Risk prediction models in emergency surgery: Protocol for a scoping review
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Risk prediction models in emergency surgery : Protocol for a scoping review. / Hansted, Anna K.; Møller, Morten H.; Møller, Ann M.; Burcharth, Jakob; Thorup, Sofie S.; Vester-Andersen, Morten.
I: Acta Anaesthesiologica Scandinavica, Bind 68, Nr. 4, 2024, s. 579-581.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Risk prediction models in emergency surgery
T2 - Protocol for a scoping review
AU - Hansted, Anna K.
AU - Møller, Morten H.
AU - Møller, Ann M.
AU - Burcharth, Jakob
AU - Thorup, Sofie S.
AU - Vester-Andersen, Morten
N1 - Publisher Copyright: © 2024 Acta Anaesthesiologica Scandinavica Foundation.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - emergency surgery
KW - perioperative
KW - risk
KW - risk prediction
KW - risk stratification
U2 - 10.1111/aas.14383
DO - 10.1111/aas.14383
M3 - Journal article
C2 - 38317635
AN - SCOPUS:85184268807
VL - 68
SP - 579
EP - 581
JO - Acta Anaesthesiologica Scandinavica
JF - Acta Anaesthesiologica Scandinavica
SN - 0001-5172
IS - 4
ER -
ID: 382986597