Beyond Early- and Late-onset Neonatal Sepsis Definitions: What are the Current Causes of Neonatal Sepsis Globally? A Systematic Review and Meta-analysis of the Evidence
Bibliographic record
Abstract
Sepsis remains a leading cause of neonatal mortality, particularly in low- and lower-middle-income countries (LLMIC). In the context of rising antimicrobial resistance, the etiology of neonatal sepsis is evolving, potentially making currently-recommended empirical treatment guidelines less effective. We performed a systematic review and meta-analysis to evaluate the contemporary bacterial pathogens responsible for early-onset sepsis (EOS) and late-onset neonatal sepsis (LOS) to ascertain if historical classifications-that guide empirical therapy recommendations based on assumptions around causative pathogens-may be outdated. We analyzed 48 articles incorporating 757,427 blood and cerebrospinal fluid samples collected from 311,359 neonates across 25 countries, to evaluate 4347 significant bacteria in a random-effects meta-analysis. This revealed Gram-negative bacteria were now the predominant cause of both EOS (53%, 2301/4347) and LOS (71%, 2765/3894) globally. In LLMICs, the predominant cause of EOS was Klebsiella spp. (31.7%, 95% CI: 24.1-39.7%) followed by Staphylococcus aureus (17.5%, 95% CI: 8.5 to 28.4%), in marked contrast to the Streptococcus agalactiae burden seen in high-income healthcare settings. Our results reveal clear evidence that the current definitions of EOS and LOS sepsis are outdated, particularly in LLMICs. These outdated definitions may be guiding inappropriate empirical antibiotic prescribing that inadequately covers the causative pathogens responsible for neonatal sepsis globally. Harmonizing sepsis definitions across neonates, children and adults will enable a more acurate comparison of the epidemiology of sepsis in each age group and will enhance knowledge regarding the true morbidity and mortality burden of neonatal sepsis.
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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 | 0.019 | 0.050 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.032 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".