Epilepsy; Drug Treatment Principles

Antiepileptic drug selection is most dictated by the expectation that the drug will control or maximally prevent seizures. Evidence-based studies confirm that some drugs are superior for certain seizure types such as absence and myoclonic. Differences in efficacy for partial onset seizures are often modest, so the choice of drug may be decided by other factors such as expected adverse effects, comorbidities, pharmacokinetics, age, gender, and cost. No drug of choice exists for each seizure type. Rather careful individualized selection is indicated

Epilepsy; Antiepileptic Drug Profiles

Twenty antiepileptic drugs (AEDs) are available in the USA and many areas of the world for chronic therapy to prevent or minimize seizures. For some seizure types, efficacy differences are modest, but all the drugs are distinctive in one or more ways and possess unique mechanisms of action, side effects, or pharmacokinetics. An understanding of these properties is needed to make an optimal selection of monotherapy and combined AED therapy

Epilepsy Treatment Strategies

Factors in everyday life can alter the probability of seizure occurrence. These include missed medication, emotional stress, sleep deprivation, excessive alcohol use, and hormonal influences. The majority of these factors can be modified or regulated to decrease the likelihood of frequent seizures in addition to the use of antiepileptic drugs

Cosmetic side effects of antiepileptic drugs in adults with epilepsy

Cosmetic side effects (CSEs) such as weight gain and alopecia are common, undesirable effects associated with several AEDs. The objective of the study was to compare the CSE profiles in a large specialty practice-based sample of patients taking both older and newer AEDs. As part of the Columbia and Yale AED Database Project, we reviewed patient records including demographics, medical history, AED use, and side effects for 1903 adult patients (≥16years of age) newly started on an AED. Cosmetic side effects were determined by patient or physician report in the medical record and included acne, gingival hyperplasia, hair loss, hirsutism, and weight gain. We compared the overall rate of CSEs and intolerable CSEs (ICSEs–CSEs that led to dosage reduction or discontinuation) between different AEDs in both monotherapy and polytherapy. Overall, CSEs occurred in 110/1903 (5.8%) patients and led to intolerability in 70/1903 (3.7%) patients. Weight gain was the most commonly reported CSE (68/1903, 3.6%) and led to intolerability in 63 (3.3%) patients. Alopecia was the second most common patient-reported CSE (36/1903, 1.9%) and was intolerable in 33/1903 (1.7%) patients. Risk factors for CSEs included female sex (7.0% vs. 4.3% in males; p<0.05) and any prior CSE (37% vs. 2.9% in patients without prior CSE; p<0.001). Significantly more CSEs were attributed to valproic acid (59/270; 21.9%; p<0.001) and pregabalin (14/143; 9.8%; p<0.001) than to all other AEDs. Significantly less CSEs were attributed to levetiracetam (7/524; 1.3%; p=0.002). Weight gain was most frequently associated with valproic acid (35/270; 13.0%; p<0.001) and pregabalin (12/143; 8.4%; p<0.001). Hair loss was most commonly reported among patients taking valproic acid (24/270; 8.9%; p<0.001). Finally, gingival hyperplasia was most commonly reported in patients taking phenytoin (10/404; 2.5%; p<0.001). Cosmetic side effects leading to dosage change or discontinuation occurred most frequently with pregabalin and valproic acid compared with all other AEDs (13.3 and 5.6% vs. 2.3%; p<0.001). For patients who had been on an AED in monotherapy (n=677), CSEs and ICSEs were still more likely to be attributed to valproic acid (30.2% and 17.1%, respectively) than to any other AED (both p<0.001). Weight gain and alopecia were the most common patient-reported CSEs in this study, and weight gain was the most likely cosmetic side effect to result in dosage adjustment or medication discontinuation. Particular attention should be paid to pregabalin, phenytoin, and valproic acid when considering cosmetic side effects. Female patients and patients who have had prior CSE(s) to AED(s) were more likely to report CSEs. Knowledge of specific CSE rates for each AED found in this study may be useful in clinical practice. •Cosmetic side effect (CSE) profiles of antiepileptic drugs (AEDs) are compared.•Women are more likely to experience CSEs compared with men.•Patients who have prior CSE(s) to AEDs are more likely to develop CSEs.•Weight gain is the most common CSE attributed to AEDs.•Valproate and pregabalin lead to more incidences of weight gain compared with other AEDs

Psychiatric and behavioral side effects of anti-epileptic drugs in adolescents and children with epilepsy

Purpose: The objective of the study was to compare the psychiatric and behavioral side effect (PBSE) profiles of both older and newer antiepileptic drugs (AEDs) in children and adolescent patients with epilepsy. Method: We used logistic regression analysis to test the correlation between 83 non-AED/ patient related potential predictor variables and the rate of PBSE. We then compared for each AED the rate of PBSEs and the rate of PBSEs that led to intolerability (IPBSE) while controlling for non-AED predictors of PBSEs. Results: 922 patients (<= 18 years old) were included in our study. PBSEs and IPBSEs occurred in 13.8% and 11.2% of patients, respectively. Overall, a history of psychiatric condition, absence seizures, intractable epilepsy, and frontal lobe epilepsy were significantly associated with increased PBSE rates. Levetiracetam (LEV) had the greatest PBSE rate (16.2%). This was significantly higher compared to other AEDs. LEV was also significantly associated with a high rate of IPBSEs (13.4%) and dose-decrease rates due to IPBSE (6.7%). Zonisamide (ZNS) was associated with significantly higher cessation rate due to IPBSE (9.1%) compared to other AEDs. Conclusion: Patients with a history of psychiatric condition, absence seizures, intractable epilepsy, or frontal lobe epilepsy are more likely to develop PBSE. PBSEs appear to occur more frequently in adolescent and children patients taking LEV compared to other AEDs. LEV-attributed PBSEs are more likely to be associated with intolerability and subsequent decrease in dose. The rate of ZNS-attributed IPBSEs is more likely to be associated with complete cessation of AED. (C) 2017 European Paediatric Neurology Society. Published by Elsevier Ltd. All rights reserved

