Assessing the quality of antimicrobial prescribing in solid organ transplant recipients: a new frontier in antimicrobial stewardship
Bibliographic record
Abstract
Abstract Background: Post-transplant infections remain a leading cause of morbidity and mortality in solid organ transplant recipients (SOTRs) and local standardized antimicrobial treatment guidelines may contribute to improved clinical outcomes. Our study assessed the rate of therapeutic compliance with local standard guidelines in the treatment of common infections in SOTR, and their associated outcomes. Methods: Consecutive adult SOTRs admitted to the transplant floor from January–September 2020 and were treated for an infectious syndrome were followed until discharge or for 30 days following the date of diagnosis, whichever was shorter. Data was extracted from electronic medical records. Guideline compliance was characterized as either appropriate, effective but unnecessary, undertreatment, or inappropriate. Results: Nine hundred and thirty-six SOTR were admitted to the transplant ward, of which 328 patients (35%) received treatment for infectious syndromes. Guidelines were applicable to 252 patients, constituting 275 syndromes: 86 pneumonias; 82 urinary tract infections; 40 intra-abdominal infections; 38 bloodstream infections; and 29 C. difficile infections. 200/246 (81%) of infectious syndromes received appropriate or effective but unnecessary empiric treatment. In addition, appropriate tailoring of antimicrobials resulted in a significant difference in 30-day all-cause mortality (adjusted OR of 0.07, 95% CI 0.01–0.38; P = .002). Lastly, we found that guideline-compliant empiric therapy was found to prevent the development of multi-drug resistance in a time-dependent analysis (adjusted HR of 0.21, 95% CI 0.08–0.52; P = .001). Conclusion: Our data show that adherence to locally developed guidelines was associated with reduced mortality and resistant-organism development in our cohort of SOTR.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".