Individualised prediction of drug resistance and seizure recurrence after medication withdrawal in people with juvenile myoclonic epilepsy: A systematic review and individual participant data meta-analysis.

Background: A third of people with juvenile myoclonic epilepsy (JME) are drug-resistant. Three-quarters have a seizure relapse when attempting to withdraw anti-seizure medication (ASM) after achieving seizure-freedom. It is currently impossible to predict who is likely to become drug-resistant and safely withdraw treatment. We aimed to identify predictors of drug resistance and seizure recurrence to allow for individualised prediction of treatment outcomes in people with JME. Methods: We performed an individual participant data (IPD) meta-analysis based on a systematic search in EMBASE and PubMed - last updated on March 11, 2021 - including prospective and retrospective observational studies reporting on treatment outcomes of people diagnosed with JME and available seizure outcome data after a minimum one-year follow-up. We invited authors to share standardised IPD to identify predictors of drug resistance using multivariable logistic regression. We excluded pseudo-resistant individuals. A subset who attempted to withdraw ASM was included in a multivariable proportional hazards analysis on seizure recurrence after ASM withdrawal. The study was registered at the Open Science Framework (OSF; https://osf.io/b9zjc/). Findings: Our search yielded 1641 articles; 53 were eligible, of which the authors of 24 studies agreed to collaborate by sharing IPD. Using data from 2518 people with JME, we found nine independent predictors of drug resistance: three seizure types, psychiatric comorbidities, catamenial epilepsy, epileptiform focality, ethnicity, history of CAE, family history of epilepsy, status epilepticus, and febrile seizures. Internal-external cross-validation of our multivariable model showed an area under the receiver operating characteristic curve of 0·70 (95%CI 0·68-0·72). Recurrence of seizures after ASM withdrawal (n = 368) was predicted by an earlier age at the start of withdrawal, shorter seizure-free interval and more currently used ASMs, resulting in an average internal-external cross-validation concordance-statistic of 0·70 (95%CI 0·68-0·73). Interpretation: We were able to predict and validate clinically relevant personalised treatment outcomes for people with JME. Individualised predictions are accessible as nomograms and web-based tools. Funding: MING fonds.

Implementing the SCORE system improves the quality of clinical EEG reading.

Objective: To assess whether implementing the freeware version of the SCORE EEG system (Standardized Computer-based Organized Reporting of EEG) leads to improvement in the quality of clinical EEG reading, and whether EEG reports in SCORE EEG are understood and accepted by the referring physicians. Methods: We generated EEG reports in the conventional, free-text style and then using SCORE EEG, in consecutive patients referred to routine EEG. We used the Georgian translation in the SCORE EEG Free Edition. We pre-defined quality indicators consisting of a list of 24 key features that need to be addressed in EEG reports. We compared these quality indicators in free-text reports with SCORE EEG. In addition, EEG reports in SCORE EEG format were assessed by ten referring physicians, who evaluated their usability on a 7-point Likert scale. Results: We included and evaluated EEG reports from 157 patients (80 female; age: 1-75 years; median: 28 years). Fourteen features were reported exclusively in SCORE EEG, four were reported significantly more often in SCORE EEG than in free-text format, and six features were reported equally often in SCORE EEG and in free-text format. Usability aspects of SCORE EEG were highly rated by the referring physicians (median 6-7 on the 7-point Likert scale). Conclusions: The structured system of EEG reporting in SCORE EEG helped the experts reading clinical EEG to cover the important aspects and increase the quality of clinical EEG reports. Significance: Implementing the freeware version of SCORE EEG in underprivileged areas will help improving management of patients with epilepsy.

Automated detection of absence seizures using a wearable electroencephalographic device: a phase 3 validation study and feasibility of automated behavioral testing.

