Is Monotherapy as Good as Polypharmacy in Long—Term Treatment of Bipolar Disorder?
Notice bibliographique
Résumé
Can J Psychiatry. 2009;54(1 1):719-725. Can Trial and Error Become the Standard of Practice? During the last several decades, the number of available treatments for bipolar disorder (BD) has increased substantially. Nevertheless, the outcome of BD remains poor in many patients,1 fuelling debates about the merits of specific medications and their combinations. Treatment of BD has several facets - treatment of acute episodes of depression or mania, long-term treatment, and control of rapid cycling, cognitive dysfunction, or residual symptoms. Not all can be addressed in a brief paper. In the following, I will focus on long-term treatment, mainly as this is the key component in the disease management. Treatment of acute episodes is, perhaps, less controversial, particularly when it comes to manic episodes - using a combination treatment may shorten time to remission and, importantly, such treatments are typically used for a limited period of time. But even then some add-on trials in mania have produced equivocal data. The situation concerning bipolar depression is far more complicated, with many studies, including newer data, not supporting the use of combinations with antidepressants.2 Therefore, even when treating patients for acute episodes, physicians must carefully judge the risk-benefit balance (with risk of switch into mania, rapid cycling, or adverse effects). Are There Reasons to Use Combinations for Long-Term Treatment? A large proportion of patients with BD are being treated off label, and many are treated with combinations of not only 2, but frequently 3 or more medications aimed at mood stabilization. The evidence to support such management is practically nonexistent, as shown below. Before reviewing the evidence, I would like to stress 2 other points. First, physicians need to be able to determine accurately that a patient is better off because of a specific treatment and whether an alternative treatment might provide the same or greater benefit. This is not easy in an illness that is highly unpredictable in its pattern of recurrences (and spontaneous remissions). Many patients are treated for limited periods of time, making evaluation of treatment response difficult. Second, with more research we may eventually know if, on average, a randomly selected combination presents an advantage over randomly selected monotherapy. However, this is not the same as knowing if a combination is better than individualized treatment. There is a growing trend to find predictors of treatment response, including clinical features, biological markers, as well as genetic variants. Evidence-based selection of an effective treatment may make this present debate ultimately less relevant than it currently seems. For some drugs, for instance lithium, the long-term response can be already predicted quite accurately using clinical data alone.3 The limiting factor in deciding whether polypharmacy presents an advantage over monotherapy is the lack of relevant studies. Available data are not helpful to answer such simple questions as to whether one should initiate long-term treatment with drug combinations, or whether an ineffective medication should be switched or combined (augmented). Existing studies of combination treatments are either inconclusive, methodologically problematic, or answer another question altogether. A common reason for using drug combinations is monotherapy failure. No existing medication works for all cases of BD. After concluding that a person is not responding, a decision needs to be made about the future direction of the treatment. This may, but does not have to, be a medication change. Treatment outcome can be influenced by therapeutic alliance, improving compliance and providing patient education, as well as addressing side effects. In some cases, reassessment may show that the patient was initially misdiagnosed and requires a different treatment altogether. Data on effectiveness of combinations are scarce. …
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 | 0,029 | 0,082 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,004 | 0,002 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,003 |
| Communication savante | 0,004 | 0,007 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,007 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 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 ».