A novel quantitative index of coronary artery motion from multislice computed tomography and the location of coronary artery disease

  • Aiden J. C. O'Loughlin
  • , Linda Tang
  • , Daniel Moses
  • , Wisdom Tong DiagRad
  • , John K. French
  • , David A. B. Richards
  • , A. Robert Denniss
  • , Annemarie Hennessy

Research output: Contribution to journalArticlepeer-review

Abstract

Background: We describe a novel quantitative index of coronary artery motion (QCAM) from multislice computed tomography (MSCT) and test its association with the location of coronary artery disease. Methods: 25 patients with known or suspected coronary artery disease underwent ECG-gated MSCT. The coronary artery images were divided into 150 sections using landmarks that could be identified at time points at end-diastole and end-systole. QCAM was derived from the change in centerline length of the coronary sections between these time points. Plaques were identified and classified by type and severity of stenosis. Results: The mean QCAM was significantly less in the coronary sections with plaque (94.3%+/-8.1%) than those without (99.0%+/-10.2%) (p=0.023). There was a significant correlation between QCAM and plaque stenosis (Spearman’s rank correlation coefficient, ρ= -0.192, p=0.018). The correlation between QCAM and plaque type approached statistical significance (Spearman’s rank correlation coefficient, ρ= -0.156, p=0.057). Sensitivity, specificity, positive and negative predictive values for the identification of coronary plaque within a section for QCAM <100% were 80%, 46%, 27% and 90% respectively. Conclusions: QCAM is a novel quantitative measurement of coronary artery motion that correlates with the location of coronary artery disease. Quantitative evaluation of coronary artery motion provides a new approach to understanding the biomechanics of coronary artery disease.
Original languageEnglish
Pages (from-to)1-5
Number of pages5
JournalInternational Journal of Cardiovascular and Cerebrovascular Disease
Volume2
Issue number1
DOIs
Publication statusPublished - 2014

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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