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Enregistrement W3216344858 · doi:10.3310/hta25690

Antidepressant medication to prevent depression relapse in primary care: the ANTLER RCT

2021· article· en· W3216344858 sur OpenAlexaff
Larisa Duffy, Caroline S. Clarke, Gemma Lewis, Louise Marston, Nick Freemantle, Simon Gilbody, Rachael Hunter, Tony Kendrick, David Keßler, Michael King, Paul Lanham, Dee Mangin, Michael Moore, Irwin Nazareth, Nicola Wiles, Faye Bacon, Sally Brabyn, Alison Burns, Yvonne Donkor, Anna Hunt, Jodi Pervin, Glyn Lewis

Notice bibliographique

RevueHealth Technology Assessment · 2021
Typearticle
Langueen
DomainePsychology
ThématiqueMental Health Treatment and Access
Établissements canadiensMcMaster University
Organismes subventionnairesHealth Technology Assessment ProgrammeUniversity College LondonNational Institute for Health and Care Research
Mots-clésMedicineMirtazapineSertralineRandomized controlled trialDepression (economics)VenlafaxineAntidepressantMajor depressive episodeFluoxetinePsychiatryCitalopramPhysical therapyInternal medicineAnxietyMood

Résumé

récupéré en direct d'OpenAlex

Background There has been a steady increase in the number of primary care patients receiving long-term maintenance antidepressant treatment, despite limited evidence of a benefit of this treatment beyond 8 months. Objective The ANTidepressants to prevent reLapse in dEpRession (ANTLER) trial investigated the clinical effectiveness and cost-effectiveness of antidepressant medication in preventing relapse in UK primary care. Design This was a Phase IV, double-blind, pragmatic, multisite, individually randomised parallel-group controlled trial, with follow-up at 6, 12, 26, 39 and 52 weeks. Participants were randomised using minimisation on centre, type of antidepressant and baseline depressive symptom score above or below the median using Clinical Interview Schedule – Revised (two categories). Statisticians were blind to allocation for the outcome analyses. Setting General practices in London, Bristol, Southampton and York. Participants Individuals aged 18–74 years who had experienced at least two episodes of depression and had been taking antidepressants for ≥ 9 months but felt well enough to consider stopping their medication. Those who met an International Statistical Classification of Diseases and Related Health Problems , Tenth Revision, diagnosis of depression or with other psychiatric conditions were excluded. Intervention At baseline, participants were taking citalopram 20 mg, sertraline 100 mg, fluoxetine 20 mg or mirtazapine 30 mg. They were randomised to either remain on their current medication or discontinue medication after a tapering period. Main outcome measures The primary outcome was the time, in weeks, to the beginning of the first depressive episode after randomisation. This was measured by a retrospective Clinical Interview Schedule – Revised that assessed the onset of a depressive episode in the previous 12 weeks, and was conducted at 12, 26, 39 and 52 weeks. The depression-related resource use was collected over 12 months from medical records and patient-completed questionnaires. Quality-adjusted life-years were calculated using the EuroQol-5 Dimensions, five-level version. Results Between 9 March 2017 and 1 March 2019, we randomised 238 participants to antidepressant continuation (the maintenance group) and 240 participants to antidepressant discontinuation (the discontinuation group). The time to relapse of depression was shorter in the discontinuation group, with a hazard ratio of 2.06 (95% confidence interval 1.56 to 2.70; p < 0.0001). By 52 weeks, relapse was experienced by 39% of those who continued antidepressants and 56% of those who discontinued antidepressants. The secondary analysis revealed that people who discontinued experienced more withdrawal symptoms than those who remained on medication, with the largest difference at 12 weeks. In the discontinuation group, 37% (95% confidence interval 28% to 45%) of participants remained on their randomised medication until the end of the trial. In total, 39% (95% confidence interval 32% to 45%) of participants in the discontinuation group returned to their original antidepressant compared with 20% (95% confidence interval 15% to 25%) of participants in maintenance group. The health economic evaluation demonstrated that participants randomised to discontinuation had worse utility scores at 3 months (–0.037, 95% confidence interval –0.059 to –0.015) and fewer quality-adjusted life-years over 12 months (–0.019, 95% confidence interval –0.035 to –0.003) than those randomised to continuation. The discontinuation pathway, besides giving worse outcomes, also cost more [extra £2.71 per patient over 12 months (95% confidence interval –£36.10 to £37.07)] than the continuation pathway, although the cost difference was not significant. Conclusions Patients who discontinue long-term maintenance antidepressants in primary care are at increased risk of relapse and withdrawal symptoms. However, a substantial proportion of patients can discontinue antidepressants without relapse. Our findings will give patients and clinicians an estimate of the likely benefits and harms of stopping long-term maintenance antidepressants and improve shared decision-making. The participants may not have been representative of all people on long-term maintenance treatment and we could study only a restricted range of antidepressants and doses. Identifying patients who will not relapse if they discontinued antidepressants would be clinically important. Trial registration Current Controlled Trials ISRCTN15969819 and EudraCT 2015-004210-26. Funding This project was funded by the National Institute for Health Research (NIHR) Health Technology Assessment programme and will be published in full in Health Technology Assessment ; Vol. 25, No. 69. See the NIHR Journals Library website for further project information.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,589
Score d'incertitude au seuil0,642

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,025
Tête enseignante GPT0,415
Écart entre enseignants0,390 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations24
Publié2021
Routes d'admission1
Résumé présentoui

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