In a prediction model, researchers found that adding carotid plaque measurements, rather than carotid intima-media thickness (CIMT), to traditional risk factors better predicted coronary artery disease (CAD) in women, while men benefited from adding CIMT to traditional risk factors, according to a study published in the April 13 issue of the Journal of the American College of Cardiology.
“Although CAD risk prediction models based on traditional risk factors have formed the basis for the clinical practice of CAD prevention, they are far from optimal,” wrote Vijat Nambi, MD, of the Baylor College of Medicine in Houston, and colleagues from various institutions.
Researchers used data from the Atherosclerosis Risk In Communities (ARIC) epidemiological study, which evaluated 13,145 patients (7,463 women) without relevant indications of stroke or CAD. Eleven measurements were taken of the far carotid wall for three segments: the distal common carotid, carotid artery bifurcation and the proximal internal carotid arteries.
In women, the area under the curve (AUC) increased from 0.759 (traditional risk factors alone) to 0.762 when CIMT was added, whereas the AUC increased to 0.770 when plaque alone was added. In addition, a model that included all three metrics (traditional risk factors, CIMT and plaque) was associated with a similar AUC of 0.770.
Conversely, for men, the AUC increased from 0.674 (traditional risk factors alone) to 0.690 when CIMT was added, while the AUC increased only to 0.686 when plaque alone was added. In a model that included all three metrics, the AUC increased the most, reaching 0.694.
“Plaque presence seemed to have a more profound effect on improving risk prediction in women than in men, and it is not completely clear why,” the authors noted.
One theory, they postulated, could be that since middle-aged women have a relatively low presence of atherosclerosis, plaque presence, which reflects a definite area of atherosclerosis, was more powerful than using a sex-specific percentile “thickness” (CIMT).
“Similarly, given the overall lower prevalence of atherosclerosis in women, it is possible that a CIMT greater than the 75th percentile misclassifies subjects without atherosclerosis as higher risk, and a specific CIMT cutpoint may be better in women,” they wrote.
Researchers concluded that adding plaque and CIMT data was the best model for intermediate risk groups, for both men and women.
The 25th and 75th percentiles of CIMT were 0.65 mm and 0.84 mm, respectively, for men and 0.58 mm and 0.74 mm, respectively, for women. Nambi et al found that plaque presence increased from 13 percent in the overall population with a CIMT under the 25th percentile to 26 percent between the 25th to 75th percentile and to 65 percent in those with a CIMT higher than the 75th percentile.
Subjects were placed into 10-year CAD risk: 0 to 5 percent risk (low), 5 to 10 percent (low-intermediate), 10 to 20 percent (intermediate-high) and greater than 20 percent (highest). In the risk groups, plaque increased by 24 percent in the 0 to 5 percent group, 34 percent in the 5 to 10 percent group, 46 percent in the 10 to 20 percent group and 54 percent in the 20 percent or greater group.
Reclassification rates when plaque and CIMT were added to traditional risk factors were:
- 8 percent in the less than 5 percent group
- 37 percent in the 5 to 10 percent group
- 38 percent in the 10 to 20 percent group, and
- 21 percent in the greater than 20 percent group.
For men in these same risk groups, reclassification rates after adding plaque and CIMT were 17, 32, 36 and 25 percent, respectively, and for women: 5, 40, 38 and 24 percent, respectively.
During the study no subjects were reclassified from a low-risk group to a high-risk group; however, those in the intermediate group showed high rates of being reclassified into the lower risk group—62 percent.
“In the future, further improvement in our ability to stratify CAD risk may be possible through reliable quantification of plaque volume, as the mere presence of plaque without any quantification helped improve overall CAD risk prediction in our analysis,” the authors concluded.
In an accompanying editorial, James H. Stein, MD, and Heather M. Johnson, MD, from the University of Wisconsin School of Medicine in Madison, wrote, “Demonstrating the utility of these tests is more than an issue of mathematics and requires more than elegant statistical analyses—it