Outcome Of Antiepileptic Drug Treatment Research Articles

OC05: Measuring the effects of first antiepileptic medication in Temporal Lobe Epilepsy: Predictive value of quantitative-EEG analysis

To determine the quantitative EEG responses in a population of drug-naïve patients with Temporal Lobe Epilepsy (TLE) after Levetiracetam (LEV) initiation as first antiepileptic drug (AED). We hypothesized that the outcome of AED treatment can be predicted from EEG data in patients with TLE. Methods: Twenty-three patients with TLE and twenty-five healthy controls were examined. Clinical outcome was dichotomized into seizure-free (SF) and non-seizure-free (NSF) after two years of LEV. EEG parameters were compared between healthy controls and patients with TLE at baseline (EEGpre) and after three months of AED therapy (EEGpre-post) and between SF and NSF patients. Receiver Operating Characteristic curves models were built to test whether EEG parameters predicted outcome. Results: AED therapy induces an increase in EEG power for Alpha (p = 0.06) and a decrease in Theta (p < 0.05). Connectivity values were lower in SF compared to NSF patients (p < 0.001). Quantitative EEG predicted outcome after LEV treatment with an estimated accuracy varying from 65.2% to 91.3% (area under the curve [AUC] = 0.56–0.93) for EEGpre and from 69.9% to 86.9% (AUC = 0.69–0.94) for EEGpre-post. Conclusions: AED therapy induces EEG modifications in TLE patients, and such modifications are predictive of clinical outcome. Significance: Quantitative EEG may help understanding the effect of AEDs in the central nervous system and offer new prognostic biomarkers for patients with epilepsy.

Measuring the effects of first antiepileptic medication in Temporal Lobe Epilepsy: Predictive value of quantitative-EEG analysis

ObjectiveTo determine the quantitative EEG responses in a population of drug-naïve patients with Temporal Lobe Epilepsy (TLE) after Levetiracetam (LEV) initiation as first antiepileptic drug (AED). We hypothesized that the outcome of AED treatment can be predicted from EEG data in patients with TLE. MethodsTwenty-three patients with TLE and twenty-five healthy controls were examined. Clinical outcome was dichotomized into seizure-free (SF) and non-seizure-free (NSF) after two years of LEV. EEG parameters were compared between healthy controls and patients with TLE at baseline (EEGpre) and after three months of AED therapy (EEGpre-post) and between SF and NSF patients. Receiver Operating Characteristic curves models were built to test whether EEG parameters predicted outcome. ResultsAED therapy induces an increase in EEG power for Alpha (p = 0.06) and a decrease in Theta (p < 0.05). Connectivity values were lower in SF compared to NSF patients (p < 0.001). Quantitative EEG predicted outcome after LEV treatment with an estimated accuracy varying from 65.2% to 91.3% (area under the curve [AUC] = 0.56–0.93) for EEGpre and from 69.9% to 86.9% (AUC = 0.69–0.94) for EEGpre-post. ConclusionsAED therapy induces EEG modifications in TLE patients, and such modifications are predictive of clinical outcome. SignificanceQuantitative EEG may help understanding the effect of AEDs in the central nervous system and offer new prognostic biomarkers for patients with epilepsy.

Investigating imaging network markers of cognitive dysfunction and pharmacoresistance in newly diagnosed epilepsy: a protocol for an observational cohort study in the UK

IntroductionEpilepsy is one of the most common serious brain disorders, characterised by seizures that severely affect a person’s quality of life and, frequently, their cognitive and mental health. Although most...

