Development of an International Standard Set of Outcomes and Measurement Methods for Routine Practice for Infants, Children, and Adolescents with Epilepsy: The International Consortium for Health Outcomes Measurement Consensus Recommendations.

At present, there is no internationally accepted set of core outcomes or measurement methods for epilepsy clinical practice. The International Consortium for Health Outcomes Measurement (ICHOM) convened an international working group of experts in epilepsy, people with epilepsy, and their representatives to develop minimum sets of standardized outcomes and outcome measurement methods for clinical practice. Using modified Delphi consensus methods with consecutive rounds of online voting over 12 months, a core set of outcomes and corresponding measurement tool packages to capture the outcomes were identified for infants, children, and adolescents with epilepsy. Consensus methods identified 20 core outcomes. In addition to the outcomes identified for the ICHOM Epilepsy adult standard set, behavioral, motor, and cognitive/language development outcomes were voted as essential for all infants and children with epilepsy. The proposed set of outcomes and measurement methods will facilitate the implementation of the use of patient-centered outcomes in daily practice.

Development of an International Standard Set of Outcomes and Measurement Methods for Routine Practice for Adults with Epilepsy: The International Consortium for Health Outcomes Measurement Consensus Recommendations.

At present, there is no internationally accepted set of core outcomes or measurement methods for epilepsy clinical practice. Therefore, the International Consortium for Health Outcomes Measurement (ICHOM) convened an international working group of experts in epilepsy, people with epilepsy and their representatives to develop minimum sets of standardized outcomes and outcomes measurement methods for clinical practice that support patient-clinician decision-making and quality improvement. Consensus methods identified 20 core outcomes. Measurement tools were recommended based on their evidence of strong clinical measurement properties, feasibility, and cross-cultural applicability. The essential outcomes included many non-seizure outcomes: anxiety, depression, suicidality, memory and attention, sleep quality, functional status, and the social impact of epilepsy. The proposed set will facilitate the implementation of the use of patient-centered outcomes in daily practice, ensuring holistic care. They also encourage harmonization of outcome measurement, and if widely implemented should reduce the heterogeneity of outcome measurement, accelerate comparative research, and facilitate quality improvement efforts.

Prognosis of adults and children following a first unprovoked seizure.

BACKGROUND: Epilepsy is clinically defined as two or more unprovoked epileptic seizures more than 24 hours apart. Given that, a diagnosis of epilepsy can be associated with significant morbidity and mortality, it is imperative that clinicians (and people with seizures and their relatives) have access to accurate and reliable prognostic estimates, to guide clinical practice on the risks of developing further unprovoked seizures (and by definition, a diagnosis of epilepsy) following single unprovoked epileptic seizure. OBJECTIVES: 1. To provide an accurate estimate of the proportion of individuals going on to have further unprovoked seizures at subsequent time points following a single unprovoked epileptic seizure (or cluster of epileptic seizures within a 24-hour period, or a first episode of status epilepticus), of any seizure type (overall prognosis). 2. To evaluate the mortality rate following a first unprovoked epileptic seizure. SEARCH METHODS: We searched the following databases on 19 September 2019 and again on 30 March 2021, with no language restrictions. The Cochrane Register of Studies (CRS Web), MEDLINE Ovid (1946 to March 29, 2021), SCOPUS (1823 onwards), ClinicalTrials.gov, the World Health Organization (WHO) International Clinical Trials Registry Platform (ICTRP). CRS Web includes randomized or quasi-randomized, controlled trials from PubMed, Embase, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform (ICTRP), the Cochrane Central Register of Controlled Trials (CENTRAL), and the Specialized Registers of Cochrane Review Groups including Epilepsy. In MEDLINE (Ovid) the coverage end date always lags a few days behind the search date. SELECTION CRITERIA: We included studies, both retrospective and prospective, of all age groups (except those in the neonatal period (< 1 month of age)), of people with a single unprovoked seizure, followed up for a minimum of six months, with no upper limit of follow-up, with the study end point being seizure recurrence, death, or loss to follow-up. To be included, studies must have included at least 30 participants. We excluded studies that involved people with seizures that occur as a result of an acute precipitant or provoking factor, or in close temporal proximity to an acute neurological insult, since these are not considered epileptic in aetiology (acute symptomatic seizures). We also excluded people with situational seizures, such as febrile convulsions. DATA COLLECTION AND ANALYSIS: Two review authors conducted the initial screening of titles and abstracts identified through the electronic searches, and removed non-relevant articles. We obtained the full-text articles of all remaining potentially relevant studies, or those whose relevance could not be determined from the abstract alone and two authors independently assessed for eligibility. All disagreements were resolved through discussion with no need to defer to a third review author. We extracted data from included studies using a data extraction form based on the checklist for critical appraisal and data extraction for systematicreviews of prediction modelling studies (CHARMS). Two review authors then appraised the included studies, using a standardised approach based on the quality in prognostic studies (QUIPS) tool, which was adapted for overall prognosis (seizure recurrence). We conducted a meta-analysis using Review Manager 2014, with a random-effects generic inverse variance meta-analysis model, which accounted for any between-study heterogeneity in the prognostic effect. We then summarised the meta-analysis by the pooled estimate (the average prognostic factor effect), its 95% confidence interval (CI), the estimates of I² and Tau² (heterogeneity), and a 95% prediction interval for the prognostic effect in a single population at three various time points, 6 months, 12 months and 24 months. Subgroup analysis was performed according to the ages of the cohorts included; studies involving all ages, studies that recruited adult only and those that were purely paediatric. MAIN RESULTS: Fifty-eight studies (involving 54 cohorts), with a total of 12,160 participants (median 147, range 31 to 1443), met the inclusion criteria for the review. Of the 58 studies, 26 studies were paediatric studies, 16 were adult and the remaining 16 studies were a combination of paediatric and adult populations. Most included studies had a cohort study design with two case-control studies and one nested case-control study. Thirty-two studies (29 cohorts) reported a prospective longitudinal design whilst 15 studies had a retrospective design whilst the remaining studies were randomised controlled trials. Nine of the studies included presented mortality data following a first unprovoked seizure. For a mortality study to be included, a proportional mortality ratio (PMR) or a standardised mortality ratio (SMR) had to be given at a specific time point following a first unprovoked seizure. To be included in the meta-analysis a study had to present clear seizure recurrence data at 6 months, 12 months or 24 months. Forty-six studies were included in the meta-analysis, of which 23 were paediatric, 13 were adult, and 10 were a combination of paediatric and adult populations. A meta-analysis was performed at three time points; six months, one year and two years for all ages combined, paediatric and adult studies, respectively. We found an estimated overall seizure recurrence of all included studies at six months of 27% (95% CI 24% to 31%), 36% (95% CI 33% to 40%) at one year and 43% (95% CI 37% to 44%) at two years, with slightly lower estimates for adult subgroup analysis and slightly higher estimates for paediatric subgroup analysis. It was not possible to provide a summary estimate of the risk of seizure recurrence beyond these time points as most of the included studies were of short follow-up and too few studies presented recurrence rates at a single time point beyond two years. The evidence presented was found to be of moderate certainty. AUTHORS' CONCLUSIONS: Despite the limitations of the data (moderate-certainty of evidence), mainly relating to clinical and methodological heterogeneity we have provided summary estimates for the likely risk of seizure recurrence at six months, one year and two years for both children and adults. This provides information that is likely to be useful for the clinician counselling patients (or their parents) on the probable risk of further seizures in the short-term whilst acknowledging the paucity of long-term recurrence data, particularly beyond 10 years.

