Probiotics in Critical Illness: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVES: To determine the safety and efficacy of probiotics or synbiotics on morbidity and mortality in critically ill adults and children. DATA SOURCES: We searched MEDLINE, EMBASE, CENTRAL, and unpublished sources from inception to May 4, 2021. STUDY SELECTION: We performed a systematic search for randomized controlled trials (RCTs) that compared enteral probiotics or synbiotics to placebo or no treatment in critically ill patients. We screened studies independently and in duplicate. DATA EXTRACTION: Independent reviewers extracted data in duplicate. A random-effects model was used to pool data. We assessed the overall certainty of evidence for each outcome using the Grading Recommendations Assessment, Development, and Evaluation approach. DATA SYNTHESIS: Sixty-five RCTs enrolled 8,483 patients. Probiotics may reduce ventilator-associated pneumonia (VAP) (relative risk [RR], 0.72; 95% CI, 0.59 to 0.89 and risk difference [RD], 6.9% reduction; 95% CI, 2.7-10.2% fewer; low certainty), healthcare-associated pneumonia (HAP) (RR, 0.70; 95% CI, 0.55-0.89; RD, 5.5% reduction; 95% CI, 8.2-2.0% fewer; low certainty), ICU length of stay (LOS) (mean difference [MD], 1.38 days fewer; 95% CI, 0.57-2.19 d fewer; low certainty), hospital LOS (MD, 2.21 d fewer; 95% CI, 1.18-3.24 d fewer; low certainty), and duration of invasive mechanical ventilation (MD, 2.53 d fewer; 95% CI, 1.31-3.74 d fewer; low certainty). Probiotics probably have no effect on mortality (RR, 0.95; 95% CI, 0.87-1.04 and RD, 1.1% reduction; 95% CI, 2.8% reduction to 0.8% increase; moderate certainty). Post hoc sensitivity analyses without high risk of bias studies negated the effect of probiotics on VAP, HAP, and hospital LOS. CONCLUSIONS: Low certainty RCT evidence suggests that probiotics or synbiotics during critical illness may reduce VAP, HAP, ICU and hospital LOS but probably have no effect on mortality.
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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 | 0.028 | 0.072 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.032 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".