561. PHARMACOGENETIC-SUPPORTED PRESCRIBING FOR KIDS WITH MENTAL HEALTH CONDTIONS
Notice bibliographique
Résumé
Abstract Background Psychiatric medications are often prescribed to treat moderate-to-severe mental health conditions in children, adolescents, and emerging adults. However, selecting an effective medication is often experienced as a trial-and-error process with vast interindividual variation in efficacy and tolerability. Pharmacogenetic (PGx) testing is one strategy that can assist health care professionals guide prescribing decisions. Limited is known about the use of this strategy in young people with mental health conditions. Aims & Objectives To address this gap, the Pharmacogenetic-Supported Prescribing in Kids (PGx-SParK) study was designed to implement and evaluate real-world clinical PGx testing among children, adolescents, and emerging adults receiving mental health care in Western Canada. Method A mirror image trial design is being used to evaluate the impact PGx testing implementation has on symptom severity, side effects, and healthcare utilization. Data is collected prospectively for six-months following PGx testing as well as retrospectively for the six-months preceding the testing, using a combination of self-report, clinician-report, and administrative data sources. Youth ages 6-24 who may be starting or changing a medication for mental health can be referred by a physician responsible for their prescribing decisions to reflect real-world application of clinical care. Saliva samples are collected from participants and genotyped for 11 genes with PGx-based prescribing guidelines. Results are then translated into a clinical report using an evidence-based software called Sequence2Script. The results provide recommendations for selection and dosing of medications based on the participant’s PGx profile. The report is delivered to the referring physician and participant (or guardian) to facilitate shared decision-making during the prescribing process. Results To date we have enrolled 1652 participants, referred by over 300 psychiatrists, family doctors and pediatricians across Western Canada (Alberta, Saskatchewan, British Columbia, and Manitoba). PGx testing shows 82% of participants had an actionable genotype. Sertraline (18%), fluvoxamine (9%), risperidone (8%), aripiprazole (7%), and atomoxetine (6%) were the most frequently prescribed psychotropic medications with a PGx-based dosing guideline. Furthermore, 10.2% of participants were currently taking a psychiatric medication that was incongruent with their PGx profile. Our findings to date have: (1) demonstrated that delivery of PGx testing improves symptoms, reduces adverse drug effects, and decreased healthcare utilization; (2) identified novel associations between CYP2D6 genetic variation and the efficacy of fluoxetine and amphetamine treatment; and (3) estimated, for the first time, a high prevalence (46%) of phenoconversion in youth receiving pharmacotherapy for mental health conditions. Discussion & Conclusions Our findings suggest prescribing of psychotropic medications with available PGx-based guidelines is common among youth and approximately one in every 10 youth are taking psychotropic medications that are incongruent with their PGx profile. These results are facilitating the integration of Canada’s first evidence-based genetic testing service to improve outcomes for those seeking support for child and adolescent mental health, and promoting safer and more cost-effective psychiatric treatments tailored to youth with mental health conditions.
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 | 0,003 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 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 ».