Endocrinology and Metabolism

June Hyung Kim (Kim JH)

1 Article

Diabetes
High Brachial Ankle Pulse Wave Velocity as a Marker for Predicting Coronary Artery Stenosis in Patients with Type 2 Diabetes: Bo Hyun Kim, Jae Sik Jang, Yong Seop Kwon, June Hyung Kim, In Joo Kim, Chang Won Lee; Endocrinol Metab. 2018;33(1):88-96. Published online March 21, 2018; DOI: https://doi.org/10.3803/EnM.2018.33.1.88

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Background

We evaluated the ability of brachial ankle pulse wave velocity (baPWV) to predict coronary artery stenosis (CAS) in patients with type 2 diabetes, and compared the predictive power of baPWV to that of well-known cardiovascular disease (CVD) risk calculators.

Methods

The study group included 83 consecutive patients over 30 years old with type 2 diabetes who complained of vague chest discomfort. An automatic pulse waveform analyzer was used to measure baPWV. CAS was measured using multi-slice computed tomographic (MSCT) angiography.

Results

Age, maximal baPWV, duration of diabetes, current smoking, the UK Prospective Diabetes Study (UKPDS) Risk Engine score, American College of Cardiology/American Heart Association (ACC/AHA) risk estimator score, the Framingham risk calculator score, and coronary artery calcium score were greater in patients with CAS than in those without CAS. An area under the curve (AUC) indicative of a predictive value for CAS (≥20%) was found for several parameters. The AUC of maximal baPWV, the UKPDS Risk Engine, the ACC/AHA ASCVD risk estimator, and the Framingham risk calculator were 0.672 (95% confidence interval [CI], 0.554 to 0.785; P=0.010), 0.777 (95% CI, 0.675 to 0.878; P<0.001), 0.763 (95% CI, 0.660 to 0.866; P<0.001), and 0.736 (95% CI, 0.629 to 0.843; P<0.001), respectively. The optimal cutoff value of baPWV for the detection of CAS was 1,650 cm/sec (sensitivity, 68.9%; specificity, 63.2%).

Conclusion

Maximal baPWV was closely related with CAS detected by MSCT coronary angiography in patients with type 2 diabetes. baPWV has the potential to be a useful, noninvasive screening tool for the prediction of occult CAS in patients with type 2 diabetes.

Citations

Citations to this article as recorded by

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