Epidemiology and outcome of antimicrobial resistance to gram‐negative pathogens in bacteriuric kidney transplant recipients
Bibliographic record
Abstract
BACKGROUND: In kidney transplant recipients, episodes of bacteriuria are often treated regardless of the presence of symptoms because of the lack of clear treatment guidelines suggesting otherwise. This practice may lead to the development of antimicrobial resistance. Our aim was to determine the incidence, determinants, and impact of antimicrobial resistance in kidney transplant recipients with gram-negative bacteriuria. METHOD: We conducted a single-center, retrospective cohort study in patients who underwent kidney transplantation between January 2008 and June 2013. To identify risk factors for the development of resistance, we used a logistic regression model with generalized estimating equations to account for within-subject correlation. RESULTS: Among the 318 patients who underwent kidney transplantation during the study period, 147 patients developed 555 gram-negative episodes of bacteriuria. Resistance to trimethoprim-sulfamethoxazole and quinolones, and production of extended-spectrum β-lactamase (ESBL) occurred in 52%, 21%, and 5% of isolated microorganisms, respectively. An increased risk of resistance to quinolones and production of ESBL were associated with concomitant diabetes (odds ratio [OR]: 2.29, 95% confidence interval [CI]: 1.11-4.74), the first year post transplantation (OR: 2.88, 95% CI: 1.36-6.09), and antibiotic treatment in the previous 6 months (OR: 3.36, 95% CI: 1.66-6.81). This resistance profile was also associated with the presence of symptoms, a longer duration of antibiotic treatment, and a higher rate of hospitalization. CONCLUSION: Antimicrobial resistance to quinolones and production of ESBL were commonly seen, and were shown to demonstrate an adverse impact on outcomes in kidney transplant recipients with gram-negative bacteriuria. The decision on treatment for asymptomatic bacteriuria should be made with caution, given the potential for the selection of resistant strains.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".