Mechanisms of action of antidepressants and their combination for major depressive disorder treatment: a theoretical and clinical approach
Notice bibliographique
Résumé
Background: Annually, an estimated 8.2% of Canadians aged 18 or older are affected by major depressive disorder (MDD). Nearly half of those suffering from MDD will fail to achieve remission while also having an inadequate response to an initial and continuous 6-week single antidepressant treatment. This failure to remit or respond to monotherapy, referred to as treatment-resistant depression (TRD), affects more than 30% of those suffering from MDD. The addition of a second antidepressant to improve upon the effects or alleviate the side-effects of the initial antidepressant has repeatedly shown encouraging therapeutic benefits. Unfortunately, the use of combination therapy in clinical settings has remained relatively low. Objective: With knowledge and understanding of the mechanisms of action of each of the seven different classes of antidepressants attained from preclinical studies (in vitro and in vivo electrophysiology) conducted at the Neurobiological Psychiatry Unit (NPU) at McGill University, the therapeutic efficacy of combination therapy can be maximized and adverse interactions and events minimized. The main goal of this thesis was to review the extensive literature concerning antidepressant studies conducted at the NPU as well as the clinical literature from PubMed and OvidSP in order to discern the most efficacious antidepressants and antidepressant combination treatments. The data collected from the literature was critically compared with the clinical database of the Mood Disorders Clinic (MDC) at the McGill University Health Centre (MUHC), in order to establish the clinical pertinence of using antidepressant combinations. Methods: A literature review was conducted to discern the most frequently prescribed antidepressants and efficacious antidepressant combination treatments. Subsequently, we analyzed the database of the MUHC; 133 outpatients with a current DSM-IV diagnosis of MDD aged 18 years or older were included in this study. Sociodemographic and clinical information of each patient was obtained during his or her initial diagnostic evaluation by a multidisciplinary team and chart review. Patients were also asked to complete a self-reported BDI-II questionnaire in order to assess the severity of depressive symptoms. Statistical analyses between prescribed antidepressant combinations and symptom severity were performed to determine effectiveness. A critical comparison of the findings from the literature with the clinical information obtained from patients referred to the MDC was conducted. Results: Significantly more women than men were diagnosed with MDD. Within the six months of their initial diagnostic evaluation, 87.2% of the patients had been prescribed an antidepressant. The most frequently prescribed antidepressant was a selective serotonin reuptake inhibitor (SSRI), followed by a serotonin-norepinephrine reuptake inhibitor (SNRI) and bupropion. Consistent with the literature, the most frequent antidepressant combination treatments were i) SSRI + bupropion, ii) SNRI + bupropion, and iii) SNRI + mirtazapine. No significant difference was found between antidepressant combination treatments and mean total BDI-II scores. Conclusions: Clinical findings were generally consistent with the literature. The literature supported the use of antidepressant combinations for effective and time-efficient treatment of MDD, particularly at the beginning of treatment, yet psychiatrists still appeared hesitant on using this approach. The combinations of bupropion with an SSRI or SNRI were found to be the most efficacious combinations, receiving frequent support in the literature and in this study. Limitations: Low completion rate of the BDI-II resulted in the powers of performed tests to be lower than the desired powers, thus reducing the likelihood of detecting a difference when one may have actually existed. A larger cohort of patients could allow for clinically meaningful differences to be observed.
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 | 0,003 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,009 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».