Treatment Outcomes In Epilepsy Research Articles

Effects of Psychiatric Comorbidities on the Prognosis of New-Onset Pediatric Epilepsy: A Retrospective Nationwide Cohort Study.

Background/Objectives: To determine the impact of psychiatric disorders on epilepsy treatment outcomes and healthcare utilization in children with epilepsy (CWE) based on the presence or timing of the onset of psychiatric disorders. Methods: This retrospective controlled study enrolled children (age < 18 years) with newly diagnosed epilepsy into four groups stratified by the presence and timing of the onset of psychiatric disorders (None: no psychiatric disorders; Before: psychiatric disorders only preceding the epilepsy diagnosis; After: new psychiatric disorders diagnosed only after the epilepsy diagnosis; Mixed: different psychiatric disorders diagnosed both before and after epilepsy diagnosis) and compared the intergroup differences in epilepsy treatment outcomes and healthcare utilization. Results: Among the CWE (n = 37,678), 13,285 (35.26%) had comorbid psychiatric disorders. The After (n = 7892), Mixed (n = 3105), and Before (n = 2288) groups had significantly longer treatment periods than those in the None group (p < 0.001). Compared with the None group, the remaining groups had significantly higher frequencies of outpatient visits, emergency room visits, and admissions and higher rates of status epilepticus and drug-resistant epilepsy (p < 0.001, respectively), with higher odds ratios [95% confidence interval] for status epilepticus (2.92 [2.68-3.18]) and drug-resistant epilepsy (3.01 [2.85-3.17]) in the After group. Conclusions: Psychiatric comorbidities, diagnosed before and after epilepsy diagnosis, negatively affected the treatment outcomes. CWE without prior psychiatric disorders that were newly diagnosed during epilepsy treatment had the worst outcomes and the highest healthcare utilization rates.

Retrospective Review of Treatment Outcomes in Epilepsy Patients: Seizure Control and Medication Side Effects

ABSTRACT Background: Epilepsy management requires balancing seizure control with minimizing medication side effects. This retrospective review examines treatment outcomes in epilepsy patients, focusing on seizure control and medication-related adverse effects. Methods: Medical records of epilepsy subjects treated tertiary care center between 2017 and 2022 were retrospectively reviewed. Data on demographic characteristics, seizure control, medication regimen, and side effects were collected and analyzed. Results: A total of 200 subjects were included. Sixty percent of subjects achieved total seizure independence, 30% reduced seizure frequency, and 10% did not improve or deteriorate. The kind of antiepileptic medicine provided substantially affects prescription side effects (P &lt; 0.05), with drug X causing more dizziness than drug Y (P = 0.02). Elderly individuals were more likely to suffer weariness than younger ones (P = 0.03). Conclusion: This review provides valuable insights into epilepsy treatment outcomes, emphasizing the need for personalized management strategies to optimize seizure control while minimizing medication-related adverse effects.

Epilepsy Treatment Outcome and Its Predictors Among Children Who Had Chronic Follow Up at Dessie Comprehensive Specialized Hospital.

