A Clinical Model to Identify High-Risk CAD | Journal Scan

Apr 10, 2015
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Authors:
Yang Y, Chen L, Yam Y, et al.
Citation:
A Clinical Model to Identify Patients With High-Risk Coronary Artery Disease. JACC Cardiovasc Imaging 2015;Mar 18:[Epub ahead of print].
Summary By:
Elizabeth A. Jackson, MD, FACC

Study Questions:

Can a clinical model be developed to identify patients with high-risk coronary artery disease (CAD)?

Methods:

This was a retrospective study using data collected prospectively from a multinational coronary computed tomography (CT) angiography cohort. High-risk CAD was defined as left main coronary artery diameter stenosis >50%, three-vessel disease with a diameter stenosis of >70%, or two-vessel disease involving the proximally left anterior descending artery. Participants with a history of CAD, cardiac transplantation, and congenital heart disease were excluded.

Results:

A total of 24,251 patients were used to create the model, and an additional 7,333 participants were used to validate the model. The risk score included the following variables: age, sex, diabetes, hypertension, current smoking, hyperlipidemia, family history of CAD, history of peripheral vascular disease, and chest pain symptoms. Participants were grouped by number of points (from the risk score) including low risk (≤7 points), intermediate risk (8-17 points), and high risk (≥18 points). The model appeared robust, with an area under the curve of 0.76 (95% confidence interval, 0.75-0.78) in the derivation cohort and 0.71 (95% CI, 0.69-0.74) in the validation cohort. Among the low-risk group (≤7 points), the prevalence of high-risk CAD was 1% compared with 16.7% in the high-risk group (≥18 points).

Conclusions:

The investigators concluded that a scoring system using clinically derived variables can be used to identify patients at high risk for CAD who may have low pretest probably based on features used in prior studies.

Perspective:

This model uses clinical variables readily available for most clinicians. Further research using this score to identify those who may benefit the most from preventive therapies is warranted.

Keywords: Angiography, Chest Pain, Coronary Artery Disease, Coronary Disease, Coronary Stenosis, Diabetes Mellitus, Hyperlipidemias, Hypertension, Peripheral Vascular Diseases, Retrospective Studies, Risk, Risk Assessment, Smoking, Tomography, Secondary Prevention


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