Using VRE screening tests to predict vancomycin resistance in enterococcal bacteremia
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Enterococcus causes clinically significant bloodstream infections (BSIs). In centers with a higher prevalence of vancomycin resistant enterococcus (VRE) colonization, a common clinical question is whether empiric treatment directed against VRE should be initiated in the setting of a suspected enterococcal BSI. Unfortunately, VRE treatment options are limited, and relatively expensive, and subject patients to the risk of adverse reactions. We hypothesized that the results of VRE colonization screening could predict vancomycin resistance in enterococcal BSI. METHODS: We reviewed 370 consecutive cases of enterococcal BSI over a 7-year period at 2 tertiary-care hospitals to determine whether vancomycin-resistant BSIs could be predicted based on known colonization status (ie, patients with swabs performed within 30 days, more remotely, or never tested). We calculated sensitivity and specificity, and we plotted negative predictives values (NPVs) and positive predictive values (PPVs) as a function of prevalence. RESULTS: A negative screening swab within 30 days of infection yielded NPVs of 90% and 95% in settings where <27.0% and 15.0% of enterococcal BSI are resistant to vancomycin, respectively. In patients with known VRE colonization, the PPV for VRE in enterococcal BSI was >50% at any prevalence exceeding 25%. CONCLUSIONS: The results of a negative VRE screening test result performed within 30 days can help eliminate unnecessary empiric therapy in patients with suspected enterococcal BSI. Conversely, patients with positive VRE screening swabs require careful consideration of empiric VRE-directed therapy when enterococcal BSI appears likely.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".