Emergence of carbapenem-resistant Enterobacteriaceae as causes of bloodstream infections in patients with hematologic malignancies
Bibliographic record
Abstract
Carbapenem-resistant Enterobacteriaceae (CRE) are increasingly prevalent pathogens. However, little is known about their emergence in patients with hematologic malignancies. We identified 18 patients with hematologic malignancies over 3.5 years who developed bloodstream infections (BSIs) caused by CRE. Fourteen BSIs were caused by Klebsiella pneumoniae, three by Enterobacter cloacae, and one was polymicrobial. Initial empirical antimicrobial therapy was active in two patients (11%), and a median of 55 h elapsed between culture collection and receipt of an active agent. Ten patients (56%) died, including nine (69%) of 13 neutropenic patients, with a median of 4 days from culture collection until death. CRE isolates were analyzed for carbapenemase production, β-lactamase genes and outer membrane porin deletions and characterized by multilocus sequence typing and pulsed-field gel electrophoresis (PFGE). Carbapenem resistance mechanisms included Klebsiella pneumoniae carbapenemase production and CTX-M-15 production with an absent outer membrane porin protein. No isolate had ≥95% homology on PFGE, indicating a heterogeneous, non-outbreak population of isolates. CRE BSIs are emerging in patients with hematologic malignancies and are associated with ineffective initial empirical therapy, long delays in administration of active antimicrobials and high mortality rates. New diagnostic, therapeutic and preventive strategies for CRE infections in this vulnerable population are needed.
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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 | 0.003 |
| 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 | 0.001 |
| 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".