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Record W4415023893 · doi:10.1093/cid/ciaf494

Resistance to Antibiotics With High Empiric Treatment Relevance Explains Attributable Mortality Across 110 Pathogen-antibiotic Combinations

2025· article· en· W4415023893 on OpenAlexafffund
Kevin A. Brown, Nick Daneman

Bibliographic record

VenueClinical Infectious Diseases · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsSunnybrook Health Science CentreSunnybrook HospitalPublic Health OntarioUniversity of TorontoToronto Public Health
FundersCanadian Institutes of Health Research
KeywordsAntibioticsEmpiric treatmentAntibiotic resistanceRelevance (law)MEDLINEClinical significance

Abstract

fetched live from OpenAlex

To The Editor—We agree with Lee and Chen that drivers of AMR-associated mortality remain poorly understood and that many avenues of research remain under-explored. Our study found that antibiotic resistance among patients with bacteremia was associated with a 10% relative increase in mortality [1]; a small increase compared with many published estimates, including those used to measure the global burden of AMR [2]. We believe that the principal reason for this disparity is that most studies lack the ability to comprehensively adjust for patient healthcare exposures, comorbidities, and co-resistance patterns. We nevertheless identified a stronger relative increase (18%) for the subset of pathogen-antibiotic pairs deemed to have high empiric treatment relevance, based on a blinded adjudication by 2 study authors. Regarding Lee and Chen's first point, indeed resistance to antibiotics with low treatment relevance had zero direct impact on risk in our meta-regression model, which controlled for comorbidities and co-resistance patterns. But, if resistance to an antibiotic with low treatment relevance was associated with resistance to an antibiotic with high treatment relevance, then, indeed, this low treatment relevance resistance could have prognostic value. Regarding their second point, the consistently high estimates in Pseudomonas aeruginosa and Acinetobacter spp., were at least partly attributable to the fact that the antibiotics routinely reported for these organisms almost all had moderate-to-high empiric treatment relevance. Compare this to, say, Staphylococcus aureus, for which several routinely reported antibiotics, are of low empiric treatment relevance (eg erythromycin). As such, some of the organism-specific findings may reflect the wider principle of empiric treatment relevance. Regarding ceftriaxone resistance among Escherichia coli, Klebsiella spp., and Enterobacter spp., we dug in to extract the underlying hazard ratios and confidence intervals in Supplement 1.3 (see Table 1). The underlying data suggested little evidence of heterogeneity between the 3 hazard ratios in the main meta-regression (HR = 1.19, 95% CI: 0.90–1.56, I2 = 0%). However, heterogeneity was observed after adjustment for covariates but prior to adjustment for co-resistance (HR = 1.47, 95% CI: 1.17, 1.85, I2 = 65%). These results point to the difficulty in studying the drivers of mortality in persons with AMR infections—in addition to very strong confounding due to healthcare exposures and comorbidities, co-resistance patterns make it even more difficult, and noisy, to disentangle. Unadjusted and Adjusted Associations Between Ceftriaxone Resistance and 30-Day Mortality in Escherichia coli, Klebsiella spp., and Enterobacter spp. Extracted From Supplement 1.3 Abbreviation: HR, heart rate. Unadjusted and Adjusted Associations Between Ceftriaxone Resistance and 30-Day Mortality in Escherichia coli, Klebsiella spp., and Enterobacter spp. Extracted From Supplement 1.3 Abbreviation: HR, heart rate. Regarding their fourth point, our funnel plot is simply a testament to the large statistical variation in estimates across the 110 pathogen-antibiotic pairs. Very few of the estimates were precise, even in this multi-year study from a large jurisdiction. Finally, we agree that treatment norms do vary, and the authors raise an important point. In other jurisdictions, we may anticipate a somewhat distinct assessment of empiric treatment relevance. However, treatment relevance may still be associated with outcomes in a similar way, since adequacy of empiric therapy is such a strong predictor of survival [3]. Overall, our study found comparatively small impacts of AMR on bacteremia outcomes overall, but stronger impacts for antibiotics with high empiric treatment relevance. Further research and data to better understand the underlying drivers of outcomes among patients with bacteremia are needed. Financial support. The original study was funded by the Canadian Institutes for Health Research (grant number 401316).

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.030
GPT teacher head0.363
Teacher spread0.334 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes2
Has abstractno

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