# Microvascular errors of technique: a systematic review

## TL;DR

This systematic review identifies and categorizes technical errors in microvascular surgery, emphasizing how learning from these mistakes can improve surgical training and outcomes.

## Contribution

The study provides a structured taxonomy of microvascular errors and validates error-based learning as a training tool.

## Key findings

- Intra-operative errors like back-wall stitches and uneven lumens are most common and reduce anastomotic patency.
- Validated scoring tools such as ALI and MARS10 help quantify errors and improve feedback in training.
- Standardized error classification can enhance microsurgical training and clinical outcomes.

## Abstract

Microvascular procedures demand exceptional precision and are prone to technical errors that compromise outcomes. Performance improves fastest when errors are identified, measured, and corrected early during training. Error-based learning has become an essential part of microsurgical training, highlighting the importance of identifying and learning from mistakes to improve performance. This review aimed to systematically search the literature on all microsurgical errors and categorize them by operative phases.

A structured literature search was conducted across Medline, Embase, and Web of Science databases, following PRISMA guidelines. Two reviewers independently screened records and extracted data in duplicate. Articles were included if they evaluated microvascular anastomoses with vessels less than 2 mm in diameter, and if microsurgical errors were detailed along with their impact on outcomes, in particular on anastomotic patency. Given the heterogeneity of the data, a SWiM-style (Synthesis Without Meta-analysis) narrative synthesis was used.

A total of 34 studies met the inclusion criteria. Errors were categorized as pre-operative, intra-operative, and post-operative. Intra-operative errors were the most frequently reported. Back-wall stitches, uneven lumens, and excessive suture tension were consistently associated with reduced patency. Several validated scoring tools (e.g., ALI, MARS10, OSATS) were identified as effective in quantifying errors and guiding feedback in training settings.

Microvascular anastomosis errors span all phases of the microsurgical procedure and significantly affect anastomotic success. This review offers a structured taxonomy of errors and underscores the importance of error-based learning and assessment in microsurgical training. Standardized error classification may enhance training programs, accelerating the acquisition of microsurgical skills along the learning curve and improving clinical outcomes.

The online version contains supplementary material available at 10.1007/s00701-026-06810-w.