Treatment of resistant depression: Diagnostic profile and therapeutic role of atypical antipsychotics and mood stabilizers
Notice bibliographique
Résumé
Background: Major Depressive Disorder (MDD) is a highly prevalent and disabling or fatal disease. According to the STAR*D project, about 50% of patients with unipolar depression suffer from treatment-resistant depression (TRD). In addition, animal studies have suggested potential antidepressant properties of Valproate (VPA) possibly due to its implication in epigenetic programming. Method: We evaluated socio-demographic, psychopathological profiles and treatment outcomes in 78 patients (46 females, 32 males) with TRD from the Register of the Mood Disorder Clinic at McGill University Health Center (MUHC) by chart reviewer analysis. Clinical response was investigated prior to treatment (T-0) and after 30-90 days (T-3) of stable therapy using Montgomery-Asberg Depression Rating Scale (MADRS), Hamilton Depression Rating Scale (HAM-D17), Quick Inventory of Depressive Symptomatology (QIDS-C16) and Clinical Global Impression-severity of illness (CGI-S). These patients underwent several antidepressant treatment strategies. Only the last trial, when the patient responded to treatment and remained stable for more than 6 weeks (mean 12.5±5.90 weeks, T-3), and the treating psychiatrist kept the treatment unchanged, was included in this study for statistical analysis.Results: In the first research study, patients responded to three pharmacological interventions: A)antidepressants combination(21); B)mood stabilizers and antidepressants (16); C)atypical antipsychotics and antidepressant (41). Compared to T-0, patients in all treatment groups showed significant decrease in depressive symptoms on all scales at T-3 (P<0.001). Importantly, at T-0, Group-C showed higher depressive symptoms on all scales compared to Group-A (HAM-D17, 25.7±1 vs. 21.3±1.5, mean±S.E.M, P=0.02). In addition, Group-C, compared to Group-A showed increased previous suicide attempts (29.3% vs. 14.3%) and number of failed treatments (4.2±2.9 vs. 2.7±2, mean±SD). Finally, change from T-0 to T-3 (Δ) on HAM-D17 was significantly superior in Group-C (Δ=10.6) compared to Group-A (Δ=7, P=0.05). In the second study, we were also able to identify 14 patients (7 males and 7 females; age 19-59) who received VPA (375-1000mg/d) in addition to their treatment and clinical response to VPA was investigated after 1 (T-1), 4 (T-4) and 7 (T-7) months of therapy using the MADRS and CGI-S. As for the VPA augmentation group, VPA significantly decreased MADRS score at T-1 (23.5±1.0, P<0.001), T-4 (18.6±1.3, P<0.001), and T-7 (13.6±1.6, P<0.001) (effect size: partial η2=0.86). MADRS at T-4 was also lower than at T-1 (P<0.001) and at T-7 lower than at T-4 (P=0.008). Importantly, MADRS score at T-7 was closer to the reported value of remission (MADRS<10), and none of the patients relapsed during the observational period. Compared to T-0 (5.1±0.3), VPA also decreased CGI-S at T-1 (4.0±0.1, P=0.03), T-4 (3.3±0.2, P<0.001), and T-7 (2.6±0.3, P<0.001) (partial η2=0.74).Conclusion: The results highlight the importance of antipsychotic and/or mood stabilizer augmentation as first-line treatment in patients with severe TRD. Moreover, in a subgroup of patients, VPA showed substantial clinical improvement and maintenance over a long period and thus deserves further exploration in large double-blinded trials. We have identified a sub-class of TRD patients presenting with specific psychopathological features (significantly higher HAMD-17 score at T-0, higher suicide attempts, higher number of failed treatments, and a significantly better response to atypical antipsychotics and/or mood stabilizers) that respond significantly better to atypical antipsychotics and/or mood stabilizers. Randomized-controlled trials evaluating the independent roles of augmentation with antipsychotics or mood stabilizers are warranted in order to assess the initial pharmacological options for patients with severe TRD and to better characterize this subgroup of patients from a psychopathological and therapeutic point of view.
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 | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».