Pathogens and antimicrobial susceptibility profiles in critically ill patients with bloodstream infections: a descriptive study
Bibliographic record
Abstract
Background: Surveillance of antimicrobial resistance is vital to guiding empirical treatment of infections. Collating and reporting routine data on clinical isolate testing may offer more timely information about resistance patterns than traditional surveillance network methods. Methods: Using routine microbiology testing data collected from the Bacteremia Antibiotic Length Actually Needed for Clinical Effectiveness retrospective cohort study, we conducted a descriptive secondary analysis among critically ill patients in whom bloodstream infections had been diagnosed in 14 intensive care units (ICUs) in Canada. The participating sites were located within tertiary care teaching hospitals and represented 6 provinces and 10 cities. More than 80% of the study population was accrued from 2011-2013. We assessed the epidemiologic features of the infections and corresponding antimicrobial susceptibility profiles. Susceptibility testing was done according to Clinical Laboratory Standards Institute guidelines at accredited laboratories. Results: A total of 1416 pathogens were isolated from 1202 patients. The most common organisms were Escherichia coli (217 isolates [15.3%]), Staphylococcus aureus (175 [12.4%]), coagulase-negative staphylococci (117 [8.3%]), Klebsiella pneumoniae (86 [6.1%]) and Streptococcus pneumoniae (85 [6.0%]). The contribution of individual pathogens varied by site. For 13 ICUs, gram-negative susceptibility rates were high for carbapenems (95.4%), tobramycin (91.2%) and piperacillin-tazobactam (90.0%); however, the proportion of specimens susceptible to these agents ranged from 75.0%-100%, 66.7%-100% and 75.0%-100%, respectively, across sites. Fewer gram-negative bacteria were susceptible to fluoroquinolones (84.5% [range 64.1%-97.2%]). A total of 145 patients (12.1%) had infections caused by highly resistant microorganisms, with significant intersite variation (range 2.6%-24.0%, χ2 = 57.50, p < 0.001). Interpretation: We assessed the epidemiologic features of bloodstream infections in a geographically diverse cohort of critically ill Canadian patients using routine pathogen and susceptibility data extracted from readily available microbiology testing databases. Expanding data sharing across more ICUs, with serial measurement and prompt reporting, could provide much-needed guidance for empiric treatment for patients as well as system-wide prevention methods to limit antimicrobial resistance.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".