Prediction of antiepileptic drug treatment outcomes of patients with newly diagnosed epilepsy by machine learning

ObjectiveThe objective of this study was to build a supervised machine learning-based classifier, which can accurately predict the outcomes of antiepileptic drug (AED) treatment of patients with newly diagnosed epilepsy. MethodsWe collected information from 287 patients with newly diagnosed epilepsy between 2009 and 2017 at the Second Affiliated Hospital of Zhejiang University. Patients were prospectively followed up for at least 3 years. A number of features, including demographic features, medical history, and auxiliary examinations (electroencephalogram [EEG] and magnetic resonance imaging [MRI]) are selected to distinguish patients with different remission outcomes. Seizure outcomes classified as remission and never remission. In addition, remission is further divided into early remission and late remission. Five classical machine learning algorithms, i.e., Decision Tree, Random Forest, Support Vector Machine, XGBoost, and Logistic Regression, are selected and trained by our dataset to get classification models. ResultsOur study shows that 1) compared with the other four algorithms, the XGBoost algorithm based machine learning model achieves the best prediction performance of the AED treatment outcomes between remission and never remission patients with an F1 score of 0.947 and an area under the curve (AUC) value of 0.979; 2) The best discriminative factor for remission and never remission patients is higher number of seizures before treatment (>3); 3) XGBoost-based machine learning model also offers the best prediction between early remission and later remission patients, with an F1 score of 0.836 and an AUC value of 0.918; 4) multiple seizure type has the highest dependence to the categories of early and late remission patients. SignificancesOur XGBoost-based machine learning classifier accurately predicts the most probable AED treatment outcome of a patient after he/she finishes all the standard examinations for the epilepsy disease. The classifier's prediction result could help disease guide counseling and eventually improve treatment strategies.

Outcome of initial antiepileptic drug treatment in elderly patients with newly diagnosed epilepsy

PurposeA study was carried out to assess the outcome of initial antiepileptic drug (AED) monotherapy in elderly patients with newly diagnosed epilepsy and to investigate the cumulative probabilities of ≥2- and ≥5-year complete seizure remission and the factors that may have an effect on the outcome of AED treatment and the response to successive AED regimens. MethodsFrom the medical records of Kuopio University Hospital, Finland, the researchers identified a community-dwelling cohort of elderly subjects (aged 65 or above at the time of diagnosis; n=529) with newly diagnosed epilepsy. The seizure outcome and the effect of initial prescription of AEDs were retrospectively studied. ResultsAll told, 336 (64%) of the patients used the initial AED for the whole follow-up period, while the treatment was changed for 193 (36%) of the patients. In total, 456 (86%) of the 529 patients were treated with monotherapy until the end of follow-up. Four per cent of the patients developed refractory epilepsy. The response to the second monotherapy after failure of the first monotherapy was similar between patients whose treatment failed for reason of intolerable side effects and those showing failure due to inadequate seizure control. The estimated cumulative probability of achieving ≥2 years’ remission was 83%, and that for achieving ≥5 years of remission was 79%. Early response to treatment was a statistically significant predictor of remission. ConclusionThe prognosis of seizures in elderly patients with newly diagnosed epilepsy is good, and most patients can be successfully treated with the first AED. Patients who do not become seizure-free within the first year may be at risk of displaying a drug-resistant seizure disorder.

Outcome of antiepileptic drug treatment of 1282 patients with epilepsy, their pharmacovigilance reports and concomitant medication on CNS in an East-Hungarian adult database

ObjectiveThe aim of this study was to determine the outcome of antiepileptic drug (AED) treatment based on seizure freedom, pharmacovigilance reports and effects of concomitant medication on the central nervous system (CNS) of adult epileptic patients registered in the East-Hungarian Epilepsy Database. MethodsProspective cross-sectional database was compiled from outpatient files between 1992 and 2011. ResultsThe majority of 1282 treated patients were on monotherapy 894 patients (70%), 286 (22%) on bitherapy and 102 (8%) on polytherapy. Of all treated patients, seizure freedom was achieved by 603 (47%). Among the seizure free patients 464 (77%) were on monotherapy, 115 (19%) on bitherapy and only 24 (4%) on polytherapy. The overall rate of adverse drug reactions (ADRs) was 16.2%. From patients on AED, 279 (22%) took concomitant drugs acting on the CNS. In a logistic regression model, other CNS-related drugs and a number of prescribed antiepileptic drugs had a significant influence on the desired outcome of seizure freedom. On comparing the Proportional Reporting Ratio and 95%CI of older and newer AEDs, no significant superiority of newer AEDs was detected. ConclusionCareful drug selection for epileptic patients must be highlighted in order to improve outcome, reduce ADRs and improve patient compliance.