Decrease in vancomycin MICs and prevalence of hGISA in MRSA and MSSA isolates from a German pediatric tertiary care center
Bibliographic record
Abstract
PURPOSE: Resistance of Staphylococcus aureus to vancomycin includes a general increase of minimal inhibitory concentrations (MIC) within the susceptible range over time (Vancomycin MIC Creep) and the presence of a subset of the bacterial population that expresses resistance (heterogeneous glycopeptide-intermediate S. aureus; hGISA). Increased MICs have been associated with adverse clinical outcomes. However, the vancomycin MIC creep is not a uniform trend suggesting the importance of regional surveys. METHODS: We performed a retrospective analysis at a German pediatric tertiary care hospital. Isolates from 2002 to 2017 were selected which were newly identified methicillin-resistant S. aureus (MRSA) or samples from invasive methicillin-susceptible S. aureus (MSSA) or MRSA infections. Vancomycin and oxacillin MICs as well as GISA/hGISA were measured using MIC test strips and resistance was evaluated over time. RESULTS: A total of 540 samples were tested, 200 from the early (2002-2009) and 340 from the later period (2010-2017). All samples were vancomycin susceptible, but the MIC was higher for the earlier samples as compared to the later ones (1.11 vs 0.99; p < 0.001). 14% of the samples were hGISA, GISA strains were not detected. Again, vancomycin resistance decreased over time with 28 vs. 6% hGISA (p < 0.001). There was no significant difference between MRSA and MSSA samples with respect to vancomycin MIC and hGISA prevalence. CONCLUSION: This study shows a decreasing trend for both MIC values and presence of hGISA strains highlighting the importance of monitoring local susceptibilities. Vancomycin remains a first-line treatment option for suspected severe infection with Gram-positive cocci and proven infection with MRSA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".