The efficacy of fiber-supplemented enteral nutrition in critically ill patients: a systematic review and meta-analysis of randomized controlled trials with trial sequential analysis
Bibliographic record
Abstract
Abstract Background Evidence on the benefits of fiber-supplemented enteral nutrition (EN) in critically ill patients is inconsistent, and critical care nutrition guidelines lack recommendations based on high-quality evidence. This systematic review and meta-analysis (SRMA) aims to provide a current synthesis of the literature on this topic. Methods For this SRMA of randomized controlled trials (RCT), electronic databases (MEDLINE, EMBASE, CENTRAL) were searched systematically from inception to January 2024 and updated in June 2024. Trials investigating clinical effects of fiber-supplemented EN versus placebo or usual care in adult critically ill patients were selected. Two independent reviewers extracted data and assessed the risk of bias of the included studies. Random-effect meta-analysis and trial sequential analysis (TSA) were conducted. The primary outcome was overall mortality, and one of the secondary outcomes was diarrhea incidence. Subgroup analyses were also performed for both outcomes. Results Twenty studies with 1405 critically ill patients were included. In conventional meta-analysis, fiber-supplemented EN was associated with a significant reduction of overall mortality (RR 0.66, 95% CI 0.47, 0.92, p = 0.01, I2 = 0%; 12 studies) and diarrhea incidence (RR 0.70, 95% CI 0.51, 0.96, p = 0.03, I2 = 51%; 11 studies). However, both outcomes were assessed to have very serious risk of bias, and, according to TSA, a type-1 error cannot be ruled out. No subgroup differences were found for the primary outcome. Conclusion Very low-certainty evidence suggests that fiber-supplemented EN has clinical benefits. High-quality multicenter RCTs with large sample sizes are needed to substantiate any firm recommendation for its routine use in this group of patients. PROSPERO registration number: CRD42023492829.
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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 | 0.041 | 0.086 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.043 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 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".