Antimicrobial stewardship in solid organ transplant—Opportunities in the National Health Service
Bibliographic record
Abstract
BACKGROUND: Antimicrobial stewardship (AMS) is an intervention, which ensures the appropriateness of antimicrobial use to avoid in part the rising problem of antimicrobial resistance and negative effects of inappropriate antimicrobial use. In the solid organ transplant (SOT) population, which is prone to a particularly high risk of infection resulting from immunosuppression and anatomical issues with each type of SOT, the need for good stewardship has never been more important. This article looks at current AMS practice in SOT units in the United Kingdom and how things could be improved in the future. METHODS: The current practice of AMS alongside national antimicrobial resistance rates were reviewed using national mandatory reporting data. The background to the current practice and policies in place in the National Health Service (NHS) were also reviewed and possibilities for future approaches explored. RESULTS: AMS is a requirement within all NHS hospitals in the United Kingdom as per government policy. Mandatory reporting of specific bloodstream infections (BSIs) and antimicrobial consumption alongside financial incentives has been the approach nationwide. Gram-negative resistance rates in BSIs have been increasing prior to the COVID-19 pandemic. Little SOT-specific data on antimicrobial resistance exists, and the general approach to AMS in SOT units has generally modeled the national approach. CONCLUSION: Although there is a good, standardized approach to AMS in the NHS, there is a need for SOT-specific AMS approaches to be developed in the United Kingdom. More data is required on antimicrobial resistance rates, and studies are needed to investigate optimal antimicrobial prophylaxis regimens for each solid organ group. Tools to aid AMS efforts and novel treatment options for complex multiresistant infection must also be explored amongst transplant centers.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".