Antibiotic resistance among ICU patients during the COVID-19 pandemic and its associated factors: a retrospective study using electronic medical records in two Vietnamese hospitals
Bibliographic record
Abstract
Abstract Antimicrobial resistance (AMR) in intensive care units (ICUs) is a critical issue, which has been exacerbated by the coronavirus disease 2019 (COVID-19) pandemic. This study investigated AMR prevalence and its associated factors among ICU patients in two Vietnamese hospitals from January 2020 to June 2022. Electronic medical records of 1,296 patients with 2,432 non-duplicate bacterial isolates were collected in Phu Tho Hospital (Northern, rural, non-COVID-19 treatment) and 175 Hospital (Southern, urban, COVID-19 treatment centre). Antibiotic susceptibility testing was conducted using VITEK2, BD Phoenix 100, and disk diffusion methods. Logistic regression with 1,000 bootstrap resampling and cross-validation was used to examine factors linked to AMR. Results revealed Acinetobacter spp. (27.5%) as leading strains in Phu Tho Hospital, while Klebsiella spp. (28.0%) predominated in 175 Hospital, except during 2021when Acinetobacter spp. reached the peak. Alarmingly, Acinetobacter spp., Klebsiella spp., and Pseudomonas aeruginosa demonstrated the highest AMR rates and multidrug resistance rates (83.8%–95.8%) in both hospitals. Resistance to cephalosporins, carbapenems, and fluoroquinolones ranged from 75% to 100%. Significant associated factors included age, sex, location, initial admission diagnosis, and bacterial isolation month. This study highlights the urgent need for controlling AMR in ICUs during the pandemic.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".