Interventions to prevent anastomotic leak after esophageal surgery: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Anastomotic leakage (AL) is a common and serious complication following esophagectomy. We aimed to provide an up-to-date review and critical appraisal of the efficacy and safety of all previous interventions aiming to reduce AL risk. METHODS: We searched MEDLINE and Embase from 1946 to January 2019 for randomized controlled trials (RCTs) evaluating interventions to minimize esophagogastric AL. Pooled risk ratios (RR) for AL were obtained using a random effects model. RESULTS: Two reviewers screened 441 abstracts and identified 17 RCTs eligible for inclusion; 11 studies were meta-analyzed. Omentoplasty significantly reduced the risk of AL by 78% [RR: 0.22; 95% CI: 0.10, 0.50] compared to conventional anastomosis (3 studies, n = 611 patients). Early removal of NG tube significantly reduced the risk of AL by 62% [RR: 0.38; 95% CI: 0.02, 0.65] compared to prolonged NG tube removal (2 studies, n = 293 patients); Stapled anastomosis did not significantly reduce the risk of AL [RR: 0.92; 95% CI: 0.45, 1.87] compared to hand-sewn anastomosis (6 studies, n = 1454 patients). The quality of evidence was high for omentoplasty (vs. conventional anastomosis), moderate for early NG tube removal (vs. prolonged NG tube removal), and very low for stapled anastomosis (vs. hand-sewn anastomosis). CONCLUSIONS: This is the first meta-analysis to summarize the graded quality of evidence for all RCT interventions designed to reduce the risk of AL following esophagectomy. Our findings demonstrated that omentoplasty significantly reduced the risk of AL with a high quality of evidence. Although early NG tube removal significantly reduced AL risk, there is a need for further research to strengthen the quality of evidence for this finding. Evidence profiles presented in our review may help inform the development of future clinical practice recommendations. Systematic review registration: CRD42019127181.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.030 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".