Epilepsy is a chronic non-communicable disease of the brain that affects millions of people worldwide. A significant number of children are affected globally, and most live in developing countries, often with physical and cognitive disabilities. Regardless of these factors, epilepsy is poorly controlled, particularly in the developing countries. Thus, this study aimed to assess the magnitude of treatment outcomes and its predictors among pediatrics patients with epilepsy who were followed-up at the Dessie Comprehensive Specialized Hospital, Northeast Ethiopia. Hospital-based cross-sectional study was conducted from 1 June 2022 to 30 August 2022. A total of 200 patients with epilepsy were included in this study. Data were collected through face-to-face interviews, and by reviewing medical records. The collected data were entered into Epi-data version 4.6 and exported to SPSS version 25.0. Descriptive statistics such as frequencies, percentages, means and standard deviations were computed. Binary and multivariate logistic regression analyses were performed. Variables with p < 0.25 in bivariate analysis were entered into multivariable logistic regression. In multivariable analysis, adjusted odd ratio with 95% CI and p-value less than 0.05 were considered statistically significant. Of 200 pediatric patients with epilepsy, 66 (34.5%) had poor treatment outcomes. In the multivariate analysis, 11-15 years of age (AOR = 4.08; 95% CI = 1.202, 13.848), poor treatment adherence (AOR = 3.21; 95% CI = 1.421, 7.249), history of more seizure frequency before starting treatment (AOR = 4.19; 95% CI = 1.984, 8.834) and history of head injury (AOR = 3.03; 95% CI = 1.502, 6.112) were significantly associated with poor treatment outcomes in pediatric patients with epilepsy. Significant proportion of pediatric patients with epilepsy had poor treatment outcomes. Therefore, health-care workers should strictly follow patient treatment especially for pediatric epileptic patients' who have poor treatment adherence, more seizure frequency history, head injury history and whose age were 11-15 years old.

Gut-Brain-axis: effect of basil oil on the gut microbiota and its contribution to the anticonvulsant properties

BackgroundEpilepsy is a chronic neurological condition that disrupts the normal functioning of the brain and it is characterized by seizures. Research suggests the involvement of the Gut-Brain axis in epilepsy. This study seeks to determine the role of the gut microbiota in the anticonvulsant effect of basil oil (BO) using antibiotic-depleted and altered germ-free mice against naïve mice in Pentylenetetrazole (PTZ) induced seizure model. There is an ever growing interest in improvement of treatment outcomes in epilepsy and also in the development of newer therapeutic options, especially in the population of patients that do not attain seizure relief from available antiseizure medications (ASMs). According to research, gut microbiota can alter brain function and development. Increasing evidence suggests disrupting the delicate symbiotic balance existing between the gut and brain results in disease conditions. Also, the oil from Ocimum basilicum L., (BO) has been proven scientifically to significantly block clonic seizures induced by PTZ and picrotoxin in seizure models.MethodsThe microbiota of mice were depleted or altered by administering cocktail antibiotics and individual antibiotics respectively. DNA was isolated from mouse stool, and then the 16S ribosomal ribonucleic acid (16S rRNA) gene was quantitatively amplified using reverse transcription-polymerase chain reaction (RT-PCR). Amplicons were sequenced to determine the phylogenetic make-up of the bacteria involved. Metabolic profiles of the serum and stool of mice were determined using Proton (1H) Nuclear Magnetic Resonance (NMR) spectroscopy.ResultsCocktail antibiotic pre-treatment significantly reversed the anticonvulsant effect of BO by increasing frequency and duration of seizures but did not affect latency to seizure. In mice pre-treated with single antibiotics, the anticonvulsant effect of BO was lost as latency to seizures, frequency and duration of seizures increased compared to mice that received only BO. Assessment of the phylogenetic make-up of the microbiota in antibiotic pre-treated mice showed a distorted composition of the microbiota compared to the control group.ConclusionDepletion of the microbiota significantly reversed the anticonvulsant actions of BO. The concentrations of short chain fatty acids (SCFAs) was higher in stool than in the serum of the mice. Administration of BO probably does not influence the microbial composition within the mouse microbiota. The elevated ratio of Firmicutes to Bacteroidetes in microbiota-depleted groups might have contributed to the reversal of anticonvulsant actions of BO.

Stereoelectroencephalography-guided radiofrequency ablation of the epileptogenic zone as a treatment and predictor of future success of further surgical intervention.

