Antimicrobial resistance–attributable mortality: a patient-level analysis
Bibliographic record
Abstract
Abstract Background The impact of antimicrobial resistance (AMR) on death at the patient level is challenging to estimate. We aimed to characterize AMR-attributable deaths in a large UK teaching hospital. Methods This retrospective study investigated all deceased patients in 2022. Records of participants were independently reviewed by two investigators for cases of AMR-attributable deaths using a newly proposed patient-level definition. Results In total, 758 patients met inclusion criteria. Infection was the underlying cause of death for 11.7% (89/758) and was implicated in the pathway that led to death in 41.1% (357/758) of participants. In total, 4.2% (32/758) of all deaths were AMR-attributable. Median time from index sample collection to death was 4.5 days (IQR 2–10.5 days). The majority of AMR-attributable deaths (56.3%, 18/32) were associated with intrinsic resistance mechanisms, primarily by Enterococcus faecium (20.7%), Enterobacterales carrying repressed chromosomal ampicillinase Cs (AmpCs) (14.7%) and Pseudomonas aeruginosa (11.8%), whereas a minority (43.7%, 14/32) had acquired resistance mechanisms, primarily derepressed chromosomal AmpCs (11.8%) and ESBLs (8.8%). The median time to effective treatment was 32 h 15 min (no difference between subgroups). Only 62.5% (20/32) of AMR-attributable deaths had infection recorded on the death certificate. AMR was not recorded as a cause of death in any of the patients. Conclusions Infection and AMR were important causes of death in our cohort, yet they were significantly underreported during death certification. In a low-incidence setting for AMR, pathogen-antimicrobial mismatch due to intrinsic resistance was an equally important contributor to AMR-attributable mortality as acquired resistance mechanisms.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".