Antimicrobial resistance and mortality following E. coli bacteremia
Bibliographic record
Abstract
Background: Global estimates suggest millions of deaths annually are associated with antimicrobial resistance (AMR) but these are generated from scarce data on the relative risk of death attributable to drug-resistant versus drug-sensitive infections. Methods: bloodstream infection in Ontario, Canada between 2017 and 2020, and measured 90 day mortality among those with resistant versus sensitive isolates for each of 8 commonly used antibiotic classes and a category of difficult to treat resistance (DTTR). We used multivariable logistic regression to calculate an adjusted odds of mortality associated with AMR, after accounting for patient demographics, comorbidities, and prior healthcare exposure. Findings: bloodstream infection, resistance was most common to aminopenicillins (46.8%), followed by first generation cephalosporins (38.8%), fluoroquinolones (26.5%), sulfonamides (24.1%), third generation cephalosporins (13.8%), aminoglycosides (11.7%), beta-lactam-beta-lactamase-inhibitors (9.1%) and carbapenems (0.2%). Only 18 (0.1%) episodes exhibited DTTR. For each antibiotic class, the unadjusted odds of mortality (OR) were higher among resistant isolates, but after accounting for patient characteristics the adjusted odds (aOR) of mortality were attenuated: aminopenicillins (OR 1.22, 95% CI 1.12-1.33; aOR 1.09, 95% CI 0.99-1.20), first generation cephalosporins (OR 1.24, 95% CI 1.14-1.35; aOR 1.07, 95% CI 0.97-1.18), third generation cephalosporins (OR 1.64, 95% CI 1.47-1.82; aOR 1.29, 95% CI 1.15-1.46), beta-lactam-beta-lactamase-inhibitors (OR 1.69, 95% CI 1.52-1.89, aOR 1.28, 95% CI 1.13-1.45), carbapenems (OR 3.11, 95% CI 1.52-6.34; aOR 2.06, 95% CI 0.91-4.66), sulfonamides (OR 1.19, 95% CI 1.07-1.31, aOR 1.06, 95% CI 0.95-1.18), fluoroquinolones (OR 1.49, 95% CI 1.36-1.64, aOR 1.16, 95% CI 1.05-1.29), aminoglycosides (OR 1.43, 95% CI 1.27-1.62; aOR 1.27, 95% CI 1.11-1.46), and DTTR (OR 3.71, 95% CI 1.46-9.41; aOR 2.58, 95% CI 0.87-7.66). Interpretation: bloodstream infection, particularly for resistance to classes commonly used as empiric treatment. Surveillance for AMR-associated mortality should incorporate adjustment for patient characteristics and prior healthcare utilization. Funding: This work was supported by a project grant from CIHR (grant number 159503). This study was also supported by ICES, which is funded by an annual grant from Ontario Ministry of Health and Long-Term Care (MOHLTC).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".