Off-label indications for antidepressants in primary care: descriptive study of prescriptions from an indication based electronic prescribing system
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Notice bibliographique
Résumé
<b>Objective</b> To examine off-label indications for antidepressants in primary care and determine the level of scientific support for off-label prescribing. <b>Design</b> Descriptive study of antidepressant prescriptions written by primary care physicians using an indication based electronic prescribing system. <b>Setting</b> Primary care practices in and around two major urban centres in Quebec, Canada. <b>Participants</b> Patients aged 18 years or older who visited a study physician between 1 January 2003 and 30 September 2015 and were prescribed an antidepressant through the electronic prescribing system. <b>Main outcome measures</b> Prevalence of off-label indications for antidepressant prescriptions by class and by individual drug. Among off-label antidepressant prescriptions, the proportion of prescriptions in each of the following categories was measured: strong evidence supporting use of the prescribed drug for the respective indication; no strong evidence for the prescribed drug but strong evidence supporting use of another drug in the same class for the indication; or no strong evidence supporting use of the prescribed drug and all other drugs in the same class for the indication. <b>Results</b> 106 850 antidepressant prescriptions were written by 174 physicians for 20 920 adults. By class, tricyclic antidepressants had the highest prevalence of off-label indications (81.4%, 95% confidence interval, 77.3% to 85.5%), largely due to a high off-label prescribing rate for amitriptyline (93%, 89.6% to 95.7%). Trazodone use for insomnia was the most common off-label use for antidepressants, accounting for 26.2% (21.9% to 30.4%) of all off-label prescriptions. For only 15.9% (13.0% to 19.3%) of all off-label prescriptions, the prescribed drug had strong scientific evidence for the respective indication. For 39.6% (35.7% to 43.2%) of off-label prescriptions, the prescribed drug did not have strong evidence but another antidepressant in the same class had strong evidence for the respective indication. For the remaining 44.6% (40.2% to 49.0%) of off-label prescriptions, neither the prescribed drug nor any other drugs in the class had strong evidence for the indication. <b>Conclusions</b> When primary care physicians prescribed antidepressants for off-label indications, these indications were usually not supported by strong scientific evidence, yet often another antidepressant in the same class existed that had strong evidence for the respective indication. There is an important need to generate and provide physicians with evidence on off-label antidepressant use to optimise prescribing decisions.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| 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) | 0,000 | 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 découle