Comparison of the Framingham Risk Score, UK Prospective Diabetes Study (UKPDS) Risk Engine, Japanese Atherosclerosis Longitudinal Study-Existing Cohorts Combine (JALS-ECC) and Maximum Carotid Intima-Media Thickness for Predicting Coronary Artery Stenosis in Patients with Asymptomatic Type 2 Diabetes

抄録

Aims: To compare the efficacy of Framingham Risk Score (FRS), UK Prospective Diabetes Study (UKPDS) risk engine, a risk score based on the Japanese Atherosclerosis Longitudinal Study-Existing Cohorts Combine (JALS-ECC), the maximum intima-media thickness (max-IMT) determined on coronary computed tomography angiography (CCTA) and their combination in asymptomatic patients with type 2 diabetes.
Methods: A total of 116 Japanese patients with type 2 diabetes underwent CCTA. The risk of coronary heart disease was calculated according to the FRS, UKPDS and JALS-ECC. We evaluated the reclassification of coronary artery stenosis (CAS) based on the risk score categories after adding each IMT related variable.
Results: Sixty-eight patients had CAS. The areas under the curves (AUCs) in the receiver operating characteristic curve analyses of FRS, UKPDS and JALS-ECC were 0.763 (95% confidence interval [CI]: 0.674-0.853), 0.785 (95% CI: 0.703-0.868) and 0.767 (95% CI: 0.681-0.853), respectively. The AUCs for FRS, UKPDS and JALS-ECC combined with the max-IMT were 0.788 (95% CI: 0.705-0.872), 0.800 (95% CI: 0.720-0.879) and 0.786 (95% CI: 0.703-0.869), respectively. Combining the max-IMT with the risk scores improved the identification of subjects with stenotic lesions, in particular, those in the first, second and third tertiles of the FRS, first and second tertiles of the UKPDS and first and second tertiles of the JALS-ECC (P=0.054, P=0.056, P=0.015, P=0.082, P=0.060, P=0.007, and P=0.080, respectively). The net reclassification improvement increased following the addition of a max-IMT of ≥ 1.9 mm (32.4% in FRS, 19.9% in UKPDS and 51.7% in JALS-ECC).
Conclusions: These data suggest that combining a risk score with the max-IMT improves the prediction of CAS in comparison with the risk score alone.