The role of pharmacogenomics and opioid prescribing for infants with surgical congenital heart disease
Bibliographic record
Abstract
OBJECTIVES: Pharmacogenomic (PGx) variants associated with opioid metabolism and reward pathways may influence pain response and risk of opioid dependence. Infants undergoing surgery routinely receive opioids, and prolonged exposure impacts health outcomes. This study evaluated relationships between PGx variants and opioid utilization in infants undergoing surgery for congenital heart disease (CHD). METHODS: This retrospective cohort study included infants <1 year who underwent CHD surgery and had exome sequencing at a quaternary children's hospital from 2009 to 2020. PGx variants associated with opioid-response (COMT, DRD2/ANKK1, ABCB1, OPRM1, and CYP2D6) were evaluated. Median cumulative morphine milliequivalents (MMEs) administered were calculated over each hospitalization, and median MMEs corresponding with each variant were analyzed using Kruskal-Wallis tests. RESULTS: Overall, 48 infants were identified (54.2% male, 47.9% Hispanic/Latino, and 6.3% preterm). Most (n = 34, 70.8%) underwent open surgery, and 14 (29.2%) underwent minimally invasive procedures. Forty infants (83%) were homozygous for at least one opioid-related PGx variant. Infants who underwent open surgery and were homozygous for OPRM1: rs1799971, COMT: rs4633, rs4680, and ABCB1: rs1045642 demonstrated increased cumulative MMEs compared to wild type. Infants who underwent minimally invasive surgery and were homozygous for ABCB1: rs1045642 also had increased cumulative MMEs. No relationship between CYP2D6 metabolizer phenotypes and MMEs was observed. CONCLUSION: Most infants undergoing CHD surgery who had exome sequencing were homozygous for an opioid-related PGx variant. Additionally, infants who were homozygous received increased MMEs during hospitalization. Routine reporting of PGx variants could inform future innovation in precision medicine and opioid stewardship efforts.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".