Evidence map of open abdomen treatment: protocol for a living systematic review and meta-analysis
Christian Benignus, Christoph Marquardt, Thomas Schiedeck, Pascal Probst, Julian Müller-Kühnle, Markus Diener, Felix J. Hüttner, Sebastian SchaafAbstract
Objectives
Open abdomen treatment is a complex surgical strategy used in emergency settings, including trauma, abdominal compartment syndrome, and severe intra-abdominal sepsis. As evidence continues to expand, maintaining an up-to-date overview is increasingly challenging. Living evidence mapping combines systematic evidence synthesis with continuous updates to identify research gaps and support evidence-based clinical decision-making. Although applied in other surgical fields, no living evidence map currently exists for open abdomen treatment. This study aims to develop the first living evidence map in this field.
Methods
Following PRISMA guidelines, we will develop a living evidence map covering open abdomen treatment, abdominal compartment syndrome, enteroatmospheric fistulae, and burst abdomen. Systematic searches will be performed in CENTRAL, Web of Science, MEDLINE, and Embase. Randomized controlled trials, nonrandomized comparative studies, and systematic reviews meeting predefined eligibility criteria will be included. Two reviewers will independently extract data on surgical strategies, temporary abdominal closure techniques, complications, and clinical outcomes. Certainty of evidence will be assessed using GRADE. Meta-analyses using random-effects models will be conducted where at least three randomized controlled trials address the same research question. The evidence map will be updated every 6 months.
Results
This protocol describes the development of the first living evidence map for open abdomen treatment. By continuously synthesizing and categorizing evidence, it aims to improve literature accessibility, identify evidence gaps, and support evidence-based clinical decision-making.
Conclusions
Future integration of artificial intelligence may further enhance automation and evidence surveillance.