Objective: We investigated the efficacy of coronary computed tomography angiography (CCTA) in predicting the long-term risks in asymptomatic patients with type 2 diabetes and compared it with traditional risk factors.
Research design and methods: We analyzed 933 patients with asymptomatic type 2 diabetes who underwent CCTA. Stenosis was considered obstructive (≥50%) in each coronary artery segment using CCTA. The extent and severity scores for coronary artery disease (CAD) were evaluated. The primary end point was major adverse cardiovascular events (MACE), including all-cause mortality, nonfatal myocardial infarction, and late coronary revascularization during a mean follow-up period of 5.5 ± 2.1 years.
Results: Ninety-four patients with MACE exhibited obstructive CAD with a greater extent and higher severity scores (P < 0.001 for all). After adjusting for confounding risk factors, obstructive CAD remained an independent predictor of MACE (hazard ratio 3.11 [95% CI 2.00-4.86]; P < 0.001]). The performance of a risk prediction model based on C-statistics was significantly improved (C-index 0.788 [95% CI 0.747-0.829]; P = 0.0349) upon the addition of a finding of obstructive CAD using CCTA to traditional risk factors, including age, male, hypertension, hyperlipidemia, smoking, estimated glomerular filtration rate, and HbA1c. Both integrated discrimination improvement (IDI) and net reclassification improvement (NRI) analyses further supported this finding (IDI 0.046 [95% CI 0.020-0.072], P < 0.001, and NRI 0.55 [95% CI 0.343-0.757], P < 0.001). In contrast, the risk prediction power of the coronary artery calcium score remained unimproved (C-index 0.740, P = 0.547).
Conclusions: Based on our data, the addition of CCTA-detected obstructive CAD to models that include traditional risk factors improves the predictions of MACE in asymptomatic patients with type 2 diabetes.
© 2017 by the American Diabetes Association.