A Systematic Review and Meta-Analysis of Prediction Scores for Anastomotic Leak Following Colorectal Surgery.

Authors

Danilo Coco, Silvana Leanza

Published in

Maedica. Volume 21. Issue 2. Pages 468-476.

Abstract

Anastomotic leak (AL) remains one of the most serious postoperative complications in colorectal surgery. Numerous predictive models have been proposed to identify patients at risk, yet their comparative validity, consistency and methodological rigor remain unclear. This review aimed to critically evaluate and summarize the performance of currently available AL prediction tools.
A systematic search of PubMed, Embase and the Cochrane Central Register of Controlled Trials was performed in accordance with PRISMA 2020 guidelines. Eligible studies included those proposing or evaluating risk scores for AL using preoperative or intraoperative factors. Extracted data covered model characteristics and discrimination metrics, particularly the area under the curve (AUC). Risk of bias was assessed with the PROBAST tool. Where appropriate, random-effects meta-analysis of AUC values was planned.
Nine studies introducing nine distinct prediction models met the inclusion criteria. Commonly used predictors included sex, American Society of Anesthesiologists (ASA) classification and distance of the anastomosis from the anal verge. Only four models underwent validation and AUC values were reported in five studies. Considerable variation in model structure and inadequate reporting prevented a reliable pooled meta-analysis. Overall, methodological quality was suboptimal, with a high risk of bias observed in several domains.
Current prediction scores for AL after colorectal surgery show substantial heterogeneity and insufficient external validation. The lack of standardized development approaches and inconsistent reporting of performance measures limit their clinical utility. Future research should prioritize transparent methodology, large prospective datasets and comprehensive validation to improve predictive reliability.

PMID:
42416753
Bibliographic data and abstract were imported from PubMed on 08 Jul 2026.

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