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 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".