2813. Impact of a Recent Change in Tobramycin Breakpoints (CLSI) on the Proportion of <i>Pseudomonas aeruginosa</i> Clinical Isolates that are Multidrug-Resistant: CANWARD, 2016 to 2021
Bibliographic record
Abstract
Abstract Background Pseudomonas aeruginosa (PA) is an important nosocomial pathogen. In 2023, tobramycin (TM) susceptibility breakpoints for PA were lowered (from ≤4 to ≤1 µg/mL) by the Clinical and Laboratory Standards Institute (CLSI). The purpose of this study was to evaluate whether this change will result in an increase in the proportion of PA clinical isolates that are considered multidrug-resistant (MDR). Methods PA clinical isolates were obtained from patients evaluated at hospitals across Canada (January 2016 to December 2021) as part of an ongoing national surveillance study (CANWARD). Susceptibility testing was carried out using custom broth microdilution panels. MICs were interpreted using current (2023) CLSI breakpoints, with the exception of TM (interpreted using the 2022 and 2023 breakpoints). MDR PA were defined as isolates testing not susceptible at least three of the following: ceftazidime, ciprofloxacin, meropenem, piperacillin-tazobactam, and TM (assessed with both the 2022 and 2023 CLSI breakpoints). Results 1649 PA isolates were included in this study. The proportion of isolates testing susceptible to TM using the 2022 (S ≤4 µg/mL) and 2023 (S ≤1 µg/mL) CLSI breakpoints were 93.6% and 84.8%, respectively. Overall, 21.6% (356/1649) and 22.7% (374/1649) of PA isolates were MDR, using the 2022 and 2023 CLSI breakpoints. Below, the in vitro activities of common antipseudomonal antimicrobials versus PA isolates from this study (all and MDR subset) are presented. Conclusion In this dataset, the proportion of MDR PA isolates was minimally altered by a recent change to the CLSI TM susceptibility breakpoints. A reduction in the proportion of PA isolates that were susceptible to TM using the new breakpoints was most pronounced for the MDR subset. Disclosures George Zhanel, PhD, Avir: Grant/Research Support|Iterum: Grant/Research Support|Merck: Grant/Research Support|Orimed: Grant/Research Support|Paladin Labs: Grant/Research Support|Pfizer: Grant/Research Support|Verity: Grant/Research Support|Zambon: Grant/Research Support
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".