Combinatorial Pharmacogenomic Testing Improves Outcomes for Older Adults With Depression
Bibliographic record
Abstract
Objective: Evaluate the clinical utility of combinatorial pharmacogenomic testing for informing medication selection among older adults who have experienced antidepressant medication failure for major depressive disorder (MDD). Design: Post hoc analysis of data from a blinded, randomized controlled trial comparing two active treatment arms. Setting: Psychiatry specialty and primary care clinics across 60 U.S. community and academic sites. Participants: Adults age 65 years or older at baseline (n = 206), diagnosed with MDD and inadequate response to at least one medication on the combinatorial pharmacogenomic test report during the current depressive episode. Intervention: Combinatorial pharmacogenomic testing to inform medication selection (guided-care), compared with treatment as usual (TAU). Outcomes: Mean percent symptom improvement, response rate, and remission rate at week 8, measured using the 17-item Hamilton Depression Rating Scale; medication switching; and comorbidity moderator analysis. Results: At week 8, symptom improvement was not significantly different for guided-care than for TAU (A = 8.1%, t=1.64, df= 187; p = 0.102); however, guided-care showed significantly improved response (A = 13.6%, t = 2.16, df= 187; p = 0.032) and remission (A = 12.7%, t = 2.49, df= 189; p = 0.014) relative to TAU. By week 8, more than twice as many patients in guided-care than in TAU were on medications predicted to have no gene-drug interactions (x = 19.3, df=2;p <0.001). Outcomes in the guided-care arm showed consistent improvement through the end of the open-design 24-week trial, indicating durability of the effect. Differences in outcomes between arms were not significantly impacted by comorbidities. Conclusions: Combinatorial pharmacogenomic test-informed medication selection improved outcomes over TAU among older adults with depression. (Reprinted with permission from Am J Geriatr Psychiatry 2020; 28:933–945)
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; a candidate call from one teacher head, 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".