Longitudinal exposure of English primary care patients to pharmacogenomic drugs: An analysis to inform design of pre‐emptive pharmacogenomic testing
Bibliographic record
Abstract
AIMS: To investigate the longitudinal exposure of English primary care patients to pharmacogenomic drugs to inform design of pre-emptive testing. METHODS: Sixty-three drugs were identified with dosing guidelines based on variants of 19 pharmacogenes in the Pharmacogenomics Knowledgebase on 01 September 2018. Prescribing of these pharmacogenomic drugs between 1993 and 2017 was summarised for a sample of 648 141 English patients aged 50-99 years on 01 January 2013, registered with Clinical Practice Research Datalink practices during 2011-12. Exposure of patients to pharmacogenomic drugs retrospectively (2, 10, 20 y) and prospectively (5 y) was described. RESULTS: During 2011-12, 58% of patients were prescribed at least 1 pharmacogenomic drug, increasing to 80% over the previous 20 years. Multiple exposure was common, with 47% patients prescribed ≥2 pharmacogenomic drugs and 7% prescribed ≥5 pharmacogenomic drugs over the next 5 years. The likelihood of exposure to pharmacogenomic drugs increased with age, with 89% patients ≥70 years prescribed at least 1 pharmacogenomic drug over the previous 20 years. Even among those aged 50-59 years, 71% were prescribed at least 1 pharmacogenomic drug over the previous 20 years. The pharmacogenomic drugs prescribed to the most patients were for pain relief, gastroprotection, psychiatric and cardiovascular conditions. Three pharmacogenes (CYP2D6, CYP2C19 and SLCO1B1) accounted for >95% pharmacogenomic drugs prescribed. CONCLUSIONS: In primary care patients, exposure to pharmacogenomic drugs is extremely common, multiplicitous and has commenced by relatively early adulthood. A small number of pharmacogenes account for the majority of drugs prescribed. These findings could inform design of pre-emptive pharmacogenomic testing for implementation in primary care.
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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".