Pattern of acquisition of hospital-associated pathogens in the ICU of an academic tertiary care hospital
Bibliographic record
Abstract
BACKGROUND: Among hospitalized patients, a 48-hour window from time of hospitalization defines nosocomial infections and guides empiric antibiotic selection. This time frame may lead to overuse of broad-spectrum antibiotics. Our primary objective was to determine the earliest and median time since hospital admission to acquire antibiotic-resistant pathogens among patients admitted to the intensive care unit (ICU) of an academic, tertiary care hospital. METHODS: Retrospective chart review was conducted for adult patients admitted to the ICU from home or another hospital within the same health authority in 2018, to identify the time to acquisition of hospital-associated pathogens: methicillin-resistant Staphylococcus aureus, vancomycin-resistant enterococci, extended-spectrum beta-lactamase (ESBL)–producing Enterobacterales, non-ESBL ceftriaxone-resistant Enterobacterales, Pseudomonas aeruginosa, and Stenotrophomonas maltophilia. Patients transferred from hospitals outside the health authority, admitted to ICU after 14 days of hospitalization, who were solid organ or bone marrow transplant recipients, or who were otherwise immunocompromised were excluded. RESULTS: In 2018, 1,343 patients were admitted to this ICU; 820 met the inclusion criteria. Of these, 121 (14.76%) acquired a hospital-associated pathogen in the ICU. The probability of isolating a hospital-associated pathogen by 48 hours of hospital admission was 3%. The earliest time to isolate any of these pathogens was 29 hours, and the median was 9 days (interquartile range [IQR] 3.8–15.6 days). CONCLUSIONS: Most patients (85.3%) in this ICU never acquired a hospital-associated pathogen. The median time to acquire a hospital-associated pathogen among the remaining patients suggests that initiating empiric broad-spectrum antibiotics on the basis of a 48-hour threshold may be premature.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".