#74: Development of a Solid-Organ Transplant-Specific Antibiogram in a Tertiary Pediatric Hospital in Canada
Bibliographic record
Abstract
Abstract Background Solid-organ transplant (SOT) patients are more vulnerable to infections by antimicrobial-resistant organisms (AROs) because of their hospital exposure, compromised immune systems, and antimicrobial exposure. Therefore, it may be useful for transplant facilities to create transplant-specific antibiograms to direct empirical antimicrobial regimens and monitor trends in antimicrobial resistance. Methods SOT (i.e., lung, liver, renal, and heart) antibiograms were created using antimicrobial susceptibility data on isolates from 2012 to 2018 at The Hospital for Sick Children, a tertiary pediatric hospital and transplant center in Toronto, Ontario. The Clinical Laboratory Standards Institute (CLSI) guidelines were followed to generate the antibiograms. The first clinical isolate of a species from a patient in each year was included irrespective of body site; duplicates were eliminated and surveillance cultures were excluded. Results from 2 years of data were pooled on a rolling basis to achieve an adequate sample size in both SOT and hospital-wide antibiogram. The SOT antibiogram was then compared with the hospital-wide antibiogram of the compatible 2 pooled years from 2012 to 2018. For subgroup analyses in the SOT population, organ-specific antibiograms and transplant timing-specific antibiograms (pretransplant, post-transplant <1 year, and post-transplant ≥1 year) between transplant and sample collection dates were analyzed. All proportions were compared using the χ 2 test. Results The top 5 organisms in one (2 year) analysis period of the SOT antibiogram were Escherichia coli (n = 29), Staphylococcus aureus (n = 28), Pseudomonas aeruginosa (n = 20), Enterobacter cloacae complex (n = 18), and Klebsiella pneumoniae (n = 17). For E.coli, susceptibility in the SOT antibiogram was significantly lower than those in the hospital-wide antibiogram in 2017/2018 for ampicillin (27% vs. 48%; P = 0.015), piperacillin/tazobactam (55% vs. 87%; P < 0.001), cefotaxime (59% vs. 88%; P < 0.001), ciprofloxacin (71% vs. 87%; P = 0.007) and cotrimoxazole (41% vs. 69%; P < 0.001), but not significantly different for gentamicin (94% vs. 91%; P = 0.490), tobramycin (88% vs. 90%; P = 0.701) and amikacin (100% vs. 99%; P = 0.558). These findings were consistent throughout the study period in E.coli. There was no statistically significant difference between the SOT and hospital-wide antibiograms for other organisms. There were no significant differences in susceptibility between organ-specific antibiograms or transplant timing-specific antibiograms in 2012–2018. Conclusions We found that E.coli from the SOT population had a significantly lower sensitivity to all antimicrobials, except aminoglycosides, compared with those from the hospital-wide population. Other organisms had similar susceptibility to the hospital-wide population. Developing a SOT antibiogram will assist in revising and improving empiric treatment guidelines for this population.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".