I or not). We then compared two methods for performing multivariate logistic regression. First, we performed forward selection of the most significant variable, adding variables which remained significant (at p< 0.05).  Second, we performed backwards selection, removing the least significant variable and selecting the combination with the optimal Akaike Information Criterion (AIC)._x000D_
Surgery