Adjunctive Medication Strategies for Treatment- Resistant Depression
Notice bibliographique
Résumé
Abbreviations AD antidepressant CANMAT Canadian Network for Mood and Anxiety Treatments MDD major depressive disorder TRD treatment-resistant depression Depression is a serious illness. Numerous studies show that major depressive disorder (MDD) is associated with significant personal distress and burden of disability.1 The good news is that there are many effective treatments available to treat MDD. The bad news is that not everyone experiences a full response to treatment. For example, the Sequenced Treatment Alternatives to Relieve Depression (commonly known by its acronym, STAR*D) effectiveness study found that, in the real world, remission outcomes are modest, with only 33% of patients with MDD achieving full remission of symptoms after the first antidepressant (AD) and only 67% after 1 year of treatment involving up to 4 treatment steps.2 Thus a significant percentage of patients will have a treatment-resistant depression (TRD), regardless of how treatment-resistant is defined.3 It is also clear that TRD is associated with poor outcomes and a disproportionate amount of the burden associated with MDD.4,5 Although TRD is often used in the literature as if it were a distinct entity, there is considerable variability in how TRD is defined and there is no consensus definition.6 For example, a commonly applied definition for TRD is failure of 2 or more ADs, preferably from different classes. However, some studies define resistant depression as failure of one AD, while others attempt to stage resistance by incorporating responses to other treatments (including older medications, such as tricyclic ADs or monoamine oxidase inhibitors, and somatic treatments, such as electroconvulsive therapy).7 Moreover, the definition of failure also varies considerably from study to study. Some TRD studies include patients with treatment failure by history, while others define failure prospectively. These diagnostic issues contribute to significant heterogeneity in TRD samples, which makes it difficult to compare treatment studies of TRD. In the context of modest success rates with AD monotherapy for MDD, how can we optimize outcomes for the large proportion of patients with TRD? We practice in an evidence-based medicine climate, so it is important to first consider evidence-based treatment approaches. However, we need to remember to differentiate between evidence of lack of efficacy and lack of evidence of efficacy. The latter is much more representative of the evidence landscape in psychiatry than the former. While there is reasonable evidence to support choices for initial pharmacotherapy of MDD, there is still only limited evidence for many important clinical questions, including how to manage poor or incomplete response to an initial AD. To further complicate the issue, the terminology for treatment strategies for TRD is changing. Augmentation and combination have been used in the literature to describe distinct strategies when adding another medication to an AD. Augmentation referred to adding a medication that was not considered an AD (for example, lithium or triiodothyronine), while combination referred to the practice of adding a second AD (for example, desipramine or bupropion). However, the definition of an AD is blurring and some medications that were considered augmentation agents may be effective ADs on their own. For example, older studies indicated that lithium may have acute AD effects for unipolar depression,8 and more recent studies show that some atypical antipsychotics (for example, quetiapine extended release9) are effective as monotherapy for nonpsychotic MDD. For this reason, some authors have suggested that the terms augmentation and combination be replaced by add-on or adjunctive, which do not infer the type of medication added.10 The 2009 revision of the Canadian Network for Mood and Anxiety Treatments (CANMAT) depression guidelines summarizes the evidence for pharmacologic strategies for TRD. …
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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 | 0,000 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,037 | 0,004 |
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 ».