Search

Valproate is the most effective treatment for idiopathic generalized epilepsy. Current guidance precludes its use in females of childbearing potential, unless other treatments are ineffective or not tolerated, because of high teratogenicity. This risk was recently extended to males. New guidance will limit use both in males and females aged <55 years, resulting in withdrawal of valproate from males already taking it, as occurs for females. Whether there are risks of personal harm (including injury or death) associated with valproate withdrawal has not yet been quantified for males or females ON valproate, meaning clinicians cannot reliably counsel either sex when discussing valproate withdrawal with them, despite that this concern may be at the forefront of patients' and clinicians' minds. We assessed whether there are any morbidity or mortality risks associated with valproate withdrawal in young males and females. We performed a retrospective cohort study of internationally derived electronic health data within the TriNetX Global Collaborative Network. Included were males and females aged 16–54 years with ≥1 epilepsy disease or symptom code between 1 December 2017 and 1 December 2018, and ≥2 valproate prescriptions over the preceding 2 years (1 January 2015–30 November 2017). Five-year propensity-matched risks of mortality and a range of morbidity outcomes were compared between those remaining ON versus withdrawn from valproate during the 1 December 2017–1 December 2018 recruitment period, regardless of whether switched to another antiseizure medication. Survival analysis was undertaken using Cox-proportional hazard models, generating hazard ratios (HRs) with 95% confidence intervals (CIs). In total, 8991 males and 5243 females taking valproate were recruited. Twenty-eight per cent of males and 36% of females were subsequently withdrawn from valproate. Valproate withdrawal was associated with significantly increased risks of emergency department attendance [HRs overall: 1.236 (CI 1.159–1.319), males: 1.181 (CI 1.083–1.288), females: 1.242 (CI 1.125–1.371)], hospital admission [HRs overall: 1.160 (CI 1.081–1.246), males: 1.132 (CI 1.027–1.249), females: 1.147 (CI 1.033–1.274)], falls [HRs overall: 1.179 (CI 1.041–1.336), males: 1.298 (CI 1.090–1.546)], injuries [HRs overall: 1.095 (CI 1.021–1.174), males: 1.129 (CI 1.029–1.239)], burns [HRs overall: 1.592 (CI 1.084–2.337)] and new-onset depression [HRs overall 1.323 (CI 1.119–1.565), females: 1.359 (CI 1.074–1.720)]. The risk of these outcomes occurring was 1%–7% higher in those withdrawn from valproate than in those remaining ON valproate. Overall, valproate withdrawal was not associated with increased mortality. These results may help patients and clinicians have a more informed discussion about personal safety when considering valproate withdrawal. [ABSTRACT FROM AUTHOR]

Valproate is the most effective treatment for idiopathic generalised epilepsy. Currently, its use is restricted in women of childbearing potential owing to high teratogenicity. Recent evidence extended this risk to men's offspring, prompting recommendations to restrict use in everybody aged <55 years. This study will evaluate mortality and morbidity risks associated with valproate withdrawal by emulating a hypothetical randomised-controlled trial (called a "target trial") using retrospective observational data. The data will be drawn from ~250m mainly US patients in the TriNetX repository and ~60m UK patients in Clinical Practice Research Datalink (CPRD). These will be scanned for individuals aged 16–54 years with epilepsy and on valproate who either continued, switched to lamotrigine or levetiracetam, or discontinued valproate between 2014–2024, creating four groups. Randomisation to these groups will be emulated by baseline confounder adjustment using g-methods. Mortality and morbidity outcomes will be assessed and compared between groups over 1–10 years, employing time-to-first-event and recurrent events analyses. A causal prediction model will be developed from these data to aid in predicting the safest alternative antiseizure medications. Together, these findings will optimise informed decision-making about valproate withdrawal and alternative treatment selection, providing immediate and vital information for patients, clinicians and regulators. [ABSTRACT FROM AUTHOR]

