ImportanceSelection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the “right drugs” are prescribed.ObjectiveTo develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients.Design, Setting, and ParticipantsThis cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables.ExposuresOne of 7 antiseizure medications.Main Outcomes and MeasuresWith the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models.ResultsThe final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC.Conclusions and RelevanceIn this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.

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Aberrantly synchronized neuronal discharges in the brain lead to epilepsy, a devastating neurological disease whose pathogenesis and mechanism are unclear. SAPAP3, a cytoskeletal protein expressed at high levels in the postsynaptic density (PSD) of excitatory synapses, has been well studied in the striatum, but the role of SAPAP3 in epilepsy remains elusive. In this study, we sought to investigate the molecular, cellular, electrophysiological and behavioral consequences of SAPAP3 perturbations in the mouse hippocampus. We identified a significant increase in the SAPAP3 levels in patients with temporal lobe epilepsy (TLE) and in mouse models of epilepsy. In addition, behavioral studies showed that the downregulation of SAPAP3 by shRNA decreased the seizure severity and that the overexpression of SAPAP3 by recombinant SAPAP3 yielded the opposite effect. Moreover, SAPAP3 affected action potentials (APs), miniature excitatory postsynaptic currents (mEPSCs) and N-methyl-D-aspartate receptor (NMDAR)-mediated currents in the CA1 region, which indicated that SAPAP3 plays an important role in excitatory synaptic transmission. Additionally, the levels of the GluN2A protein, which is involved in synaptic function, were perturbed in the hippocampal PSD, and this perturbation was accompanied by ultrastructural morphological changes. These results revealed a previously unknown function of SAPAP3 in epileptogenesis and showed that SAPAP3 may represent a novel target for the treatment of epilepsy.

Epilepsy patients may still have seizures after surgery, and there have been few studies on the response to antiepileptic drugs (AEDs) after surgery failure. The purpose of this study was to analyze the response to AEDs after unsuccessful epilepsy surgery. Methods: Patients who underwent unsuccessful epilepsy surgery between January 1999 and January 2019 were evaluated. Patient demographics, etiology, factors related to surgery and AED use patterns were assessed.Results: After excluding the 5 patients who were lost to follow-up and the 2 patients who died, the records of 103 consecutive patients were analyzed. Ninety patients (87.4 %) had seizure recurrence within one year after surgery, 2 (1.9 %) patients had recurrence from one year to two years after surgery, and 11 (10.7 %) patients had recurrence two or more years after surgery (2-10 years). After surgery failure, the patients tried at least 2 kinds of AEDs with different mechanisms for more than 2 years. The average total number of AEDs used was 5.97, the average number of AEDs used before surgery was 3.21, and the average number of AEDs used after surgery was 4.02. After retreatment with AEDs, 10 patients (9.7 %) were seizure-free, 18 patients' (17.5 %) seizures were alleviated, and 75 patients (72.8 %) had seizures as they did prior to the adjustments. The number of AEDs used before and after surgery and the total number of AEDs were not significantly different among the seizure free group, alleviated seizure group and no change group. There were no significant differences in seizure onset age, surgery age, etiology, time between seizure onset and surgery, magnetic resonance imaging, seizure type, localization and lateralization of the surgery site among the three groups. Conclusions: The results showed that a small percentage of patients (27.2 %) who undergo unsuccessful epilepsy surgery benefit from AED adjustments; however, the vast majority of patients (72.8 %) do not benefit from AED adjustments.

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