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Point-of-Care Ultrasound: A Review of Ultrasound Parameters for Predicting Difficult Airways
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Predicting obstructive coronary artery disease using carotid ultrasound parameters: A nomogram from a large

Na Wu1,2, Xinghua Chen3, Mingyang Li3

  • 1Department of Epidemiology, College of Preventive Medicine, Army Medical University (Third Military Medical University), Chongqing, China.

European Journal of Clinical Investigation
|May 22, 2018
PubMed

Insights

The total number of carotid plaques, along with plaque characteristics, significantly improves coronary artery disease (CAD) prediction beyond clinical risk factors. A nomogram using these factors offers a practical CAD detection method.

Area of Science:

  • Cardiovascular Imaging
  • Atherosclerosis Research
  • Predictive Modeling in Cardiology

Background:

  • Carotid ultrasound is a noninvasive tool for assessing coronary artery disease (CAD) risk.
  • Current consensus lacks a definitive carotid ultrasound parameter for measuring atherosclerosis.
  • This study aimed to identify the optimal carotid ultrasound parameters and clinical risk factors (CRF) for predicting CAD.

Purpose of the Study:

  • To determine which carotid ultrasound parameters, alone or in combination with clinical risk factors (CRF), best predict coronary artery disease (CAD).
  • To evaluate the incremental predictive value of carotid intima-media thickness (CIMT), plaque count, and plaque echogenicity for CAD.
  • To develop a predictive model and nomogram for CAD detection based on optimal carotid ultrasound parameters and CRF.

Main Methods:

  • 2431 patients with suspected CAD underwent coronary angiography and carotid ultrasound.
  • Carotid ultrasound measurements included carotid intima-media thickness (CIMT), total plaque count, and plaque echogenicity.
  • Statistical analysis focused on the incremental predictive ability of ultrasound parameters over CRF using area under the curve (AUC) and net reclassification index (NRI).

Main Results:

  • The total number of carotid plaques showed the highest incremental predictive ability for CAD over CRF (AUC 0.752 vs 0.701).
  • Areas of maximum soft and hard plaques also demonstrated significant incremental value.
  • Carotid intima-media thickness (CIMT) did not provide significant incremental prediction over CRF.
  • A combined model including plaque count, plaque characteristics, and CRF achieved the highest discriminatory and reclassification value (AUC 0.757).

Conclusions:

  • The total number of carotid plaques and their characteristics (soft, hard, mixed) significantly enhance CAD prediction beyond clinical risk factors.
  • A nomogram integrating these factors provides an intuitive and practical tool for CAD detection.
  • For low to intermediate risk patients, total plaque count combined with CRF emerged as the optimal predictive model.
Abstract

Keywords:
carotid plaquecarotid ultrasoundcoronary artery diseasenomogram

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