Objective: A diagnosis of epilepsy has been associated with adverse cardiovascular events (CEs), but the extent to which antiseizure medications (ASMs) may contribute to this is not well understood. The aim of this study was to compare the risk of adverse CEs associated with ASM in patients with epilepsy (PWE). Methods: A retrospective case–control cohort study was conducted using TriNetX, a global health federated network of anonymized patient records. Patients older than 18 years, with a diagnosis of epilepsy (International Classification of Diseases, 10th Revision code G40) and a medication code of carbamazepine, lamotrigine, or valproate were compared. Patients with cardiovascular disease prior to the diagnosis of epilepsy were excluded. Cohorts were 1:1 propensity score matched (PSM) according to age, sex, ethnicity, hypertension, heart failure, atherosclerotic heart disease, atrial and cardiac arrythmias, diabetes, disorders of lipoprotein metabolism, obesity, schizophrenia and bipolar disorder, medications, and epilepsy classification. The primary outcome was a composite of adverse CEs (ischemic stroke, acute ischemic heart disease, and heart failure) at 10 years. Cox regression analyses were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) following 1:1 PSM. Results: Of 374 950 PWE included; three cohorts were established after PSM: (1) carbamazepine compared to lamotrigine, n = 4722, mean age 37.4 years; (2) valproate compared to lamotrigine, n = 5478, mean age 33.9 years; and (3) valproate compared to carbamazepine, n = 4544, mean age 37.0 years. Carbamazepine and valproate use were associated with significantly higher risk of composite cardiovascular outcome compared to lamotrigine (HR = 1.390, 95% CI = 1.160–1.665 and HR = 1.264, 95% CI = 1.050–1.521, respectively). Valproate was associated with a 10‐year higher risk of all‐cause death than carbamazepine (HR = 1.226, 95% CI = 1.017–1.478), but risk of other events was not significantly different. Significance: Carbamazepine and valproate were associated with increased CE risks compared to lamotrigine. Cardiovascular risk factor monitoring and careful follow‐up should be considered for these patients. [ABSTRACT FROM AUTHOR]

The critical success index (CSI) is an established metric used in meteorology to verify the accuracy of weather forecasts. It is defined as the ratio of hits to the sum of hits, false alarms, and misses. Translationally, CSI has gained popularity as a unitary outcome measure in various clinical situations where large numbers of true negatives may influence the interpretation of other, more traditional, outcome measures, such as specificity (Spec) and negative predictive value (NPV), or when unified interpretation of positive predictive value (PPV) and sensitivity (Sens) is needed. The derivation of CSI from measures including PPV has prompted questions as to whether and how CSI values may vary with disease prevalence (P), just as PPV estimates are dependent on P, and hence whether CSI values are generalizable between studies with differing prevalences. As no detailed study of the relation of CSI to prevalence has been undertaken hitherto, the dataset of a previously published test accuracy study of a cognitive screening instrument was interrogated to address this question. Three different methods were used to examine the change in CSI across a range of prevalences, using both the Bayes formula and equations directly relating CSI to Sens, PPV, P, and the test threshold (Q). These approaches showed that, as expected, CSI does vary with prevalence, but the dependence differs according to the method of calculation that is adopted. Bayesian rescaling of both Sens and PPV generates a concave curve, suggesting that CSI will be maximal at a particular prevalence, which may vary according to the particular dataset. [ABSTRACT FROM AUTHOR]

This study aimed to develop a risk prediction model for epilepsy-related death in adults. In this age- and sex-matched case-control study, we compared adults (aged ≥16 years) who had epilepsy-related death between 2009 and 2016 to living adults with epilepsy in Scotland. Cases were identified from validated administrative national datasets linked to mortality records. ICD-10 cause-of-death coding was used to define epilepsy-related death. Controls were recruited from a research database and epilepsy clinics. Clinical data from medical records were abstracted and used to undertake univariable and multivariable conditional logistic regression to develop a risk prediction model consisting of four variables chosen a priori. A weighted sum of the factors present was taken to create a risk index—the Scottish Epilepsy Deaths Study Score. Odds ratios were estimated with 95% confidence intervals (CIs). Here, 224 deceased cases (mean age 48 years, 114 male) and 224 matched living controls were compared. In univariable analysis, predictors of epilepsy-related death were recent epilepsy-related accident and emergency attendance (odds ratio 5.1, 95% CI 3.2–8.3), living in deprived areas (odds ratio 2.5, 95% CI 1.6–4.0), developmental epilepsy (odds ratio 3.1, 95% CI 1.7–5.7), raised Charlson Comorbidity Index score (odds ratio 2.5, 95% CI 1.2–5.2), alcohol abuse (odds ratio 4.4, 95% CI 2.2–9.2), absent recent neurology review (odds ratio 3.8, 95% CI 2.4–6.1) and generalized epilepsy (odds ratio 1.9, 95% CI 1.2–3.0). Scottish Epilepsy Deaths Study Score model variables were derived from the first four listed before, with Charlson Comorbidity Index ≥2 given 1 point, living in the two most deprived areas given 2 points, having an inherited or congenital aetiology or risk factor for developing epilepsy given 2 points and recent epilepsy-related accident and emergency attendance given 3 points. Compared to having a Scottish Epilepsy Deaths Study Score of 0, those with a Scottish Epilepsy Deaths Study Score of 1 remained low risk, with odds ratio 1.6 (95% CI 0.5–4.8). Those with a Scottish Epilepsy Deaths Study Score of 2–3 had moderate risk, with odds ratio 2.8 (95% CI 1.3–6.2). Those with a Scottish Epilepsy Deaths Study Score of 4–5 and 6–8 were high risk, with odds ratio 14.4 (95% CI 5.9–35.2) and 24.0 (95% CI 8.1–71.2), respectively. The Scottish Epilepsy Deaths Study Score may be a helpful tool for identifying adults at high risk of epilepsy-related death and requires external validation. [ABSTRACT FROM AUTHOR]