Key Drivers and Facilitators of the Choice to Use mHealth Technology in People With Neurological Conditions: Observational Study.

BACKGROUND: There is increasing interest in the potential uses of mobile health (mHealth) technologies, such as wearable biosensors, as supplements for the care of people with neurological conditions. However, adherence is low, especially over long periods. If people are to benefit from these resources, we need a better long-term understanding of what influences patient engagement. Previous research suggests that engagement is moderated by several barriers and facilitators, but their relative importance is unknown. OBJECTIVE: To determine preferences and the relative importance of user-generated factors influencing engagement with mHealth technologies for 2 common neurological conditions with a relapsing-remitting course: multiple sclerosis (MS) and epilepsy. METHODS: In a discrete choice experiment, people with a diagnosis of MS (n=141) or epilepsy (n=175) were asked to select their preferred technology from a series of 8 vignettes with 4 characteristics: privacy, clinical support, established benefit, and device accuracy; each of these characteristics was greater or lower in each vignette. These characteristics had previously been emphasized by people with MS and or epilepsy as influencing engagement with technology. Mixed multinomial logistic regression models were used to establish which characteristics were most likely to affect engagement. Subgroup analyses explored the effects of demographic factors (such as age, gender, and education), acceptance of and familiarity with mobile technology, neurological diagnosis (MS or epilepsy), and symptoms that could influence motivation (such as depression). RESULTS: Analysis of the responses to the discrete choice experiment validated previous qualitative findings that a higher level of privacy, greater clinical support, increased perceived benefit, and better device accuracy are important to people with a neurological condition. Accuracy was perceived as the most important factor, followed by privacy. Clinical support was the least valued of the attributes. People were prepared to trade a modest amount of accuracy to achieve an improvement in privacy, but less likely to make this compromise for other factors. The type of neurological condition (epilepsy or MS) did not influence these preferences, nor did the age, gender, or mental health status of the participants. Those who were less accepting of technology were the most concerned about privacy and those with a lower level of education were prepared to trade accuracy for more clinical support. CONCLUSIONS: For people with neurological conditions such as epilepsy and MS, accuracy (ie, the ability to detect symptoms) is of the greatest interest. However, there are individual differences, and people who are less accepting of technology may need far greater reassurance about data privacy. People with lower levels of education value greater clinician involvement. These patient preferences should be considered when designing mHealth technologies.