Stereoelectroencephalography (SEEG)-guided radiofrequency ablation (RFA) is increasingly being used as a treatment for drug-resistant localization-related epilepsy. The aim of this study is to analyze the successes and failures using RFA and how response correlates with surgical epilepsy treatment outcomes. We retrospectively reviewed 62 patients who underwent RFA via SEEGelectrodes. After excluding five, the remaining 57 were classified into subgroups based on procedures and outcomes. Forty patients (70%) underwent a secondary surgical procedure, of whom 32 were delayed: 26 laser interstitial thermal therapy (LITT), five resection, one neuromodulation. We determined the predictive value of RFA outcome upon subsequent surgical outcome by categorizing the delayed secondary surgery outcome as success (Engel I/II) versus failure (Engel III/IV). Demographic information, epilepsy characteristics, and the transient time of seizure freedom after RFA were calculated for each patient. Twelve of 49 patients (24.5%) who had RFA alone and delayed follow-up achieved Engel class I. Of the 32 patients who underwent a delayed secondary surgical procedure, 15 achieved Engel class I and nine Engel class II (24 successes), and eight were considered failures (Engel class III/IV). The transient time of seizure freedom after RFA was significantly longer in the success group (4 months, SD = 2.6) as compared to the failure group (.75 months, SD = 1.16; p < .001). Additionally, there was a higher portion of preoperative lesional findings in patients in the RFA alone and delayed surgical success group (p = .03) and a longer time to seizure recurrence in the presence of lesions (p < .05). Side effects occurred in 1% of patients. In this series, RFA provided a treatment during SEEG-guided intracranial monitoring that led to seizure freedom in ~25% of patients. Of the 70% who underwent delayed surgery, longer transient time of seizure freedom after RFA was predictive of the results of the secondary surgeries, 74% of which were LITT.

Treatment outcome and associated factors among epileptic patients at ambulatory clinic of Mettu Karl Comprehensive Specialized Hospital: A cross-sectional study.

Objective:The study was aimed to assess the magnitude and associated factors of 2-year seizure-free status of epileptic patients at Mettu Karl Comprehensive Specialized Hospital (MKCSH).Methods:A retrospective cross-sectional study was conducted at MKCSH from 12 February 2020 to 11 August 2020. Treatment outcome was measured in terms of seizure control status and seizure frequency. Accordingly, good treatment outcome was declared if the patients had a 2-year seizure-free status and poor if the patient had episodes of seizure in the last 2 years. The pharmaceutical care network Europe guideline was used to assess the presence of medication-related problems. Multivariable logistic regression was used to analyze the variables by using crude odds ratio (COR) and adjusted odds ratio (AOR) with 95% confidence interval (CI). Finally, the predictors of epilepsy treatment outcome were declared if a P-value was ⩽0.05.Results:Over the study period, more than half of the participants (172 (57.7%)) were males. The median age of participants was 29 and majorly distributed to 18–30 classes. More than one-fourth of the patients were suffering from absent seizure 85 (28.5%) and general tonic-clonic seizure 83 (27.9%). The most commonly prescribed medication was Phenobarbitone which accounts 34 (11.41%), whereas Valproic acid 8 (2.68%) was the least prescribed. Overall, less than half 131 (43.96%) of patients developed poor treatment outcomes. The predictors of treatment outcomes were having head the injury (AOR = 5.7; 95% CI: 3.18, 10.31), family history of epilepsy (AOR = 5.6; 95% CI: 3.07, 10.46) and the presence of drug therapy problems (AOR = 5.2; 95% CI: 2.79, 9.69).Conclusion:The magnitude of poor epilepsy treatment outcome was found to be high. The predictors of epilepsy treatment outcome were the history of head injury, family history of epilepsy and drug therapy problems. Therefore, to improve the treatment outcome of epileptic patients, health care providers should pay close attention to the identified factors. Besides this, the clinical pharmacy service should be implemented to minimize any medication-related problems

