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CT measured abdominal fat correlated parameters for diagnosis of coronary artery disease / 中国介入影像与治疗学
Article in Chinese | WPRIM (Western Pacific) | ID: wpr-861900
Responsible library: WPRO
ABSTRACT

Objective:

To observe the value of CT measured abdominal fat correlated parameters for diagnosis of coronary artery disease (CAD).

Methods:

Totally 211 patients with suspected CAD who underwent coronary angiography and abdominal CT plain scan within 30 days were retrospectively analyzed. The risk factors of CAD were analyzed. ROC curve was used to observe the diagnostic efficacy of each risk factor alone and their combination for CAD.

Results:

Among 211 cases, CAD was definitely diagnosed with coronary angiography in 112 cases, while 99 cases were found non-CAD. The single factor analysis showed that age, gender, smoking, diabetes mellitus, hypertension, high density lipoprotein-cholesterol (HDL-C), visceral adipose tissue (VAT) area, subcutaneous adipose tissue (SAT) area and VAT area/SAT area (VAT/SAT) were significantly different between CAD and non-CAD patients (all P0.05). Multivariable Logistic regression analysis demonstrated that age, smoking, diabetes mellitus and VAT/SAT were independent risks of CAD (all P<0.01). ROC curves showed the AUC of diagnosing CAD in age, smoking, diabetes mellitus and VAT/SAT was 0.67, 0.61, 0.62 and 0.73, respectively. The AUC of the combination of the above four risk factors was 0.80, higher than that of any single risk factor (all P<0.05).

Conclusion:

CT measured abdominal fat correlated parameters could be used for diagnosing CAD.

Full text: Available Database: WPRIM (Western Pacific) Type of study: Diagnostic study / Prognostic study / Risk factors Aspects: Patient-preference Language: Chinese Journal: Chinese Journal of Interventional Imaging and Therapy Year: 2020 Document type: Article
Full text: Available Database: WPRIM (Western Pacific) Type of study: Diagnostic study / Prognostic study / Risk factors Aspects: Patient-preference Language: Chinese Journal: Chinese Journal of Interventional Imaging and Therapy Year: 2020 Document type: Article
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