86 Predictors of early-onset sepsis and risk stratification decision making for preterm infants
Bibliographic record
Abstract
Abstract Background Early-onset sepsis (EOS) is a rare (<1%) but potentially catastrophic event if not treated early in infants born between 300/7 -346/7 weeks gestational age (GA). The standard of care includes initiation of antibiotics pending culture results leading to over-exposure to antibiotics with long-term adverse outcomes and antimicrobial resistance. A risk-stratification-based algorithm could help guide antibiotic use and improve antimicrobial stewardship. Objectives To identify risk factors for EOS preterm neonates born 300/7-346/7 weeks GA and compare different risk stratification approaches in this population. Design/Methods Retrospective observational study of infants born between 300/7-346/7 weeks’ GA admitted to the Canadian Neonatal Network Neonatal Intensive Care Units from January 1, 2018, to December 31, 2023. EOS was defined as having a positive blood and/or cerebrospinal fluid culture within 3 days of birth. A stepwise logistic regression analysis was used to identify significant risk factors and calculate adjusted odds ratios (AOR) and 95% confidence intervals (CI). Different risk groups were compared using the identified risk factors. Results The incidence of EOS was 0.6% (119/18,756) among eligible neonates and 42% (8060) were exposed to broad-spectrum empiric antibiotics for suspect EOS. E. Coli was the most common pathogen (57%, 68/119). Significant risk factors for EOS included GA (30 vs 31-33 vs 34 weeks), duration of rupture of membranes (0 min vs 1min to 24h vs >24h), maternal fever, no labour initiation and respiratory support on admission (none vs non-invasive vs invasive). The risk-factor-based multivariate prediction model had high accuracy (AUC 0.85, 95% CI 0.82-0.89). Neonates were stratified into three risk groups for EOS: low risk (0.04%, 2/4775), moderate risk (0.42%, 35/8297) and high risk (3.21%, 67/2085). Conclusion The risk factor-based model approach for suspected EOS management could guide more precise and optimize antibiotic use in preterm infants born between 300/7 and 346/7 weeks GA. Predictors of neonatal early-onset sepsis and risk stratification decision-making for preterm infants Quintero Salazar J, Ting J, Abou Mehrem A, Khurshid F, Boucoiran I, Shah PS, Beltempo M, on behalf of the Canadian Neonatal Network Investigators
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".