The Critical Success Index (CSI) and Gilbert Skill score (GS) are verification measures that are commonly used to check the accuracy of weather forecasting. In this article, we propose that they can also be used to simplify the joint interpretation of positive predictive value (PPV) and sensitivity estimates across diagnostic accuracy studies of epilepsy data. This is because CSI and GS each provide a single measure that takes the weather forecasting equivalent of PPV and sensitivity into account. We have re‐analysed data from our recent systematic review of diagnostic accuracy studies of administrative epilepsy data using CSI and GS. We summarise the results and benefits of this approach. [ABSTRACT FROM AUTHOR]

Objective: This study was undertaken to investigate the trends and mechanisms of epilepsy‐related deaths in Scotland, highlighting the proportion that were potentially avoidable. Methods: This was a retrospective observational data‐linkage study of administrative data from 2009–2016. We linked nationwide data encompassing mortality records, hospital admissions, outpatient attendance, antiepileptic drug (AED) prescriptions, and regional primary care attendances. Adults (aged ≥16 years) suffering epilepsy‐related death were identified for study using International Classification of Diseases, 10th Revision coding combined with AED prescriptions. We reported epilepsy‐related mortality rate (MR), age‐specific mortality ratios, multiple cause‐of‐death frequencies, and the proportion of potentially avoidable deaths (identified as those with an underlying cause listed as avoidable by the Office for National Statistics). Results: A total of 1921 epilepsy‐related deaths were identified across Scotland; 1185 (62%) decedents were hospitalized for seizures in the years leading up to death, yet only 518 (27%) were seen in a neurology clinic during the same period. MR remained unchanged over time, ranging from 5.9 to 8.7 per 100 000 Scottish population (95% confidence interval [CI] = −.05 to.66 per 100 000 for annual change in MR). Mortality ratios were significantly increased in young adults aged 16–54 years (2.3, 95% CI = 1.8–2.8), peaking at age 16–24 years (5.3, 95% CI = 1.8–8.8). Sudden unexpected death in epilepsy (SUDEP) constituted 30% of the 553 young adult epilepsy‐related deaths, with several other non‐SUDEP fatal mechanisms identified including aspiration pneumonia, cardiac arrest, AED or narcotic poisoning, drowning, and alcohol dependence. Seventy‐six percent of young adult epilepsy‐related deaths were potentially avoidable. Significance: Epilepsy‐related deaths are a major public health problem in Scotland, given that they are not reducing, people are dying young, and many deaths are potentially avoidable. SUDEP is only one of several important mechanisms by which epilepsy‐related deaths are occurring in young adults. Services may need to be re‐evaluated to improve specialist referral following seizure‐related hospital admissions. [ABSTRACT FROM AUTHOR]

Our objective was to undertake a systematic review ascertaining the accuracy of using administrative healthcare data to identify epilepsy cases. We searched MEDLINE and Embase from 01/01/1975 to 03/07/2018 for studies evaluating the diagnostic accuracy of routinely collected healthcare data in identifying epilepsy cases. Any disease coding system in use since the International Classification of Diseases, Ninth Revision (ICD‐9) was permissible. Two authors independently screened studies, extracted data, and quality‐assessed studies. We assessed positive predictive value (PPV), sensitivity, negative predictive value (NPV), and specificity. The primary analysis was a narrative synthesis of review findings. Thirty studies were included, published between 1989 and 2018. Risks of bias were low, high, and unclear in 4, 14, and 12 studies, respectively. Coding systems included ICD‐9, ICD‐10, and Read Codes, with or without antiepileptic drugs (AEDs). PPVs included ranges of 5.2%–100% (Canada), 32.7%–96.0% (USA), 47.0%–100% (UK), and 37.0%–88.0% (Norway). Sensitivities included ranges of 22.2%–99.7% (Canada), 12.2%–97.3% (USA), and 79.0%–94.0% (UK). Nineteen studies contained at least one algorithm with a PPV >80%. Sixteen studies contained at least one algorithm with a sensitivity >80%. PPV was highest in algorithms consisting of disease codes (ICD‐10 G40‐41, ICD‐9 345) in combination with one or more AEDs. The addition of symptom codes to this (ICD‐10 R56; ICD‐9 780.3, 780.39) lowered PPV. Sensitivity was highest in algorithms consisting of symptom codes with one or more AEDs. Although using AEDs alone achieved high sensitivities, the associated PPVs were low. Most NPVs and specificities were >90%. We conclude that it is reasonable to use administrative data to identify people with epilepsy (PWE) in epidemiological research. Studies prioritizing high PPVs should focus on combining disease codes with AEDs. Studies prioritizing high sensitivities should focus on combining symptom codes with AEDs. We caution against the use of AEDs alone to identify PWE. [ABSTRACT FROM AUTHOR]

