Mortality and Outcomes of Patients Infected with Extensively Drug-Resistant Bacteria Admitted to Intensive Care Units
Bibliographic record
Abstract
Background: The emergence of antimicrobial-resistant pathogens in intensive care units (ICU) makes treatment of these infections very difficult. Objectives: This study aimed to survey the resistance phenotype, risk factors, and possible treatment options for hospital-acquired infections (HAI) at ICUs in a referral university hospital. Methods: Samples were prepared from ICU patients with HAIs. Pathogens were isolated, and the resistance phenotype, prescribed antibiotics, mortality rate, associated risk factors, and clonal relationship of the isolates were assessed. Results: From 100 patients admitted to ICUs, 76 (76%) showed clinical symptoms of HAIs. The most common sources of infection were lung secretions (51.8%). The isolated bacteria were Gram-negative (77, 90.6%) and Gram-positive (9.4%). The most common isolated microorganism was the Acinetobacter baumannii complex (63.5%). The most effective antimicrobials were colistin (96.1%) and linezolid (100%) for Gram-negative and Gram-positive isolates, respectively. An extensively drug-resistant (XDR) phenotype was documented in all isolates of A. baumannii, Pseudomonas aeruginosa, and Serratia marcescens. The XDR phenotype was a significant risk factor only for mortality. A. baumannii had the highest mortality rate (66.6%) among all the isolates. While A. baumannii strains had limited diversity, the other species were highly divergent. Conclusions: The results revealed a high mortality rate for Gram-negative isolates, especially in adenosine triphosphate-binding cassette (ABC). The high genetic similarities of ABC strains might be an indication of neglecting infection control prevention. The use of cotrimoxazole and colistin instead of carbapenems can be promising for mortality reduction in the studied ICUs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".