Are We Overestimating the Effect of Indocyanine Green on Leaks Following Colorectal Surgery: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
ABSTRACT Introduction Systematic reviews of retrospective studies suggest that indocyanine green (ICG) angiography reduces anastomotic leak (AL) and improves postoperative outcomes. This systematic review and meta-analysis evaluates colorectal surgery outcomes following ICG use with comparison of results found in randomized controlled trials (RCTs) and retrospective studies. Methods A systematic search was conducted of studies evaluating ICG in colorectal surgery with more than five patients. Systematic search of MEDLINE, Embase, Scopus, and Web of Science was conducted in August 2021 and this study followed PRISMA and MOOSE guidelines. Primary outcome was AL. Meta-analysis was conducted with RevMan 5.4. Results Overall, 2403 studies were retrieved with 28 total studies including three RCTs meeting criteria. RCTs included 964 patients, whereas other studies comprised 7327 patients with 44.6% receiving ICG. The ICG and non-ICG cohorts were similar with respect to age (62.6 vs 63.1 years), sex (45.1% vs 43.1% female), smoking (22.4% vs 25.3% smokers), and diabetes (13.4% vs 14.2%), respectively. Anastomotic height (6.5 vs 6.8 cm) and technique (78.7% vs 74.8% stapled) were also comparable. With retrospective studies included, ICG was associated with AL reduction (odds ratio [OR] 0.41; 95% CI, 0.32–0.53; p < 0.001) and reoperation for AL (OR 0.64; 95% CI, 0.43–0.95; p = 0.03), with pronounced effects for rectal anastomoses (OR 0.31; 95% CI, 0.21–0.44; p < 0.001). RCT evidence suggests a much smaller effect size (OR 0.64; 95% CI, 0.42–0.99; p = 0.04), and no reduction in AL reoperation (OR 0.72; 95% CI, 0.29–1.80; p = 0.48) or length of stay (LOS). Conclusion Retrospective studies suggest reduced AL, reoperation for AL, and LOS with ICG angiography. However, RCTs suggest a smaller effect size and do not demonstrate reduced reoperation or LOS. Additional RCTs are required before widespread ICG uptake.
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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 | 0.078 | 0.142 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.021 | 0.050 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.002 |
| 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".