Gaps in the detection of drug-drug interactions between antipsychotic and cardiometabolic medications: a multisource analysis
Notice bibliographique
Résumé
BACKGROUND: Individuals with severe mental illness (SMI) are frequently prescribed both antipsychotic medications and cardiometabolic medications, placing them at increased risk of drug-drug interactions (DDIs). However, evidence guiding the identification and management of these interactions remains fragmented. To address this research gap, in this study, we systematically summarize potential DDIs between antipsychotic and cardiometabolic medications and evaluate the performance of commonly used online DDI checkers in identifying these interactions. METHODS: A systematic review was conducted using PubMed, Embase, PsycINFO, and Web of Science to identify studies reporting DDIs between antipsychotic and cardiometabolic medications up to March 20, 2024. Disproportionality analysis was performed using data from the Canada Vigilance Adverse Reaction Online Database (1965-2024) and the FDA Adverse Event Reporting System (FAERS, 2004-2024) to identify DDI signals. Four online DDI checkers-Drugs.com, Medscape, ddinter, and ANSM Thesaurus-were used to evaluate their ability to identify the observed interactions. RESULTS: Across all sources, 1776 unique potential DDIs were identified. Clozapine was the most frequently implicated antipsychotic medication in a systematic review, often associated with musculoskeletal and connective tissue disorders. DDI signals associated with aripiprazole and quetiapine were also frequently observed. Except for nervous system disorders and cardiometabolic disorders, the adverse outcomes of DDIs involving aripiprazole or quetiapine were most commonly associated with musculoskeletal and connective tissue disorders and gastrointestinal disorders. Quetiapine interactions, especially with lipid-lowering agents such as simvastatin, were also commonly linked to musculoskeletal and connective tissue disorders. Notably, 45.4% of identified DDIs were not flagged by any of the four DDI checkers. Drugs.com detected the most interactions. Combinations of clozapine and metformin, ziprasidone and metformin, and risperidone and clonidine were consistently identified by at least three of the checkers. CONCLUSIONS: This systematic review and disproportionality analysis identified potential DDIs between antipsychotic medications and cardiometabolic medications, many of which were not captured by commonly used DDI checkers. These findings underscore the need for clinicians to consult multiple sources and apply clinical judgment when prescribing these medications. Improved integration of pharmacovigilance data into DDI checkers may enhance the identification and prevention of harmful interactions.
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 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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,002 | 0,004 |
| 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 | 0,002 |
| 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 ».