The article discusses the sleep-related hypermotor epilepsy (SHE) or nocturnal frontal lobe epilepsy (NFLE) which is misdiagnosed as a non-epileptic sleep disorder. It provides an overview of normal sleep physiology to help contextualize nocturnal epilepsy and its differential diagnoses. Also discussed are the epidemiology of SHE, clinical history, differential diagnoses with generalized tonic-clonic seizures (GTCS), temporal lobe epilepsy (TLE) and parasomnias complex motor.

Introduction In an increasingly digital age for healthcare around the world, administrative data have become rich and accessible tools for potentially identifying and monitoring population trends in diseases including epilepsy. However, it remains unclear (1) how accurate administrative data are at identifying epilepsy within a population and (2) the optimal algorithms needed for administrative data to correctly identify people with epilepsy within a population. To address this knowledge gap, we will conduct a novel systematic review of all identified studies validating administrative healthcare data in epilepsy identification. We provide here a protocol that will outline the methods and analyses planned for the systematic review. Methods and analysis The systematic review described in this protocol will be conducted to follow the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE and Embase will be searched for studies validating administrative data in epilepsy published from 1975 to current (01 June 2018). Included studies will validate the International Classification of Disease (ICD), Ninth Revision (ICD-9) onwards (ie, ICD-9 code 345 and ICD-10 codes G40-G41) as well as other non-ICD disease classification systems used, such as Read Codes in the UK. The primary outcome will be providing pooled estimates of accuracy for identifying epilepsy within the administrative databases validated using sensitivity, specificity, positive and negative predictive values, and area under the receiver operating characteristic curves. Heterogeneity will be assessed using the I² statistic and descriptive analyses used where this is present. The secondary outcome will be the optimal administrative data algorithms for correctly identifying epilepsy. These will be identified using multivariable logistic regression models. 95% confidence intervals will be quoted throughout. We will make an assessment of risk of bias, quality of evidence, and completeness of reporting for included studies. Ethics and dissemination Ethical approval is not required as primary data will not be collected. Results will be disseminated in peer-reviewed journals, conference presentations and in press releases. PROSPERO registration CRD42017081212. [ABSTRACT FROM AUTHOR]

The article discusses the sound adoption of the critical success index (CSI) for the assessment of any screening or diagnostic test where there is low prevalence of the condition being sought.

The adverse effects profile of levetiracetam in epilepsy is still being fully described. We recently published a Cochrane Review evaluating the effectiveness of levetiracetam, added on to usual care, in treating drug-resistant focal epilepsy. The five most common adverse effects were reported and analysed with no scope for reporting any less common adverse effects than those. Here, we report and analyse the remaining adverse effects (including the five most common). These were (in decreasing order of frequency) somnolence; headache; asthenia; accidental injury; dizziness; infection; pharyngitis; pain; rhinitis; abdominal pain; flu syndrome; vomiting; diarrhoea; convulsion; nausea; increased cough; anorexia; upper respiratory tract infection; hostility; personality disorder; urinary tract infection; nervousness; depression; aggression; back pain; agitation; emotional liability; psychomotor hyperactivity; pyrexia; rash; ECG abnormalities; decreased appetite; nasal congestion; irritability; abnormal behaviour; epistaxis; insomnia; altered mood; anxiety; bloody urine; diplopia; dissociation; memory impairment; pruritis; increased appetite; acne; and stomach discomfort. Only somnolence and infection were significantly associated with levetiracetam. When adverse effects pertaining to infection were combined, these affected 19.7% and 15.1% of participants on levetiracetam and placebo (relative risk 1.16, CI 0.89-1.50, Chi2 heterogeneity p = 0.13). Somnolence and infection further retained significance in adults while no single adverse effect was significant in children. This review updates the adverse effects profile data on levetiracetam use by empirically reporting its common and uncommon adverse effects and analysing their relative importance statistically using data from a group of trials that posses low Risk of Bias and high Quality of Evidence GRADE scores. [ABSTRACT FROM AUTHOR]