Design and Implementation of a Dashboard for Drug Interactions Mediated by Cytochromes Using a Health Care Data Warehouse in a University Hospital Center: Development Study
Bibliographic record
Abstract
Background: The enzymatic system of cytochrome P450 (CYP450) is a group of enzymes involved in the metabolism of drugs present in the liver. Literature records instances of underdosing of drugs due to the concurrent administration of another drug that strongly induces the same cytochrome for which the first drug is a substrate and overdosing due to strong inhibition. IT solutions have been proposed to raise awareness among prescribers to mitigate these interactions. Objective: This study aimed to develop a drug interaction dashboard for Cytochrome-mediated drug interactions (DIDC) using a health care data warehouse to display results that are easily readable and interpretable by clinical experts. Methods: The initial step involved defining requirements with expert pharmacologists. An existing model of interactions involving the (CYP450) was used. A program for the automatic detection of cytochrome-mediated drug interactions (DI) was developed. Finally, the development and visualization of the DIDC were carried out by an IT engineer. An evaluation of the tool was carried out. Results: The development of the DIDC was successfully completed. It automatically compiled cytochrome-mediated DIs in a comprehensive table and provided a dedicated dashboard for each potential DI. The most frequent interaction involved paracetamol and carbamazepine with CYP450 3A4 (n=50 patients). The prescription of tacrolimus with CYP3A5 genotyping pertained to 675 patients. Two experts qualitatively evaluated the tool, resulting in overall satisfaction scores of 6 and 5 out of 7, respectively. Conclusions: At our hospital, measurements of molecules that could have altered concentrations due to cytochrome-mediated DIs are not systematic. These DIs can lead to serious clinical consequences. The purpose of this DIDC is to provide an overall view and raise awareness among prescribers about the importance of measuring concentrations of specific drugs and metabolites. Ultimately, the tool could lead to an individualized approach and become a prescription support tool if integrated into prescription assistance software.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".