E. coli bacteraemia and antimicrobial resistance following antimicrobial prescribing for urinary tract infection in the community
Bibliographic record
Abstract
BACKGROUND: Urinary tract infections are one of the most common infections in primary and secondary care, with the majority of antimicrobial therapy initiated empirically before culture results are available. In some cases, however, over 40% of the bacteria that cause UTIs are resistant to some of the antimicrobials used, yet we do not know how the patient outcome is affected in terms of relapse, treatment failure, progression to more serious illness (bacteraemia) requiring hospitalization, and ultimately death. This study analyzed the current patterns of antimicrobial use for UTI in the community in Scotland, and factors for poor outcomes. OBJECTIVES: To explore antimicrobial use for UTI in the community in Scotland, and the relationship with patient characteristics and antimicrobial resistance in E. coli bloodstream infections and subsequent mortality. METHODS: We included all adult patients in Scotland with a positive blood culture with E. coli growth, receiving at least one UTI-related antimicrobial (amoxicillin, amoxicillin/clavulanic acid, ciprofloxacin, trimethoprim, and nitrofurantoin) between 1st January 2009 and 31st December 2012. Univariate and multivariate logistic regression analysis was performed to understand the impact of age, gender, socioeconomic status, previous community antimicrobial exposure (including long-term use), prior treatment failure, and multi-morbidity, on the occurrence of E. coli bacteraemia, trimethoprim and nitrofurantoin resistance, and mortality. RESULTS: There were 1,093,227 patients aged 16 to 100 years old identified as receiving at least one prescription for the 5 UTI-related antimicrobials during the study period. Antimicrobial use was particularly prevalent in the female elderly population, and 10% study population was on long-term antimicrobials. The greatest predictor for trimethoprim resistance in E. coli bacteraemia was increasing age (OR 7.18, 95% CI 5.70 to 9.04 for the 65 years old and over group), followed by multi-morbidity (OR 5.42, 95% CI 4.82 to 6.09 for Charlson Index 3+). Prior antimicrobial use, along with prior treatment failure, male gender, and higher deprivation were also associated with a greater likelihood of a resistant E. coli bacteraemia. Mortality was significantly associated with both having an E. coli bloodstream infection, and those with resistant growth. CONCLUSION: Increasing age, increasing co-morbidity, lower socioeconomic status, and prior community antibiotic exposure were significantly associated with a resistant E. coli bacteraemia, which leads to increased mortality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".