Epilepsy treatment outcomes in the referral hospitals of northeast Ethiopia

BackgroundGlobally, more than half of the epileptic patients have uncontrolled seizures despite treatment with the appropriate antiepileptic drugs. However, the problem has been remained under-reported especially in developing countries. Therefore, this study was designed to assess the treatment outcomes of epileptic patients in the referral hospitals of the Amhara region. MethodsAn institutional-based cross-sectional study was conducted among 420 epileptic patients in the referral hospitals of Amhara region. The study participants were selected by a systematic random sampling method using the patient registration logbook as a sampling frame. An interviewer-administered questionnaire and abstraction checklist were used to collect data. The data was entered into Epi-Data software version 3.1 and analyzed using the Statistical Package for Social Science) version 24.0. The Descriptive statistics were done and presented using the descriptive summaries and frequency tables. Bivariate and Multivariate Binary Logistic Regression Models with a Backward Elimination Method were done to identify the factors associated with the treatment outcome of epilepsy. The odds ratio with a 95 % confidence interval was used to determine the significance level of association. ResultIn this study, the magnitude of uncontrolled seizure was 44 % (95 % CI: 39%–48.6%). Being male [AOR = 0.39: 95 % CI 0.25,0.62], high medication necessity beliefs [AOR = 0.34: 95 %CI 0.2,0.57], positive medication beliefs [AOR = 0.23: 95 % CI: 0.13−0.4], and good medication adherence [AOR = 0.21: 95 % CI: 0.1−0.46] were the preventive factors of uncontrolled seizure. In contrast, the presence of co-morbidities [AOR = 2.22, 95 %CI:1.38−3.57] and poor social support [AOR = 1.7: 95 %CI:1.07−2.69] were a risk factors of uncontrolled seizures. ConclusionUncontrolled seizure was found to be higher than the expected seizure frequency, which is preferably zero after one year of treatment. The clinical and treatment related factors were the factors associated with uncontrolled seizure. Emphasis should be given to the treatment strategies of epileptic patients. The health extension packages should integrate community-based counseling to enhance social support and early detection of comorbidities, increase medication adherence, and medication belief among epileptic patients.

Atypical benzodiazepine misuse and withdrawal in patient with epilepsy: a case report

BackgroundBenzodiazepines (BDZ) are a class of psychoactive drugs that have been widely used for the treatment of many medical conditions. In this paper, a case of an atypical pattern of diazepam dependence in a patient with epilepsy is presented. The article may be an interesting proposition for rational management in the treatment of benzodiazepine dependence in a patient with non-withdrawal and withdrawal seizures. Detoxification is used then to optimize neurological treatment.Case presentationA 52-year-old Caucasian male, diagnosed with epilepsy with partial complex, and rarely tonic-clonic seizures and benzodiazepine (diazepam) misuse, was admitted to a detoxification unit specialized to treat substance dependence. This patient presented an atypical schedule of substance misuse with a weekly dose of 40 to 45 mg of diazepam, taken once a week, every Saturday. The patient reported having a group of symptoms that usually preceded generalized seizures that were described as the “aura” manifesting as confusion, derealisation, anxiety, and difficulties in speaking. First Saturday after admission to the hospital, the first aura experience was reported, while diazepam level in serum was higher than usual, which was supporting also strong psychological background for withdrawal. On weekend days, 3 weeks after admission, when the patient was receiving 3 mg of diazepam daily, he developed a severe “aura.” The EEG showed no seizure activity. Two weeks later (again Friday to Saturday), the patient reported the occurrence of aura, the EEG recording showed paroxysmal discharges, generalized multiple spikes associated with slow waves, lasting about 30 s, accompanied by eyelid myoclonia and disturbed consciousness. A week later another aura developed that resolved spontaneously without a seizure. Twelve days later, diazepam was completely removed; the elimination of serum benzodiazepines took place. The patient did not report aura until the end of the stay and he was dismissed 5 weeks later.ConclusionsThe differential diagnosis of an aura can be challenging. Carefully proceed serum monitored elimination of benzodiazepines in epileptic patients may serve in safety benefits and helps to achieve detoxification goals. Hence, it is important to prevent, recognize, and treat benzodiazepine dependence in every patient and may improve epilepsy treatment outcome.

