Clinical prediction of acute aortic dissection.
BACKGROUND
Clinical criteria for aortic dissection are poorly defined. Thus, 35% of aortic dissections remain unsuspected in vivo, and 99% of suspected cases can be refuted.


OBJECTIVE
To identify independent predictors of acute aortic dissection and create a prediction model for facilitated estimation of the individual risk of dissection.


METHODS
Two hundred fifty patients with acute chest pain, back pain, or both; absence of an established differential diagnosis of the pain syndrome; and clinical suspicion of acute aortic dissection were evaluated for the presence of 26 clinical variables in a prospective, observational study. Multivariate analysis was performed to create a prediction model of aortic dissection.


RESULTS
Aortic pain with immediate onset, a tearing or ripping character, or both; mediastinal widening, aortic widening, or both on chest radiography; and pulse differentials, blood pressure differentials, or both (P<.001 for all) were identified as independent predictors of acute aortic dissection. Probability of dissection was low with absence of all 3 variables (7%), intermediate with isolated findings of aortic pain or mediastinal widening (31% and 39%, respectively), and high with isolated pulse or blood pressure differentials or any combination of the 3 variables (> or = 83%). Accordingly, 4% of all dissections were assigned to the low-probability group, 19% to the intermediate-probability group, and 77% to the high-probability group of aortic dissection.


CONCLUSIONS
Assessment of 3 clinical variables permitted identification of 96% of the acute aortic dissections and stratification into high-, intermediate-, and low-probability groupings of disease. With better selection for prompt diagnostic imaging, this prediction model can be used as an aid to improve patient care in aortic dissection. Arch Intern Med. 2000;160:2977-2982
von Kodolitsch
Y
Y
Department of Cardiology, University Hospital Eppendorf, Hamburg, Germany.
Schwartz
A G
AG
Nienaber
C A
CA
D000368:Aged; D000465:Algorithms; D000784:Aortic Dissection / Q000175:diagnosis*; D001014:Aortic Aneurysm / Q000175:diagnosis*; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D011446:Prospective Studies; D018570:Risk Assessment