561. PHARMACOGENETIC-SUPPORTED PRESCRIBING FOR KIDS WITH MENTAL HEALTH CONDTIONS
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".