Epilepsy Treatment Outcome and Its Predictors among Ambulatory Patients with Epilepsy at Mizan-Tepi University Teaching Hospital, Southwest Ethiopia.

Background Epilepsy is among the most common neurological disorders which is highly treatable with currently available antiepileptic drugs at a reasonable price. In Ethiopia, despite a number of studies revealed high prevalence of epilepsy, little is known on predictors of poorly controlled seizures. Thus, the aim of this study was to assess epilepsy treatment outcome and its predictors among patients with epilepsy on follow-up at the ambulatory care unit of Mizan-Tepi University Teaching Hospital, Southwest Ethiopia. Methods A hospital-based cross-sectional study involving patient interview and chart review was conducted from March 10 to April 10, 2018. Drug use patterns and sociodemographic data of the study participants were accustomed to descriptive statistics. Backward logistic regression analysis was done to identify predictors of poor seizure control. Statistical significance was considered at p value <0.05. Results From a total of 143 studied patients with epilepsy, 60.8% had uncontrolled seizures. Monotherapy (79%) was commonly used for the treatment of seizures, of which phenobarbital was the most commonly utilized single anticonvulsant drug (62.9%). The majority (72.7%) of the patients had developed one or more antiepileptic-related adverse effects. Medium medication adherence (adjusted odds ratio (AOR) = 5.4; 95% CI = 1.52–19.23; p=0.009), poor medication adherence (AOR = 8.16; 95% CI = 3.04–21.90; p=0.001), head injury before seizure occurrence (AOR = 4.9; 95% CI = 1.25–19.27; p=0.02), and seizure attacks ≥4 episodes/week before AEDs initiation (AOR = 8.52; % CI = 2.41–13.45; p=0.001) were the predictors of uncontrolled seizure. Conclusions Based on our findings, more than half of the patients with epilepsy had poorly controlled seizures. Nonadherence to antiepileptic drugs, high frequency of seizure attack before AEDs initiation, and history of a head injury before the occurrence of seizure were predictors of uncontrolled seizure. Patient medication adherence should be increased by the free access of antiepileptic drugs and attention should be given for the patients with a history of head injury and high frequency of seizure attacks before AEDs initiation.

Neuroinflammation impact in epileptogenesis and new treatment strategy.

Epilepsy is considered a major serious chronic neurological disorder, characterized by recurrent seizures. It is usually associated with a history of a lesion in the nervous system. Irregular activation of inflammatory molecules in the injured tissue is an important factor in the development of epilepsy. It is unclear how the imbalanced regulation of inflammatory mediators contributes to epilepsy. A recent research goal is to identify interconnected inflammation pathways which may be involved in the development of epilepsy. The clinical use of available antiepileptic drugs is often restricted by their limitations, incidence of several side effects, and drug interactions. So development of new drugs, which modulate epilepsy through novel mechanisms, is necessary. Alternative therapies and diet have recently reported positive treatment outcomes in epilepsy. Vitamin D (Vit D) has shown prophylactic and therapeutic potential in different neurological disorders. So, the aim of current study was to review the associations between different brain inflammatory mediators and epileptogenesis, to strengthen the idea that targeting inflammatory pathway may be an effective therapeutic strategy to prevent or treat epilepsy. In addition, neuroprotective effects and mechanisms of Vit D in clinical and preclinical studies of epilepsy were reviewed.

Trends in antiepileptic drug treatment and effectiveness in clinical practice in England from 2003 to 2016: a retrospective cohort study using electronic medical records

ObjectiveTo assess the evolution of antiepileptic drug (AED) treatment patterns and seizure outcomes in England from 2003 to 2016.Design, setting and participantsRetrospective cohort study of electronic medical records from Clinical...