OBJECTIVE: Our primary goal was to measure the accuracy of fully automated absence seizure detection, using a wearable electroencephalographic (EEG) device. As a secondary goal, we also tested the feasibility of automated behavioral testing triggered by the automated detection. METHODS: We conducted a phase 3 clinical trial (NCT04615442), with a prospective, multicenter, blinded study design. The input was the one-channel EEG recorded with dry electrodes embedded into a wearable headband device connected to a smartphone. The seizure detection algorithm was developed using artificial intelligence (convolutional neural networks). During the study, the predefined algorithm, with predefined cutoff value, analyzed the EEG in real time. The gold standard was derived from expert evaluation of simultaneously recorded full-array video-EEGs. In addition, we evaluated the patients' responsiveness to the automated alarms on the smartphone, and we compared it with the behavioral changes observed in the clinical video-EEGs. RESULTS: We recorded 102 consecutive patients (57 female, median age = 10 years) on suspicion of absence seizures. We recorded 364 absence seizures in 39 patients. Device deficiency was 4.67%, with a total recording time of 309 h. Average sensitivity per patient was 78.83% (95% confidence interval [CI] = 69.56%-88.11%), and median sensitivity was 92.90% (interquartile range [IQR] = 66.7%-100%). The average false detection rate was .53/h (95% CI = .32-.74). Most patients (n = 66, 64.71%) did not have any false alarms. The median F1 score per patient was .823 (IQR = .57-1). For the total recording duration, F1 score was .74. We assessed the feasibility of automated behavioral testing in 36 seizures; it correctly documented nonresponsiveness in 30 absence seizures, and responsiveness in six electrographic seizures. SIGNIFICANCE: Automated detection of absence seizures with a wearable device will improve seizure quantification and will promote assessment of patients in their home environment. Linking automated seizure detection to automated behavioral testing will provide valuable information from wearable devices.

Focal EEG features and therapeutic response in patients with juvenile absence and myoclonic epilepsy.

OBJECTIVE: To investigate the characteristics of focal EEG features in patients with juvenile absence epilepsy (JAE) and juvenile myoclonic epilepsy (JME), and to assess their possible influence on therapeutic response. METHODS: Focal EEG features were prospectively scored in 168 consecutive patients. Ninety-six patients were drug-naïve and 72 patients were already on antiepileptic drugs (AEDs): 38 on adequate medication and 34 on inadequate medication. Therapeutic response was assessed one year after starting adequate therapy. RESULTS: One-hundred-eighteen patients (70.2%) had focal EEG features: 89 patients (53%) had focal epileptiform discharges, and 80 patients (47.6%) had focal slowing. Most often, these were multifocal and localized in frontal and temporal regions. Among patients already on AEDs, patients with focal EEG features were more often treated with inadequate medication due to misdiagnosis, than patients without focal features. Data on therapeutic response were available for 118 patients; most of them (90.7%) were seizure free. None of the focal EEG features affected therapeutic response. CONCLUSION: Focal EEG features are common in patients with JME and JAE, but they do not influence the therapeutic response. SIGNIFICANCE: It is important that physicians are aware of the focal EEG features in order to avoid misdiagnosis and inadequate therapy.

Perspectives of epilepsy surgery in resource-poor countries: a study in Georgia.

BACKGROUND: To identify patients with concordant seizure semiology, interictal epileptiform discharges on standard EEG and brain MRI changes to define the patients with pharmacoresistant epilepsy (PRE) who would be suitable for epilepsy surgery according to non-invasive protocol. METHODS: The medical records of the patients with epilepsy seen in Epilepsy Center of Institute of Neurology and Neuropsychology (ECINN) (Tbilisi, Georgia) were reviewed retrospectively. The diagnostic work-up included neurological examination, standard EEG, and MRI. The degree of concordance of the seizure semiology, EEG, and neuroimaging was used to determine the potential candidates for surgery. The probability of seizure freedom rate was estimated based on known predictive values of anatomical, electrophysiological, and semiological characteristics. RESULTS: A total of 83 (25 %) patients met the criteria of PRE. Fourteen (17 %) patients had complete concordance of seizure semiology, MRI, and EEG. Out of these patients, 11 had mesial temporal sclerosis on MRI and three had focal cortical dysplasia (FCD). Estimated seizure-free surgical success rate in this group was 75-95 % without the need for further investigations. Out of 25 (30 %) non-lesional MRI cases, the concordance of seizure semiology and EEG was in nine patients with probable success rate up to 60 %. Thirteen patients (16 %) had discordant EEG and MRI data and were not suitable for surgery without further testing. CONCLUSIONS: A significant portion of PRE patients with concordant anatomical, electrophysiological, and semiological characteristics can be treated surgically in resource-limited countries. Nevertheless, most patients will still require further investigation for proper localization of epileptogenic focus.