Augmentation vs. switching medications in older patients with treatment-resistant depression: clinical moderators that matter
Notice bibliographique
Résumé
Introduction Older adults with treatment-resistant depression (TRD) can be treated with augmentation or switched to a different drug. Objectives We aimed to identify factors that moderate the effectiveness of these strategies on treatment outcomes to guide the selection of the optimal strategy for each patient. Methods We analyzed data from 742 older adults with TRD in the Outcomes of Treatment-Resistant Depression in Older Adults (OPTIMUM) clinical trial. All participants were randomized to one of two treatment strategies, which were augmentation with aripiprazole, bupropion, or lithium; or switching to bupropion or nortriptyline. Treatment outcomes were change in MADRS scores and remission after 10 weeks. Age, burden of comorbid physical illness, number of adequate previous antidepressant trials, presence of executive cognitive impairment, and clinically relevant comorbid anxiety were examined as potential moderators of the effect of the two treatment strategies (augmentation vs. switching) on treatment outcomes. Results Overall, augmentation produced more improvement in MADRS scores and produced a higher rate of remission than switching. For change in MADRS scores after 10 weeks of treatment, the number of adequate previous antidepressant trials was the only significant moderator of the superiority of augmentation over switching (b = -1.6, t = -2.1, p = 0.033, 95%CI [-3.0,-0.1]). There were no significant moderators for remission. Conclusions Older patients with TRD with less than three previous antidepressant trials benefit more from augmentation than from switching. Future studies validating this finding with different drugs in more diverse samples can facilitate their application in real world settings. Disclosure of Interest H. Kim Grant / Research support from: Dr. Kim reports grant support from the PSI foundation (R23-21). She is supported by the Canadian Institutes of Health Research (CIHR) and the Temerty Faculty of Medicine (Chisholm Memorial Fellowship)., J. Karp: None Declared, H. Lavretsky Grant / Research support from: Dr. Lavretsky received support from grants (K24 AT009198, R01 AT008383, and R01 MH114981) from the NIH., D. Blumberger Grant / Research support from: Dr. Blumberger reports grants from Canadian Institutes of Health Research (CIHR) and the Temerty family through the Centre for Addiction and Mental Health (CAMH) Foundation during the conduct of the study; nonfinancial support from Magventure (in-kind equipment support for investigator-initiated research); grants from Brainsway (principal investigator of an investigator-initiated study and site principal investigator for sponsored clinical trials), National Institutes of Health (NIH), Brain Canada Foundation, Campbell Family Research Institute, and Patient-Centered Outcomes Research Institute outside the submitted work; received medication supplies for an investigator-initiated trial from Indivior; and has participated in advisory boards for Janssen and Welcony., P. Brown Grant / Research support from: Dr. Brown received additional support from the National Institute of Mental Health OPTIMUM NEURO grant (5R01MH114980)., A. Flint Grant / Research support from: Dr. Flint has received grant support from the US National Institutes of Health, the Patient-Centered Outcomes Research Institute, the Canadian Institutes of Health Research, Brain Canada, the Ontario Brain Institute, and Alzheimer’s Association., E. Lenard: None Declared, P. Miller: None Declared, C. Reynolds Shareolder of: Dr. Reynolds receives payment from the American Association of Geriatric Psychiatry as Editor-in-Chief of the American Journal of Geriatric Psychiatry and royalty income for intellectual property as co-inventor of the Pittsburgh Sleep Quality Index., S. Roose: None Declared, E. Lenze Grant / Research support from: Dr. Lenze received additional support from the Taylor Family Institute for Innovative Psychiatric Research at Washington University School of Medicine, as well as the Washington University Institute of Clinical and Translational Sciences grant (UL1TR002345) from the National Center for Advancing Translational Sciences of the National Institutes of Health (NIH)., B. Mulsant Grant / Research support from: Dr. Mulsant received additional support from the Labatt Family Chair in Biology of Depression in Late-Life Adults at the University of Toronto. He holds and receives support from the Labatt Family Chair in Biology of Depression in Late-Life Adults at the University of Toronto. He currently receives or has received during the past three years research support from Brain Canada, the CAMH Foundation, the Canadian Institutes of Health Research, and the US National Institutes of Health (NIH); Capital Solution Design LLC (software used in a study funded by CAMH Foundation), and HAPPYneuron (software used in a study funded by Brain Canada).
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 | 0,019 | 0,030 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,003 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».