Late-onset Carbapenem-resistant Enterobacteriaceae Sepsis Among Very Preterm Infants: A Multicenter Study in China
Bibliographic record
Abstract
BACKGROUND: Carbapenem-resistant Enterobacteriaceae (CRE) infections are emerging as a crisis in developing countries. We aim to investigate the epidemiologic characteristics and antibiotic treatment strategies of CRE sepsis in very preterm infants (VPIs). METHOD: This cross-sectional study included all infants born at 24-31 weeks of gestation or birth weight <1500 g who developed late-onset sepsis caused by Enterobacteriaceae , as recorded in the 2022 Chinese Neonatal Network database. Late-onset sepsis was defined as sepsis occurring after 72 hours of birth. RESULTS: Of 11,447 VPIs admitted, 205 infants had 207 episodes of Enterobacteriaceae -related late-onset sepsis, of which 27 (13.0%) were caused by CRE. The most common CRE pathogens were Klebsiella spp. (66.7%, 18/27). Multivariate analysis identified prior carbapenem exposure as an independent risk factor for CRE sepsis (adjusted odds ratio: 2.33; 95% confidence interval: 1.02-5.49). In the CRE group, mortality due to Enterobacteriaceae sepsis (22.2% vs. 10.1%, P = 0.07) and all-cause mortality during hospitalization (29.6% vs. 13.5%, P = 0.04) were both higher than that in the non-CRE group. For empirical antibiotic therapy, of the 27 CRE cases, 21 (77.8%) were treated with meropenem alone and 6 (28.6%) of these infants died from CRE sepsis. For definitive therapy, 17/22 (77.3%) received monotherapy, of which 12 (70.6%) were treated with meropenem, while 5 (22.7%) received combination therapy. CONCLUSIONS: In Chinese neonatal intensive care units, 13.0% of late-onset Enterobacteriaceae sepsis in VPIs was caused by CRE, which was associated with a significant mortality rate. Meropenem-based regimens remain the primary treatment for CRE sepsis, though with a high treatment failure rate.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".