Treatment outcome and associated factors among patients with epilepsy

Epilepsy is a major public health problem worldwide. Despite multiple drug therapies, people with epilepsy continue to have frequent seizures. There is a dearth of data on epilepsy treatment outcome and associated factors in our setting. Therefore, the aim of this was to assess treatment outcome and associated factors among epileptic patients on follow up at the neurologic clinic of Ayder comprehensive specialized hospital, Ethiopia. A cross-sectional study was conducted on randomly selected epileptic patients. Data were collected through patient interview and review of medical records. Epilepsy treatment outcome was evaluated in terms of seizure control status in the last one year follow up period. Binary logistic regression analysis was performed to identify predictors of treatment outcome. A total of 270 patients were included. Of whom, 46.6% had controlled seizures. Whereas, 38.5%, 8.8%, and 5.9% had experienced seizure attacks 1–5 times, 6–10 times, and greater than 10 times, respectively. Alcohol consumption [adjusted odds ratio [(AOR): 14.87, 95% confidence interval (CI): 3.25–68.11], negative medication belief [AOR: 3.0, 95%CI: 1.31–6.71], low medication adherence [AOR:11.52, 95%CI: 3.25–40.82], and presence of comorbidities [AOR: 10.35, 95%CI: 4.40–24.40] were predictors of uncontrolled seizure. Our finding revealed that more than half of the epileptic patients had uncontrolled seizure. Epileptic patients with a negative medication belief, comorbidities, low medication adherence, and those who consume alcohol were more likely to have uncontrolled seizure. Therefore, more emphasis should be given to these patients.

Prediction of antiepileptic drug treatment outcomes using machine learning

Objective. Antiepileptic drug (AED) treatments produce inconsistent outcomes, often necessitating patients to go through several drug trials until a successful treatment can be found. This study proposes the use of machine learning techniques to predict epilepsy treatment outcomes of commonly used AEDs. Approach. Machine learning algorithms were trained and evaluated using features obtained from intracranial electroencephalogram (iEEG) recordings of the epileptiform discharges observed in Mecp2-deficient mouse model of the Rett Syndrome. Previous work have linked the presence of cross-frequency coupling (ICFC) of the delta (2–5 Hz) rhythm with the fast ripple (400–600 Hz) rhythm in epileptiform discharges. Using the ICFC to label post-treatment outcomes we compared support vector machines (SVMs) and random forest (RF) machine learning classifiers for providing likelihood scores of successful treatment outcomes. Main results. (a) There was heterogeneity in AED treatment outcomes, (b) machine learning techniques could be used to rank the efficacy of AEDs by estimating likelihood scores for successful treatment outcome, (c) ICFC features yielded the most effective a priori identification of appropriate AED treatment, and (d) both classifiers performed comparably. Significance. Machine learning approaches yielded predictions of successful drug treatment outcomes which in turn could reduce the burdens of drug trials and lead to substantial improvements in patient quality of life.

Evaluation of machine learning algorithms for treatment outcome prediction in patients with epilepsy based on structural connectome data

The objective of this study is to evaluate machine learning algorithms aimed at predicting surgical treatment outcomes in groups of patients with temporal lobe epilepsy (TLE) using only the structural brain connectome. Specifically, the brain connectome is reconstructed using white matter fiber tracts from presurgical diffusion tensor imaging. To achieve our objective, a two-stage connectome-based prediction framework is developed that gradually selects a small number of abnormal network connections that contribute to the surgical treatment outcome, and in each stage a linear kernel operation is used to further improve the accuracy of the learned classifier. Using a 10-fold cross validation strategy, the first stage in the connectome-based framework is able to separate patients with TLE from normal controls with 80% accuracy, and second stage in the connectome-based framework is able to correctly predict the surgical treatment outcome of patients with TLE with 70% accuracy. Compared to existing state-of-the-art methods that use VBM data, the proposed two-stage connectome-based prediction framework is a suitable alternative with comparable prediction performance. Our results additionally show that machine learning algorithms that exclusively use structural connectome data can predict treatment outcomes in epilepsy with similar accuracy compared with “expert-based” clinical decision. In summary, using the unprecedented information provided in the brain connectome, machine learning algorithms may uncover pathological changes in brain network organization and improve outcome forecasting in the context of epilepsy.

