Occupation and motor neuron disease: a New Zealand case-control study.

OBJECTIVES: To assess associations between occupation and motor neuron disease (MND). METHODS: We conducted a population-based case-control study with cases (n=321) recruited through the New Zealand Motor Neurone Disease Association and hospital discharge data. Controls (n=605) were recruited from the Electoral Roll. Information on personal and demographic details, lifestyle factors and a full occupational history was collected using questionnaires and interviews. Associations with ever/never employed and employment duration were estimated using logistic regression stratified by sex and adjusted for age, ethnicity, socioeconomic deprivation, education and smoking. RESULTS: Elevated risks were observed for field crop and vegetable growers (OR 2.93, 95% CI 1.10 to 7.77); fruit growers (OR 2.03, 95% CI 1.09 to 3.78); gardeners and nursery growers (OR 1.96, 95% CI 1.01 to 3.82); crop and livestock producers (OR 3.61, 95% CI 1.44 to 9.02); fishery workers, hunters and trappers (OR 5.62, 95% CI 1.27 to 24.97); builders (OR 2.90, 95% CI 1.41 to 5.96); electricians (OR 3.61, 95% CI 1.34 to 9.74); caregivers (OR 2.65, 95% CI 1.04 to 6.79); forecourt attendants (OR 8.31, 95% CI 1.79 to 38.54); plant and machine operators and assemblers (OR 1.42, 95% CI 1.01 to 2.01); telecommunications technicians (OR 4.2, 95% CI 1.20 to 14.64); and draughting technicians (OR 3.02, 95% CI 1.07 to 8.53). Industries with increased risks were agriculture (particularly horticulture and fruit growing), construction, non-residential care services, motor vehicle retailing, and sport and recreation. Positive associations between employment duration and MND were shown for the occupations fruit growers, gardeners and nursery growers, and crop and livestock producers, and for the horticulture and fruit growing industry. CONCLUSIONS: This study suggests associations between MND and occupations in agriculture and several other occupations

EpiNet as a way of involving more physicians and patients in epilepsy research: validation study and accreditation process

Objective EpiNet was established to encourage epilepsy research. EpiNet is used for multicenter cohort studies and investigator‐led trials. Physicians must be accredited to recruit patients into trials. Here, we describe the accreditation process for the EpiNet‐First trials. Methods Physicians with an interest in epilepsy were invited to assess 30 case scenarios to determine the following: whether patients have epilepsy; the nature of the seizures (generalized, focal); and the etiology. Information was presented in two steps for 23 cases. The EpiNet steering committee determined that 21 cases had epilepsy. The steering committee determined by consensus which responses were acceptable for each case. We chose a subset of 18 cases to accredit investigators for the EpiNet‐First trials. We initially focused on 12 cases; to be accredited, investigators could not diagnose epilepsy in any case that the steering committee determined did not have epilepsy. If investigators were not accredited after assessing 12 cases, 6 further cases were considered. When assessing the 18 cases, investigators could be accredited if they diagnosed one of six nonepilepsy patients as having possible epilepsy but could make no other false‐positive errors and could make only one error regarding seizure classification. Results Between December 2013 and December 2014, 189 physicians assessed the 30 cases. Agreement with the steering committee regarding the diagnosis at step 1 ranged from 47% to 100%, and improved when information regarding tests was provided at step 2. One hundred five of the 189 physicians (55%) were accredited for the EpiNet‐First trials. The kappa value for diagnosis of epilepsy across all 30 cases for accredited physicians was 0.70. Significance We have established criteria for accrediting physicians using EpiNet. New investigators can be accredited by assessing 18 case scenarios. We encourage physicians with an interest in epilepsy to become EpiNet‐accredited and to participate in these investigator‐led clinical trials

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

Summary 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  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

Epilepsy in the elderly

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Characteristics of epileptiform discharge duration and interdischarge interval in genetic generalized epilepsies

We sought to investigate (1) the characteristics of epileptiform discharge (ED) duration and interdischarge interval (IDI) and (2) the influence of vigilance state on the ED duration and IDI in genetic generalized epilepsy (GGE). In a cohort of patients diagnosed with GGE, 24-h ambulatory EEG recordings were performed prospectively. We then tabulated durations, IDI, and vigilance state in relation to all EDs captured on EEGs. We used K-means cluster analysis and finite mixture modeling to quantify and characterize the groups of ED duration and IDI. To investigate the influence of sleep, we calculated the mean, median, and SEM in each population from all subjects for sleep state and wakefulness separately, followed by the Kruskal-Wallis test to compare the groups. We analyzed 4,679 EDs and corresponding IDI from 23 abnormal 24-h ambulatory EEGs. Our analysis defined two populations of ED durations and IDI: short and long. In all populations, both ED durations and IDI were significantly longer in wakefulness. Our results highlight different characteristics of ED populations in GGE and the influence by the sleep-wake cycle

Influence of comorbidity on mortality in patients with epilepsy and psychogenic nonepileptic seizures

This study aims to determine the contribution of comorbidities to excess psychogenic nonepileptic seizures (PNES) mortality.M.T. was supported by the Epilepsy Society of Australia UCB Pharma scholarship. The study was supported by an investigator-initiated project grant from UCB Pharma Australia. UCB was not involved in the study design, the collection, analysis, and interpretation of the data gathered, the writing of the report, or the decision to submit the article for publication.Peer reviewe

The Liverpool Adverse Events Profile (LAEP) reflects anxiety and depression rather than antiepileptic drug side effects in individual patients

Abstract not available

The use of computer-assisted-telephone-interviewing to diagnose seizures, epilepsy and idiopathic generalized epilepsy.

BACKGROUND: Computer-assisted-telephone-interviewing (CATI), widely used in market research, could be a useful alternative for conducting diagnostic interviews in epilepsy epidemiology. METHODS: We administered a diagnostic seizure questionnaire by CATI, interpreting the responses with standardized classification guidelines, compared against an epilepsy specialist's assessment, for agreement [Kappa statistic (kappa)], sensitivity, specificity, positive predictive value, negative predictive value and Youden's Index (YI). RESULTS: 99 outpatients with 382 lifetime events participated: 22 generalized-onset epilepsy [16 Idiopathic Generalized Epilepsy (IGE)], 59 partial-onset epilepsy, 12 non-epileptic and 6 uncertain. We observed almost perfect agreement in diagnosing epilepsy (kappa=0.94), seizure-onset types (kappa=0.84), simple or complex partial seizures (kappa=0.87), any generalized non-convulsive seizure (kappa=0.82), and IGE (kappa=0.82). Although substantial, agreement was not as close for secondarily generalized seizures (kappa=0.74), and generalized tonic-clonic seizures (kappa=0.79). This related more to under-recognition of individual generalized non-convulsive seizures rather than misinterpretation of partial seizures. DISCUSSION: Epilepsy diagnostic questionnaires administered by CATI and interpreted with standardized diagnostic guidelines can effectively classify epilepsy, most seizure types and IGE in outpatients with suspected seizures. Applying this diagnostic method in 'field' settings will allow firmer conclusions to be drawn on its wider epidemiological utility