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Enregistrement W4386267524 · doi:10.47626/1516-4446-2023-3126

Treatment resistance in schizophrenia: a meta-analysis of prevalence and correlates

2023· article· en· W4386267524 sur OpenAlexaff
Elton Diniz, Laís Fonseca, Deyvis Rocha, Alisson Paulino Trevizol, Raphael O. Cerqueira, Bruno Bertolucci Ortiz, André R. Brunoni, Rodrigo A. Bressan, Christoph U. Correll, Ary Gadelha

Notice bibliographique

RevueBrazilian Journal of Psychiatry · 2023
Typearticle
Langueen
DomaineMedicine
ThématiqueSchizophrenia research and treatment
Établissements canadiensCentre for Addiction and Mental Health
Organismes subventionnairesCilagConselho Nacional de Desenvolvimento Científico e TecnológicoDaiichi-SankyoCoordenação de Aperfeiçoamento de Pessoal de Nível SuperiorAllerganFundação de Amparo à Pesquisa do Estado de São Paulo
Mots-clésMeta-analysisSchizophrenia (object-oriented programming)Resistance (ecology)PsychologyClinical psychologyPsychiatryMedicineInternal medicineBiology

Résumé

récupéré en direct d'OpenAlex

Background: Despite its clinical relevance, comprehensive estimates on the prevalence of treatment resistance in schizophrenia-spectrum disorders are missing. This study aimed to systematically assess the prevalence and correlates of treatment-resistant schizophrenia (TRS). Method: An electronic search was conducted in PubMed and EMBASE for articles published until 05/17/2022, following PRISMA guidelines. Included studies were of patients with schizophrenia-spectrum disorders providing TRS prevalence information or allowing its calculation. Estimates within intent-to-treat and observed cases analyses were calculated via random-effects model meta-analysis. Sensitivity, subgroup and meta-regression analyses were performed. Findings: Across 49 studies (n=28392, mean age=38·3±6·2 years, males=64·8±12·1%, mean illness duration=14·3±5·3 years) the overall TRS prevalence was 36·7% (95%CI=33·0-40·5%, I2=97·3%, p<0·0001), ranging from 15·4% (95%CI=13·4-17·5%) to 75·6% (95%CI=69·1-81·3%). For first-episode psychosis samples (n=10579), the prevalence was 22·0% (95%CI=18·4-25·8%) vs. 39·5% (95%CI=32·2-47·0%) in multiple-episode schizophrenia (n=4685) (Q=18·27, p<0·0001). The pooled estimate of early-onset/primary (without any treatment response) TRS was 23.6% (3 studies; n=694; 95%CI=20·5-26·8%; I2=36·5%, p=0·207) vs. 9.3% (2 studies; n=450; 95%CI=6·8-12·2%; I2=0, p=0·6125) for later-onset/secondary (≥6 months after initial treatment response) TRS. In subgroup analyses, higher TRS frequency was associated with longer illness duration and recruitment from long-term hospitals or clinics specialized in clozapine. In meta-regression analysis, older age and poor functioning were associated with greater TRS. In sensitivity analyses of low-risk of bias studies, the TRS prevalence was 27.8%. This study is registered with PROSPERO, CRD42018092033. Interpretation: Between 22% of first-episode and 40% of multi-episode patients with schizophrenia have TRS, with heterogeneity explained by different study designs, settings and populations. At least two distinct trajectories of TRS, i.e., early-onset/primary and later-onset/secondary, with relevance for clinical practice and neurobiological studies, but only. few studies specifically addressed early-onset/primary vs late-onset/secondary TRS. Additional epidemiological studies of TRS are needed to disentangle different trajectories and inform health policies and clinical decision-making. Funding Information: No funding source. Declaration of Interests: Dr. Gadelha has been a consultant and/or advisor to or has received honoraria from: Aché, Daiichi-Sankyo, Torrent, Bayer, Cristalia, and Janssen. Dr. Correll has been a consultant and/or advisor to or has received honoraria from: AbbVie, Acadia, Alkermes, Allergan, Angelini, Aristo, Boehringer-Ingelheim, Cardio Diagnostics, Cerevel, CNX Therapeutics, Compass Pathways, Darnitsa, Gedeon Richter, Hikma, Holmusk, IntraCellular Therapies, Janssen/J&J, Karuna, LB Pharma, Lundbeck, MedAvante-ProPhase, MedInCell, Merck, Mindpax, Mitsubishi Tanabe Pharma, Mylan, Neurocrine, Newron, Noven, Otsuka, Pharmabrain, PPD Biotech, Recordati, Relmada, Reviva, Rovi, Seqirus, SK Life Science, Sunovion, Sun Pharma, Supernus, Takeda, Teva, and Viatris. He provided expert testimony for Janssen and Otsuka. He served on a Data Safety Monitoring Board for Lundbeck, Relmada, Reviva, Rovi, Supernus, and Teva. He has received grant support from Janssen and Takeda. He received royalties from UpToDate and is also a stock option holder of Cardio Diagnostics, Mindpax, LB Pharma and Quantic. Dr. Bressan has received research grants from Janssen Cilag, Novartis, Roche and the following governmental funding research agencies: CAPES, CNPq and FAPESP (2016/022465 and 22 2011/50740-5); he has been a forum consultant for Janssen, Novartis and Roche and has participated in speaker bureaus for Ache, Janssen, Lundbeck, and Novartis. Dr. André Russowski Brunoni reports grants from Sao Paulo Research State Foundation (2018/10861-7, 2019/06009-6), Brazilian National Council of Scientific Development productivity support (PQ-1B), and University of Sao Paulo Medical School productivity support (PIPA-A), and Newton Advanced Fellowship (NAFR 2012\1010), and in-kind support from Flow Neuroscience and MagVenture, unrelated to the present work. All other authors do not have any conflict of interest to report with regards to the topic of this manuscript.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,028
score de la tête « metaresearch » (Gemma)0,044
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens large)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Méta-analyse · Signal consensuel: Méta-analyse
GenreSignal candidat: Empirique · Signal consensuel: aucune
Score de désaccord entre enseignants0,979
Score d'incertitude au seuil0,146

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0280,044
Méta-épidémiologie (sens strict)0,0040,003
Méta-épidémiologie (sens large)0,0210,076
Bibliométrie0,0090,008
Études des sciences et des technologies0,0010,001
Communication savante0,0040,002
Science ouverte0,0030,002
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,037
Tête enseignante GPT0,325
Écart entre enseignants0,288 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Devis d'étudeMéta-analyse
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

Citations55
Publié2023
Routes d'admission1
Résumé présentoui

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