Pharmacogenetic Testing in a 70-Year-Old Woman with Polypharmacy and Multiple Comorbidities: A Case Report
Bibliographic record
Abstract
BACKGROUND Comorbidities and polypharmacy are difficult to manage, as polypharmacy hinders identification and prevention of medication-related problems. Risk for adverse drug events (ADEs) can be minimized through pharmacogenomic (PGx) testing and related therapeutic adjustments. CASE REPORT A 70-year-old woman with comorbidities and medications enrolled in the Program of All-inclusive Care for the Elderly presented with left lower extremity (LLE) pain, generalized weakness, and major depressive disorder. The provider requested a medication safety review, where the clinical pharmacist-recommended PGx testing given the LLE pain and weakness while taking a statin and inconsistent INR readings taking warfarin. The pharmacist recommended switching atorvastatin to pravastatin to minimize the risk for statin-associated ADEs due to CYP3A4 inhibition and switching fluoxetine to citalopram due to uncontrolled depression/anxiety and to mitigate drug-drug interactions with carvedilol to reduce the risk of orthostatic hypotension. Recommendations were accepted and upon follow-up the patient reported minor LLE pain and improved wellbeing on citalopram. Following PGx testing, the patient had decreased function at SLCO1B1 and was an intermediate metabolizer for CYP2C9 and CYP2D6. This case demonstrates how preemptive PGx testing would have identified drug-gene interactions (DGIs) at the time of prescribing and reduced the risk of statin-associated muscular symptoms, highlighting the utility of panel-based PGx testing in older adults at high risk for ADEs and/or therapy failure. CONCLUSIONS Decreased function at SLCO1B1 increases exposure to statins, leading to statin-induced myalgias, as displayed in this case. PGx testing can help identify DGIs, choose optimal therapies in medically complex older adults, and minimize ADE risk.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".