Risk prediction models in emergency surgery: Protocol for a scoping review

• Published in: Acta anaesthesiologica Scandinavica 2024-04, Vol.68 (4), p.579-581
• Main Authors: Hansted, Anna K., Møller, Morten H., Møller, Ann M., Burcharth, Jakob, Thorup, Sofie S., Vester‐Andersen, Morten
• Format: Article
• Language: English
• Subjects: Emergencies; emergency surgery; Evaluation; Humans; Meta-analysis; perioperative; Prediction models; Quality; Risk; Risk Assessment - methods; risk prediction; risk stratification; Surgery; Surgical Procedures, Operative; Systematic Reviews as Topic
• ISSN: 0001-5172; 1399-6576; 1399-6576
• DOI: 10.1111/aas.14383

Background Risk prediction models are used for many purposes in emergency surgery, including critical care triage and benchmarking. Several risk prediction models have been developed, and some are used for purposes other than those for which they were developed. We aim to provide an overview of the existing literature on risk prediction models used in emergency surgery and highlight knowledge gaps. Methods We will conduct a scoping review on risk prediction models used for patients undergoing emergency surgery in accordance with the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses extension for Scoping Reviews (PRISMA‐ScR). We will search Medline, EMBASE, and the Cochrane Library and include all study designs. We aim to answer the following questions: (1) What risk prediction models are used in emergency surgery? (2) Which variables are used in these models? (3) Which surgical specialties are the models used for? (4) Have the models been externally validated? (5) Where have the models been externally validated? (6) What purposes were the models developed for? (7) What are the strengths and limitations of the included models? We will summarize the results descriptively. The certainty of evidence will be evaluated using a modified Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Conclusion The outlined scoping review will summarize the existing literature on risk prediction models used in emergency surgery and highlight knowledge gaps.