Browsing a Video with Simple Constrained Queries over Fuzzy Annotations

this paper by describing the technology used to create our query prototype and we present the structure of our XML annotation

Cross-cultural Convergent Validation of the Spanish and English Versions of the Personal Impact of Epilepsy Scale (PIES) in a Cohort of Patients from a Level IV Epilepsy Center in South Florida

Abstract Objective To provide further convergent validity on the Personal Impact of Epilepsy Scale (PIES) Spanish version for use in Hispanics with epilepsy. Participants and Method This study included 90 patients (22 Hispanic and Spanish speaking - HSS, 30 Hispanic and English speaking – HES, and 38 non-Hispanic and English speaking – NHES) who were diagnosed with epilepsy and referred by their epileptologist for a comprehensive neuropsychological evaluation that, in addition to cognitive measures, also included the Beck Depression Inventory (BDI-II), Beck Anxiety Inventory (BAI), and PIES in English or Spanish- depending on the participant’s primary language. The PIES was created with subscales to measure the effect of seizures, adverse effects of treatment, and comorbidities such as mood disorders on quality of life. We compared scores on the PIES subscales in English and Spanish to determine equivalence. We further examined if language of administration moderated the relationship between PIES subscale scores and depression/anxiety through nonparametric bootstrapping. Results There were no significant demographic or clinical differences between the 3 groups examined. ANOVA contrast of mean scores on all PIES subscales between HSS, HES and NHES groups did not reach significance (Seizures F [2,88] = .06 , p = -.93; Medication use F[2,88] = 1.95, p = .14, Comorbidity F[2,88] = .64, p = .52, and total PIES F[2,88] = .19, p = .83). The relationships between PIES and BDI-II/BAI scores were not significantly moderated by language of administration. Conclusions Our data suggest clinical equivalency between the English and Spanish versions of the PIES. Furthermore, the relationships between PIES subscales and the BDI-II/BAI were not significantly changed by language of administration. These findings indicate that the Spanish PIES appears to have good convergent validity with the original English version and can be useful in the assessment of quality of life in HSS patients

Intellectual, Psychological, and Clinical Predictors of Quality of Life in Patients with Epilepsy from a Level IV Epilepsy Center in South Florida

Abstract Objective To examine clinical, cognitive, and psychological predictors of Quality of Life (QOL) among Hispanics and non-Hispanics with epilepsy. Participants and Method Data was collected from 90 patients (52 Hispanic; H, 38 Non-Hispanic; NH) diagnosed with epilepsy and referred for neuropsychological evaluation that included measures of depression (BDI-II), anxiety (BAI), and QOL using the Personal Impact of Epilepsy Scale (PIES). We conducted multiple linear regression models with patient’s full-scale IQ, age of seizure onset, duration of seizure disorder, and anxiety and depression symptom severity entered as predictors, with the PIES as outcome measure. Results There were no significant differences between H and NH on any demographic or clinical variable except for longer duration of seizure disorder (t = 2.20, p = 0.03) in H. For the complete sample, higher FSIQ predicted better QOL in relation to the perceived disabling effects of seizures (β = .28, p =.02), higher BDI-II scores predicted more negative medication side effects and greater mood related disruption in QOL (β = -2.27, p =.03; β = -3.82, p =.00), and higher BAI scores were the best predictor for worse overall QOL (β = -.29, p = .05). When the sample was divided according to ethnic background, longer seizure duration was associated with the adverse effects of less medication in the H group (β = .32, p =.04), with all other findings being identical to those of the complete sample. When results for NH patients were analyzed, none of the intellectual, psychological and clinical variables were significantly associated with any of the QOL scales; however, the relationship between the BAI and comorbid mood dysfunction approached significance (β = -1.45, p = .059). Conclusions Our data indicates that overall QOL was primarily predicted by the severity of anxiety symptoms, but not influenced by overall intelligence, symptoms of depression, and clinical seizure variables

Is seizure frequency variance a predictable quantity?

Background: There is currently no formal method for predicting the range expected in an individual's seizure counts. Having access to such a prediction would be of benefit for developing more efficient clinical trials, but also for improving clinical care in the outpatient setting. Methods: Using three independently collected patient diary datasets, we explored the predictability of seizure frequency. Three independent seizure diary databases were explored: SeizureTracker (n = 3016), Human Epilepsy Project (n = 93), and NeuroVista (n = 15). First, the relationship between mean and standard deviation in seizure frequency was assessed. Using that relationship, a prediction for the range of possible seizure frequencies was compared with a traditional prediction scheme commonly used in clinical trials. A validation dataset was obtained from a separate data export of SeizureTracker to further verify the predictions. Results: A consistent mathematical relationship was observed across datasets. The logarithm of the average seizure count was linearly related to the logarithm of the standard deviation with a high correlation (R2 > 0.83). The three datasets showed high predictive accuracy for this log-log relationship of 94%, compared with a predictive accuracy of 77% for a traditional prediction scheme. The independent validation set showed that the log-log predicted 94% of the correct ranges while the RR50 predicted 77%. Conclusion: Reliably predicting seizure frequency variability is straightforward based on knowledge of mean seizure frequency, across several datasets. With further study, this may help to increase the power of RCTs, and guide clinical practice