A predictive model for screening cerebrovascular disease in patient undergoing coronary artery bypass grafting.
OBJECTIVE
The occurrence of stroke during coronary artery bypass grafting (CABG) is multifactorial but the coexistence of carotid disease is considered as one of the avoidable sources. Beyond the perioperative management, the detection of carotid disease in a coronary patient could be of prognostic significance. A systematic screening for all candidates for CABG could, however, be a non-efficient strategy. We aimed to optimize the Duplex screening of candidates for CABG by studying risk factors of significant concomitant carotid lesions.


METHODS
We prospectively studied 1043 consecutive candidates for CABG by Duplex scanning. A first subgroup of 825 patients permitted to establish the predictive model of >50% stenosis. A multivariate analysis provided independent predictive factors. The ability of the model to predict >50% and >70% stenosis of neck arteries has been prospectively assessed on the 218 consecutive patients.


RESULTS
In the first group, 108 (13.1%) and 58 (7%) had respectively at least a >50% and >70% stenosis. The independent risk factors were: past history of stroke or transient ischemic attack, neck bruit, clinically apparent peripheral arterial disease (PAD) or subclinical PAD (ABI <0.85 or >1.5), and age >70 years (P<0.05). Among the subsequent 218 patients, the presence of at least one of these factors was able to detect 24 out of 26 patients (92.3%) with a >50% stenosis, and 100% of those with >70% stenosis, and could rule out 41% from a systematic Duplex screening.


CONCLUSIONS
The excellent sensitivity of this risk assessment approach, makes an efficient screening of cerebrovascular disease possible in CABG patients.
Department of Thoracic & Cardiovascular Surgery and Vascular Medicine, Dupuytren University Hospital, Limoges, France. aboyans@unilim.fr
Victor
V
Aboyans
Philippe
P
Lacroix
Jérôme
J
Guilloux
Florence
F
Rollé
Alexandre
A
Le Guyader
Michel
M
Cautrès
Elisabeth
E
Cornu
Marc
M
Laskar