Multivariable prediction of seizure outcome one year after resective epilepsy surgery: development of a model with independent validation

Abstract

Purpose: To identify predictors of seizure-outcome after epilepsy surgery and validate the findings in an independent series of patients. To use the results to develop a predictive model.

Methods: Sequential patients undergoing resective surgery for medically intractable epilepsy were identified at Yale New Haven Hospital (1987-1990, group 1) and Columbia Presbyterian Hospital (1991-1994, group 2). Information about seizure outcome and predictors of outcome was obtained from medical chart review. Good seizure-outcome was defined as having been seizure-free for one year beginning with discharge from the hospital. Multiple logistic regression was used to develop a model of predictors in group 1. It was then validated in group 2.

Results: There were 133 patients in group 1 and 81 in group 2. In a multivariable analysis, independent predictors of outcome in group 1 were presence of mesial temporal sclerosis based on postsurgical pathological analysis (MTS) (relative risk (RR) = 1.47), having a known underlying etiology (RR = 1.32), and partial seizures only (RR = 1.17). In group 2, the findings for each factor were similar to those in group 1: MTS, RR = 1.49; etiology, RR = 1.32; and partial seizures, RR = 1.24. Used in combination, these three factors can identify patients with nearly a 100% chance of being seizure-free (all three factors present) versus less than a 50% chance (none of the three factors present).

Conclusions: With independent validation of the findings, we can be reasonably certain that the three factors identified in this analysis are meaningful and generalizable predictors of seizure outcome following epilepsy surgery. Use of predictive models should be considered in future studies to convert study results into clinically relevant statements about a particular patient's likelihood of surgical success.