Antimicrobial susceptibility of Enterobacterales causing infection in the elderly: focus on aztreonam-avibactam and recently approved β-lactamase inhibitor combinations
Bibliographic record
Abstract
Abstract Background The US elderly population (≥65 years old) increased markedly in the last decades, and infections are responsible for approximately one-third of all deaths in this population. We evaluated the antimicrobial susceptibility of Enterobacterales causing infection in elderly patients in US hospitals. Methods Unique patient clinical isolates were consecutively collected from 72 US hospitals in 2021–2023 and tested for susceptibility by broth microdilution. Results for 10 574 Enterobacterales from elderly patients were analysed and compared with 9793 isolates from adult patients (18–64 years old). Carbapenem-resistant Enterobacterales (CRE) were screened for carbapenemases by whole-genome sequencing. Results All isolates from elderly patients were inhibited at aztreonam-avibactam MIC of ≤8 mg/L (>99.9% susceptible at ≤4 mg/L). Ceftazidime-avibactam and meropenem-vaborbactam were very active against Enterobacterales overall (≥99.7% susceptible) but exhibited limited activity against CRE (70.4%–71.6% susceptible). The most active agents against CRE were aztreonam-avibactam (98.8% susceptible), cefiderocol (96.3% susceptible) and tigecycline (96.3% susceptible). Susceptibility rates of isolates from the elderly were comparable (±≤ 2.6%) with those from the adult population; however, the frequencies of CRE and MDR phenotypes were lower among the elderly than adults. The most common carbapenemase among CREs from elderly patients were Klebsiella pneumoniae carbapenemases (55.6% of CRE) and NDM (24.7%); a metallo-β-lactamase was identified in 28.4% of CRE isolates. Conclusions Enterobacterales causing infections in the elderly population showed a similar antimicrobial resistance profile but a lower frequency of CRE and MDR isolates to those causing infection in the adults.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".