What is the impact of nutrition interventions on incidence of anastomotic leak after colorectal surgery? A systematic review of randomized trials
Bibliographic record
Abstract
Nutrition-related factors have been associated with increased incidence of anastomotic leak (AL) following colorectal surgery; however, specific perioperative nutrition recommendations remain unclear. This systematic review examined the extent to which perioperative nutrition interventions influence the incidence of AL compared to standard care. On November 4, 2024, we searched Medline, Embase, CINAHL, Scopus, and Google Scholar. Eligible studies were randomized controlled trials involving adults undergoing colorectal surgery for any indication. Interventions included any perioperative nutrition or dietary support versus standard care, and our primary outcome was incidence of AL, defined as a failed surgical anastomosis with leakage of bowel contents. The risk of bias was assessed using the Cochrane tool, and findings were synthesized narratively. Sixteen trials (n = 2013) were included, primarily from Asia and Europe, with most trials (81 %) exclusively recruiting patients undergoing surgery for colorectal cancer. Interventions included probiotics (50 %), parenteral nutrition (25 %), enteral nutrition (6 %), oral nutrition supplements (12 %), and mixed therapies (6 %). A total of 139 (6.9 %) AL events were reported, although most trials (63 %) did not clearly define or explain AL measurement and only one listed AL as the primary outcome. Most trials (81 %) showed no difference in incidence of AL; however, two trials (13 %) saw a reduction, and one trial (6 %) reported an increase. Eight (50 %) trials were at high risk of bias. In conclusion, few low-risk-of-bias trials have evaluated the influence of perioperative nutrition on anastomotic leak. Given the infrequent occurrence of AL, large, adequately powered studies are required to detect meaningful differences. Consequently, current evidence remains insufficient to draw firm conclusions about the role of perioperative nutrition on AL risk.
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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 | 0.010 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.017 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".