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LO04: Canadian best practice diagnostic algorithm for acute aortic syndrome

Published online by Cambridge University Press:  02 May 2019

R. Ohle*
Affiliation:
Health Science North, Sudbury, ON
S. McIsaac
Affiliation:
Health Science North, Sudbury, ON
J. Yan
Affiliation:
Health Science North, Sudbury, ON
K. Yadav
Affiliation:
Health Science North, Sudbury, ON
P. Jetty
Affiliation:
Health Science North, Sudbury, ON
R. Atoui
Affiliation:
Health Science North, Sudbury, ON
N. Fortino
Affiliation:
Health Science North, Sudbury, ON
B. Wilson
Affiliation:
Health Science North, Sudbury, ON
N. Coffey
Affiliation:
Health Science North, Sudbury, ON
T. Scott
Affiliation:
Health Science North, Sudbury, ON
A. Cournoyer
Affiliation:
Health Science North, Sudbury, ON
F. Rubens
Affiliation:
Health Science North, Sudbury, ON
D. Savage
Affiliation:
Health Science North, Sudbury, ON
D. Ansell
Affiliation:
Health Science North, Sudbury, ON
J. Middaugh
Affiliation:
Health Science North, Sudbury, ON
A. Gupta
Affiliation:
Health Science North, Sudbury, ON
B. Bittira
Affiliation:
Health Science North, Sudbury, ON
Y. Callaway
Affiliation:
Health Science North, Sudbury, ON
S. Bignucolo
Affiliation:
Health Science North, Sudbury, ON
B. Mc Ardle
Affiliation:
Health Science North, Sudbury, ON
E. Lang
Affiliation:
Health Science North, Sudbury, ON

Abstract

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Introduction: Acute aortic syndrome (AAS) is a time sensitive aortic catastrophe that is often misdiagnosed. There are currently no Canadian guidelines to aid in diagnosis. Our goal was to adapt the existing American Heart Association (AHA) and European Society of Cardiology (ESC) diagnostic algorithms for AAS into a Canadian evidence based best practices algorithm targeted for emergency medicine physicians. Methods: We chose to adapt existing high-quality clinical practice guidelines (CPG) previously developed by the AHA/ESC using the GRADE ADOLOPMENT approach. We created a National Advisory Committee consisting of 21 members from across Canada including academic, community and remote/rural emergency physicians/nurses, cardiothoracic and cardiovascular surgeons, cardiac anesthesiologists, critical care physicians, cardiologist, radiologists and patient representatives. The Advisory Committee communicated through multiple teleconference meetings, emails and a one-day in person meeting. The panel prioritized questions and outcomes, using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to assess evidence and make recommendations. The algorithm was prepared and revised through feedback and discussions and through an iterative process until consensus was achieved. Results: The diagnostic algorithm is comprised of an updated pre test probability assessment tool with further testing recommendations based on risk level. The updated tool incorporates likelihood of an alternative diagnosis and point of care ultrasound. The final best practice diagnostic algorithm defined risk levels as Low (0.5% no further testing), Moderate (0.6-5% further testing required) and High ( >5% computed tomography, magnetic resonance imaging, trans esophageal echocardiography). During the consensus and feedback processes, we addressed a number of issues and concerns. D-dimer can be used to reduce probability of AAS in an intermediate risk group, but should not be used in a low or high-risk group. Ultrasound was incorporated as a bedside clinical examination option in pre test probability assessment for aortic insufficiency, abdominal/thoracic aortic aneurysms. Conclusion: We have created the first Canadian best practice diagnostic algorithm for AAS. We hope this diagnostic algorithm will standardize and improve diagnosis of AAS in all emergency departments across Canada.

Type
Oral Presentations
Copyright
Copyright © Canadian Association of Emergency Physicians 2019