Pharmacogenomic Testing and Depressive Symptom Remission: A Systematic Review and <scp>Meta‐Analysis</scp> of Prospective, Controlled Clinical Trials
Bibliographic record
Abstract
Pharmacogenomic (PGx) testing has emerged as a compelling strategy that clinicians can use to inform antidepressant medication selection and dosing, but the clinical efficacy of this strategy has been questioned. We systematically reviewed and meta-analyzed clinical trials for an association between the use of PGx-guided antidepressant therapy and depressive symptom remission in patients with major depressive disorder (MDD). We included prospective, controlled clinical trials published in English up to July 12, 2022. Data extraction and synthesis adhered to the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Each trial was assessed for risk of bias and a random-effects model was used to estimate pooled risk ratios. Thirteen trials comprising 4,767 patients were analyzed, including 10 randomized controlled trials, and three open label trials. Across all included trials, those that received PGx-guided antidepressant therapy (n = 2,395) were 1.41 (95% confidence interval (CI) = 1.15-1.74, P = 0.001) more likely to achieve remission compared with those that received unguided antidepressant therapy (n = 2,372). Pooled risk ratios for randomized controlled trials and open label trials were 1.46 (95% CI: 1.13-1.88) and 1.26 (95% CI = 0.84-1.88), respectively. These results suggest that PGx-guided antidepressant therapy is associated with a modest but significant increase in depressive symptom remission in adults with MDD. Efforts to address the heterogeneity in PGx test composition (i.e., genes and alleles tested) and accompanying prescribing recommendations across trials will likely reduce the uncertainty about the efficacy of PGx-guided antidepressant therapy in the literature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.044 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.035 | 0.009 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; both teacher heads agree on what is shown here.
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".