Factors affecting epilepsy treatment outcomes in Nigeria.

Epilepsy is a complex chronic illness with variable treatment outcomes determined by factors specific to each affected person. We investigated prognostic factors for epileptic seizure outcome after 2 years of anti-epileptic drug (AED) monotherapy. Two hundred and thirty-four AED naïve epilepsy patients were started on AED monotherapy and monitored for 2 years for seizure control. Patients on AED polytherapy were excluded. Prognostic factors investigated were socio-demographics characteristics, age of onset of epilepsy, etiology and precipitants of seizures, presenting seizure types, duration of epilepsy before initiation of AED, family support for treatment, compliance to AED, and clinic attendance. Patients were 160 (68.4%) males and 74 (31.6%) females, with mean ages of 26.2 ± 10.4 and 30.7 ± 16.0 years, respectively. Mean age of onset of epilepsy was 19.0 ± 15.0 years. None of the patients was treated with AED for first seizures, although 75% received traditional and spiritual therapy. Majority were single and of low socioeconomic background, and treatment was funded mainly by parents/guardians. About 88% presented with partial seizures, and carbamazepine was the commonest AED used followed by phenytoin sodium. At the end of 2 years of AED, 147 (63%) achieved complete seizure control, and significant prognostic factors were female sex, age older than 20 years at presentation, secondary and tertiary education attainment, being employed, family support, regular clinic attendance, AED compliance, and generalized epilepsy. Patients with epilepsy are more likely to achieve complete seizure control when they are older, educated, and employed or supported by family members to buy their AEDs and attend clinics regularly.

Gene-Wide Tagging study of the Effects of Common Genetic Polymorphisms in the α Subunits of the GABA A Receptor on Epilepsy Treatment Response

We aimed to identify the effect of SNPs in the α-subunits of GABAA receptors on epilepsy treatment outcomes by using a gene-wide tagging method. There were 720 epileptic patients included in the present study. A total of 136 tagging SNPs in GABRA1, GABRA2, GABRA3, GABRA4, GABRA5 and GABRA6 were genotyped by Illumina(®)GoldenGate(®) Genotyping platform. Clinical information, such as prescribed antiepileptic drugs, height, weight, epilepsy syndrome classification, etiology, number of attacks, renal function and liver function were collected. The associations between SNPs and epilepsy treatment outcomes were analyzed using SAS(®) version 9.1.3. Both multivariate logistic regression and multifactor dimensionality reduction analyses were performed. The results of single gene effects did not remain significant after Bonferroni's corrections. Further multivariate logistic regression and multifactor dimensionality reduction analyses of interactions between these genes showed that under adjustment of clinical factors, the epilepsy treatment outcomes were significantly associated with the genotype combinations of GABRA1 rs6883877, GABRA2 rs511310 and GABRA3 rs4828696 (p < 0.0001; adjusted r(2) = 0.149). Our results indicated that genetic variants in the α subunits of GABA(A) receptors may interactively affect the treatment responses of antiepileptic drugs. Further replication using an independent sample collection would be essential to confirm our findings.

Newer anti-epileptic drugs

A number of newer anti-epileptic drugs have been developed in the last few years to improve the treatment outcomes in epilepsy. In this review, we discuss the use of newer anti-epileptic drugs in children. MEDLINE search (1966-2013) was performed using terms newer anti-epileptic drugs, Oxcarbazepine, vigabatrin, topiramate, zonisamide, levetiracetam, lacosamide, rufinamide, stiripentol, retigabine, eslicarbazepine, brivaracetam, ganaxolone and perampanel for reports on use in children. Review articles, practice parameters, guidelines, systematic reviews, meta-analyses, randomized controlled trials, cohort studies, and case series were included. The main data extracted included indications, efficacy and adverse effects in children. Oxcarbazepine is established as effective initial monotherapy for children with partial-onset seizures. Vigabatrin is the drug of choice for infantile spasms associated with tuberous sclerosis. Lamotrigine, levetiracetam and lacosamide are good add-on drugs for patients with partial seizures. Lamotrigine may be considered as monotherapy in adolescent females with idiopathic generalized epilepsy. Levetiracetam is a good option as monotherapy for females with juvenile myoclonic epilepsy. Topiramate is a good add-on drug in patients with epileptic encephalopathies such as Lennox-Gastaut syndrome and myoclonic astatic epilepsy.

