THURSDAY, Oct. 28 (HealthDay News) -- A prediction model that uses computed tomography (CT) findings of ancillary aortic abnormalities may help physicians identify individuals at high risk for cardiovascular disease (CVD), according to research published in the November issue of Radiology.
Martijn J.A. Gondrie, M.D., of the University Medical Center Utrecht in the Netherlands, and colleagues assigned visual scores for ancillary aortic abnormalities to a representative population of 817 patients who had undergone diagnostic chest CT for non-cardiovascular indications, and 347 patients who experienced a cardiovascular event, to determine whether they could predict CVD through use of prevalent subclinical ancillary aortic findings on CT imaging.
The researchers assigned visual scores to the aortic abnormalities (zero to eight for calcifications, zero to four for plaques, zero to four for irregularities, and zero to one for elongation) and compared four models that incorporated scores for the abnormalities plus other factors. They found that each abnormality was highly predictive and that each model performed well, particularly the one that incorporated the sum score for aortic calcifications, which they chose to validate externally. It also performed well in external validation.
"A derived prediction model incorporating ancillary aortic findings detected on routine diagnostic CT images complements established risk scores and may help to identify patients at high risk for CVD. Timely application of preventative measures may ultimately reduce the number or severity of CVD events," the authors write.