Probiotic Supplementation in the Prevention of Necrotizing Enterocolitis in Preterm Neonates
Bibliographic record
Abstract
Abstract Necrotizing enterocolitis (NEC) is one of the most devastating gastrointestinal emergencies in preterm neonates, particularly those with very low birth weight (VLBW, <1,500 g). It is characterized by inflammation and necrosis of the intestinal wall, often leading to perforation, sepsis, and death. Despite advances in neonatal intensive care, mortality from NEC remains as high as 20–30%, and survivors frequently suffer from long-term complications such as short bowel syndrome and neurodevelopmental delay. Disruption of the intestinal microbiota and impaired mucosal immunity are key contributors to its pathogenesis. Probiotic supplementation, by introducing beneficial microbial strains, has been proposed as a preventive strategy that can modulate gut colonization, enhance mucosal barrier integrity, and downregulate pro-inflammatory responses. Objective: This study aimed to systematically evaluate the efficacy and safety of probiotic supplementation in preventing NEC in preterm neonates, with a focus on clinical outcomes including NEC incidence, mortality, and sepsis. Methods: A systematic search of PubMed, Embase, Scopus, and the Cochrane Library was performed from inception to [Month, Year]. Eligible studies included randomized controlled trials (RCTs) and observational studies comparing probiotic supplementation with placebo or standard care in preterm neonates (<37 weeks’ gestation). The primary outcome was incidence of NEC stage II or higher (Bell’s criteria). Secondary outcomes included all-cause mortality, late-onset sepsis, feeding intolerance, duration of hospitalization, and adverse events. Data extraction was performed independently by two reviewers. Risk of bias was assessed using the Cochrane tool for RCTs and the Newcastle–Ottawa Scale for observational studies. Pooled risk ratios (RRs) with 95% confidence intervals (CIs) were calculated using a random-effects model. Subgroup analyses were conducted based on probiotic strain (single vs multi-strain), birth weight categories (extremely low birth weight vs very low birth weight), and geographical setting (developed vs developing countries). Results: Twenty-seven RCTs encompassing 6,655 preterm neonates (3,298 probiotic vs 3,357 control) showed a significant reduction in NEC incidence (RR 0.35; 95% CI 0.27–0.44; P < 0.00001) and overall mortality (RR 0.58; 95% CI 0.46–0.75; P < 0.0001). No increase in sepsis risk was observed (RR 0.94; 95% CI 0.83–1.06; P = 0.31). An updated meta-analysis of 70 studies (8,319 cases and 9,283 controls) corroborated these findings, showing NEC reduction (RR 0.436; 95% CI 0.357–0.531; P < 0.001) and lower overall mortality (RR 0.651; 95% CI 0.506–0.836; P < 0.001), as well as NEC-related mortality (RR 0.639; 95% CI 0.423–0.966; P = 0.034). A network meta-analysis of 51 RCTs (11,661 infants) found that multi-strain combinations (e.g., Bifidobacterium + Lactobacillus + Streptococcus) significantly reduced mortality and NEC incidence, with striking efficacy (RR for combination including Streptococcus: 0.17; 95% CI 0.00–0.84) Conclusion: Probiotic supplementation appears to be a safe and effective intervention for reducing the incidence of NEC and all-cause mortality in preterm neonates, particularly when multi-strain preparations are used. However, variability in probiotic strains, dosages, initiation timing, and study methodologies highlights the need for standardized, large-scale, multicenter RCTs to establish universal clinical guidelines. Until then, cautious adoption with strain-specific consideration may be warranted in neonatal intensive care practice.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".