T207. METABOLIC ADVERSE EFFECTS OF OFF-LABEL USE OF SECOND-GENERATION ANTIPSYCHOTICS IN THE ADULT POPULATION: A SYSTEMATIC REVIEW AND META-ANALYSIS
Notice bibliographique
Résumé
Abstract Background Second generation antipsychotics (SGAs) are approved by the US Food and Drug Administration (FDA) for the treatment of schizophrenia, bipolar disorder and treatment refractory depression. Since the late 1990’s, prescription of antipsychotics has more than doubled and off-label use (i.e. use for indications other than those approved by the FDA) has disproportionately contributed to this increase. These drugs, when used “on-label’, have very significant metabolic adverse effects. Currently, there is no existing literature systematically investigating the metabolic adverse effects of ‘off-label’ use to guide clinical decision making practices. Methods A systematic review was conducted and MEDLINE, EMBASE, CENTRAL, PsycINFO, and CINAHL were searched for all randomized controlled trials (RCTs) that reported off-label use of SGAs in the adult population. A grey literature search was also completed on ClinicalTrials.gov and the ICTRP Search Portal. The Cochrane Risk of Bias tool was used for the assessment of potential biases. Meta-analyses of the reported metabolic outcomes were completed based on psychiatric diagnosis and subgroup analyses were conducted based on the individual prescribed SGA. Metabolic adverse events analyzed include weight, fasting plasma glucose, HbA1C, total cholesterol, HDL, LDL, and triglycerides. Sensitivity analyses were performed where studies of low quality were excluded. Results The search strategy identified 9309 references, for which 47 randomized control trials (RCTS) were included in the meta-analysis. These studies were published from 1984 to 2019 and came from North America (n=27), Europe (n=9), Asia (n=4), and other (n=7). Participants with generalized anxiety disorder (n=9 studies) treated with quetiapine gained on average 0.53kg (CI 0.22 to 0.84, p= 0.008), had increases in triglyceride levels by 0.17mmol/L (CI 0.052 to 0.29, p=0.005), and decreases in HDL levels by 0.038mmol/L (CI -0.065 to -0.011 p=0.005). Similar results were found in patients with borderline personality disorder (BPD), where olanzapine was associated with a mean weight increase of 2.67kg (CI 2.23 to 3.11, p<0.00001), and increases in triglyceride levels by 0.0029mmol/L (CI 0.0015to 0.0045, p = 0.002). In a single study in patients with trichotillomania, olanzapine was associated with a weight gain of 4.90kg (CI 3.04 to 6.76). Participants with OCD (n=8 studies) receiving quetiapine gained on average 3.40kg (CI 3.17 to 3.63). In studies examining PTSD treatment (n=7 studies), quetiapine was associated with a mean 23kg weight increase (CI 4.85 to 41.15), and olanzapine with a 6.63kg mean weight increase (CI 4.87 to 8.39, p<0.0001). The degree of weight gain associated with quetiapine could partly be attributable to the report of endpoint mean weights only between groups (rather than change data). Participants with cocaine-related substance use disorder (n= 4 studies) on risperidone gained on average 4.70kg (CI 1.78 to 7.62). Where applicable, sensitivity analyses removing low quality studies, did not affect the significance of findings. Discussion Based on the limited data available, there is a significant association between off-label antipsychotic use and weight gain. This was most commonly reported in association with quetiapine and olanzapine, which may be due to the fact they were the most widely studied agents (n =12, and 16 studies, respectively). Although other metabolic adverse events have been documented, there is not yet enough data available to draw conclusions. Our findings raise concern given the widespread use of SGAs in off-label indications, highlighting the need for raising clinical awareness, ensuring regular metabolic monitoring, and further study of these adverse effects.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,009 | 0,001 |
| Bibliométrie | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».