The Burden of Antimicrobial Resistant Bacteremia in Ontario: A Population-Wide Analysis of Attributable Mortality From 110 Pathogen-Antibiotic Combinations
Bibliographic record
Abstract
BACKGROUND: Reliable information on the burden of antimicrobial resistance (AMR) is necessary to confront the threat of antimicrobial resistance. We sought to examine the association between AMR and mortality across cultured bacterial bloodstream pathogens in the province of Ontario, Canada. METHODS: We used linked microbiology data from 114 hospital, community, and public health laboratories to develop a positive bacterial blood culture episode cohort, between January 2017 and December 2021, for the population of Ontario, Canada (population 14.6 million). Antibiotics tested in >10% of cultures of a pathogen, with resistance 1%-99%, were eligible. We used separate proportional hazards models for each pathogen, to estimate the hazard ratio of 30-day mortality for each eligible antibiotic, adjusting for patient risk factors, and summarized results using mixed-effects meta-analysis. RESULTS: We identified 83 962 bacteremia episodes, due to 30 pathogens, and 110 eligible pathogen-antibiotic combinations. The 30-day mortality was 17.1% (14 362/83 962). Unadjusted associations between resistance and 30-day mortality were substantially larger (hazard ratio [HR] = 1.47; 95% confidence interval [CI], 1.32-1.65) than adjusted associations accounting for age, sex, healthcare exposures, comorbidities, and co-resistance (HR = 1.10; 95% CI, 1.07-1.16). Associations were larger for antibiotics commonly used for empiric treatment (HR = 1.18; 95% CI, 1.10-1.26). CONCLUSIONS: We found that antimicrobial resistance was associated with a 10% relative increase in the risk of mortality among patients with bacteremia, and 1.2 AMR attributable deaths per 100 000 population per year in Ontario, Canada. Comprehensive risk adjustment is necessary for understanding the impact of AMR bacteremia on patient outcomes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.010 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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".