Strategies for improving adherence to antiepileptic drug treatment in people with epilepsy
Notice bibliographique
Résumé
BACKGROUND: Poor adherence to antiepileptic medication is associated with increased mortality, morbidity and healthcare costs. In this review, we focus on interventions designed and tested in randomised controlled trials (RCTs) and quasi-RCTs to assist people with adherence to antiepileptic medication. This is an update of a Cochrane review first published in 2011, and last updated in 2017. OBJECTIVES: To determine the effectiveness of interventions aimed at improving adherence to antiepileptic medication in adults and children with epilepsy. SEARCH METHODS: For the latest update, we searched the following databases on 18 February 2020: Cochrane Register of Studies (CRS Web), MEDLINE, CINAHL Plus and PsycINFO. CRS Web includes RCTs or quasi-RCTs from PubMed, Embase, ClinicalTrials.gov, the World Health Organization International Clinical Trials Registry Platform (ICTRP), CENTRAL, and the Specialized Registers of Cochrane Review Groups including Epilepsy. We also searched the reference lists of relevant articles. SELECTION CRITERIA: RCTs and quasi-RCTs of adherence-enhancing interventions aimed at people with a clinical diagnosis of epilepsy (as defined in individual studies), of any age and treated with antiepileptic drugs in a primary care, outpatient or other community setting. DATA COLLECTION AND ANALYSIS: All review authors independently assessed lists of potentially relevant citations and abstracts. At least two review authors independently extracted data and performed a quality assessment of each study according to the Cochrane tool for assessing risk of bias. We graded the level of evidence for each outcome according to GRADE. The studies differed widely according to the type of intervention and measures of adherence; therefore combining data was not appropriate. MAIN RESULTS: We included 20 studies reporting data on 2832 participants. Thirteen studies targeted adults with epilepsy, one study included participants of all ages, one study included participants older than two years, one recruited pediatric patients aged between 1 month to 15 years, one study targeted caregivers of children with epilepsy, one targeted adolescents and caregivers, and two studies targeted families of children with epilepsy. We identified three ongoing studies. Follow-up time was generally short in most studies, ranging from 1 to 12 months. The studies examined three main types of interventions: educational interventions, behavioural interventions and mixed interventions. All but three studies compared treatment with usual care or 'no intervention'. Due to heterogeneity between studies in terms of interventions, methods used to measure adherence and the way the studies were reported, we did not pool the results and these findings were inappropriate to be included in a meta-analysis. Education and counselling of participants with epilepsy had mixed success (moderate-certainty evidence). Behavioural interventions such as the use of intensive reminders provided more favourable effects on adherence (moderate-certainty evidence). The effect on adherence to antiepileptic drugs described by studies of mixed interventions showed improved adherence in the intervention groups compared to the control groups (high-certainty evidence). Eleven studies described seizure frequency or seizure severity or both, with four of them, reporting improved adherence and decreased seizure frequency in the intervention groups (moderate-certainty evidence). Findings related to self-efficacy and quality of life were mixed, with no clear pattern across types of intervention. AUTHORS' CONCLUSIONS: Behavioural interventions such as intensive reminders and the use of mixed interventions demonstrate some positive results, however, we need more reliable evidence on their efficacy, derived from carefully-designed RCTs before we can draw a firm conclusion. None of the newly included studies have provided additional information that would lead to significant changes in our conclusions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,006 |
| Bibliométrie | 0,006 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».