Cumulative Antimicrobial Susceptibility Data from Intensive Care Units at One Institution: Should Data Be Combined?
Bibliographic record
Abstract
Cumulative susceptibility test data (CSTD) are used to guide empirical antimicrobial therapy and to track trends in antibiotic resistance. The Clinical and Laboratory Standards Institute recommends reporting CSTD at least annually and sets the minimum number of isolates per reported organism at 30. To comply, many hospitals combine data from multiple intensive care units (ICUs); however, this may not be appropriate to guide empirical therapy because of variations in patient populations. In this study, susceptibility data for two different ICUs at a tertiary care hospital in Toronto, Canada, were used to create a traditional CSTD report, which combined data from different ICUs, and a rolling-average CSTD report, which pooled 2 years of data for each ICU separately. For simplicity, data for only the most common Gram-negative organisms (Escherichia coli,Pseudomonas aeruginosa) and the most relevant antibiotics (ciprofloxacin, piperacillin-tazobactam) were examined. With the rolling-average method, significant differences in susceptibility were seen between the ICUs in 50% of the organism-antimicrobial combinations. Furthermore, the 3% median year-over-year difference in susceptibilities seen for the 16 organism-antibiotic combinations by using the traditional method was lower than the 14% median difference seen for the 20 between-ICU within-year comparisons obtained using the rolling-average method. Changes in our selection of empirical antibiotics resulted from this revised approach, and our results suggest that pooling data from ICUs with different patient populations may not be appropriate. A rolling-average method may be an appropriate strategy for the creation of individual-unit CSTD reports.
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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 | 0.150 | 0.390 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.017 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".