John Libbey Eurotext

Predictive factors for a good prognosis following surgery for temporal lobe epilepsy: a cohort study in Spain Volume 13, issue 1, Mars 2011

Medical-Surgical Epilepsy Unit, Department of Neurology, Clinical Epidemiology Unit, Neurophysiology Service, Department of Neurosurgery, Department of Neuroradiology, Department of Neuropsychology, Hospital de Cruces, Baracaldo, Spain

To investigate the outcome of temporal lobe epilepsy surgery and identify the variables which predict a good prognosis with respect to seizures in postoperative follow-up after two and four years. This retrospective study included 115 selected patients who underwent surgery for temporal lobe epilepsy between 1996 and 2007. In the second year after surgery 86.1% of patients had a good prognosis for seizure control (73.9% Engel class I and 12.2% Engel class II) and 89.2% (76.3% Engel class I and 12.9% Engel class II) in the fourth year. Sixty-four of 93 (68.8%) patients were free of disabling seizures (Engel class I) during the entire period and 78 (83.8%) had good prognosis (Engel class I and II). For the second year, logistic regression analysis revealed the following variables to be independently predictive of good seizure control: absence of two or more seizure episodes in the first year after surgery, normal postoperative video-EEG, and age at surgery of less than 35 years. In the fourth year, mesial temporal sclerosis, female sex and normal postoperative video-EEG were the predictive factors. For the group with a good prognosis in both the second and the fourth year, the predictive variables were: absence of two or more seizure episodes in the first year after surgery (OR: 13.762, CI 95%: 2.566-73.808, p<0.002) and normal postoperative video-EEG (OR: 16.301, CI 95%: 3.704-71.740, p<0.001). This study illustrates the sustained benefit of temporal lobe epilepsy surgery. The multivariate logistic regression analysis failed to identify a good predictive model composed of preoperative variables alone, although it was possible to build such a model with either pre- and postoperative variables or only postoperative variables.