Antimicrobial resistance–attributable mortality: a patient-level analysis
Why this work is in the frame
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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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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 it