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  • Title: Early identification of seizure freedom with medical treatment in patients with mesial temporal lobe epilepsy and hippocampal sclerosis.
    Author: Cheval M, Houot M, Chastan N, Szurhaj W, Marchal C, Catenoix H, Valton L, Gavaret M, Herlin B, Biraben A, Lagarde S, Mazzola L, Minotti L, Maillard L, Dupont S.
    Journal: J Neurol; 2023 May; 270(5):2715-2723. PubMed ID: 36763175.
    Abstract:
    BACKGROUND: Mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) is usually associated with a poor response to antiseizure medications. We focused on MTLE-HS patients who were seizure free on medication to: (1) determine the clinical factors associated with seizure freedom and (2) develop a machine-learning classifier to better earlier identify those patients. METHODS: We performed a retrospective, multicentric study comparing 64 medically treated seizure-free MTLE-HS patients with 200 surgically treated drug-resistant MTLE-HS patients. First, we collected medical history and seizure semiology data. Then, we developed a machine-learning classifier based on clinical data. RESULTS: Medically treated seizure-free MTLE-HS patients were seizure-free for at least 2 years, and for a median time of 7 years at last follow-up. Compared to drug-resistant MTLE-HS patients, they exhibited: an older age at epilepsy onset (22.5 vs 8.0 years, p < 0.001), a lesser rate of: febrile seizures (39.0% vs 57.5%, p = 0.035), focal aware seizures (previously referred to as aura)(56.7% vs 90.0%, p < 0.001), autonomic focal aware seizures in presence of focal aware seizure (17.6% vs 59.4%, p < 0.001), dystonic posturing of the limbs (9.8% vs 47.0%, p < 0.001), gestural (27.4% vs 94.0%, p < 0.001), oro-alimentary (32.3% vs 75.5%, p < 0.001) or verbal automatisms (12.9% vs 36.0%, p = 0.001). The classifier had a positive predictive value of 0.889, a sensitivity of 0.727, a specificity of 0.962, a negative predictive value of 0.893. CONCLUSIONS: Medically treated seizure-free MTLE-HS patients exhibit a distinct clinical profile. A classifier built with readily available clinical data can identify them accurately with excellent positive predictive value. This may help to individualize the management of MTLE-HS patients according to their expected pharmacosensitivity.
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