Bayesian variable selection for survival regression in genetics

Variable selection in regression with very big numbers of variables is challenging both in terms of model specification and computation. We focus on genetic studies in the field of survival, and we present a Bayesian-inspired penalized maximum likelihood approach appropriate for high-dimensional problems. In particular, we employ a simple, efficient algorithm that seeks maximum a posteriori (MAP) estimates of regression coefficients. The latter are assigned a Laplace prior with a sharp mode at zero, and non-zero posterior mode estimates correspond to significant single nucleotide polymorphisms (SNPs). Using the Laplace prior reflects a prior belief that only a small proportion of the SNPs significantly influence the response. The method is fast and can handle datasets arising from imputation or resequencing. We demonstrate the localization performance, power and false-positive rates of our method in large simulation studies of dense-SNP datasets and sequence data, and we compare the performance of our method to the univariate Cox regression and to a recently proposed stochastic search approach. In general, we find that our approach improves localization and power slightly, while the biggest advantage is in false-positive counts and computing times. We also apply our method to a real prospective study, and we observe potential association between candidate ABC transporter genes and epilepsy treatment outcomes.

Bayesian survival analysis in genetic association studies

Motivation: Large-scale genetic association studies are carried out with the hope of discovering single nucleotide polymorphisms involved in the etiology of complex diseases. There are several existing methods in the literature for performing this kind of analysis for case-control studies, but less work has been done for prospective cohort studies. We present a Bayesian method for linking markers to censored survival outcome by clustering haplotypes using gene trees. Coalescent-based approaches are promising for LD mapping, as the coalescent offers a good approximation to the evolutionary history of mutations.Results: We compare the performance of the proposed method in simulation studies to the univariate Cox regression and to dimension reduction methods, and we observe that it performs similarly in localizing the causal site, while offering a clear advantage in terms of false positive associations. Moreover, it offers computational advantages. Applying our method to a real prospective study, we observe potential association between candidate ABC transporter genes and epilepsy treatment outcomes.Availability: R codes are available upon request.Contact: ioanna.tachmazidou@imperial.ac.ukSupplementary information: Supplementary data are available at Bioinformatics online.

Inverse correlation of valproic acid serum concentrations and quality of life in adolescents with epilepsy

Correlation between steady-state serum concentrations of antiepileptic drugs and both seizure control and adverse drug reactions frequency (two major determinants of quality of life in patients with epilepsy) is still matter of controversy. The aim of our study was to investigate whether a correlation exists between steady-state serum concentration of valproic acid and quality of life in adolescent patients with epilepsy. Twenty-one adolescent patients with epilepsy, treated with valproic acid for more than 6 months entered the study. On two occasions, 3 months apart, both through and 2-h-after-the-dose serum concentrations of valproic acid were measured, as well as quality of life, using QOLIE-AD-48 for adolescents. Adverse drug reactions and seizure control were also recorded. Significant inverse correlation between through serum concentrations of valproic acid and total QOLIE-AD-48 scores was observed, together with correlation between through serum concentrations and adverse drug reactions frequency. The scores of memory/concentration and physical functioning QOLIEAD-48 domains were significantly and inversely correlated with through serum concentrations. Our study suggests that therapeutic monitoring of valproic acid serum concentrations could be useful predictor and marker of the most important epilepsy treatment outcome--quality of life.