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Total coronary atherosclerotic plaque burden assessment by CT angiography for detecting obstructive coronary artery disease associated with myocardial perfusion abnormalities.
J Cardiovasc Comput Tomogr. 2016 Mar-Apr; 10(2):121-7.JC

Abstract

BACKGROUND

Total atherosclerotic plaque burden assessment by CT angiography (CTA) is a promising tool for diagnosis and prognosis of coronary artery disease (CAD) but its validation is restricted to small clinical studies. We tested the feasibility of semi-automatically derived coronary atheroma burden assessment for identifying patients with hemodynamically significant CAD in a large cohort of patients with heterogenous characteristics.

METHODS

This study focused on the CTA component of the CORE320 study population. A semi-automated contour detection algorithm quantified total coronary atheroma volume defined as the difference between vessel and lumen volume. Percent atheroma volume (PAV = [total atheroma volume/total vessel volume] × 100) was the primary metric for assessment (n = 374). The area under the receiver operating characteristic curve (AUC) determined the diagnostic accuracy for identifying patients with hemodynamically significant CAD defined as ≥50% stenosis by quantitative coronary angiography and associated myocardial perfusion abnormality by SPECT.

RESULTS

Of 374 patients, 139 (37%) had hemodynamically significant CAD. The AUC for PAV was 0.78 (95% confidence interval [CI] 0.73-0.83) compared with 0.84 [0.79-0.88] by standard expert CTA interpretation (p = 0.02). Accuracy for both CTA (0.91 [0.87, 0.96]) and PAV (0.86 [0.81-0.91]) increased after excluding patients with history of CAD (p < 0.01 for both). Bland-Altman analysis revealed good agreement between two observers (bias of 280.2 mm(3) [161.8, 398.7]).

CONCLUSIONS

A semi-automatically derived index of total coronary atheroma volume yields good accuracy for identifying patients with hemodynamically significant CAD, though marginally inferior to CTA expert reading. These results convey promise for rapid, reliable evaluation of clinically relevant CAD.

Authors+Show Affiliations

Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.Department of Medicine, Division of Cardiology, Catholic University of Paraná (PUC-PR), Brazil.Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.Department of Radiology, Keio University, Tokyo, Japan.Division of Image Processing, Department of Radiology, Leiden University Medical Center / Medis Medical Imaging Systems, Leiden, The Netherlands.Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.Beth Israel Deaconess Medical Center, Harvard University, Boston, MA, USA.Heart Institute (InCor), University of Sao Paulo Medical School, São Paulo, Brazil.Division of Cardiology, St. Luke's International Hospital, Tokyo, Japan.Division of Image Processing, Department of Radiology, Leiden University Medical Center / Medis Medical Imaging Systems, Leiden, The Netherlands.Heart Institute (InCor), University of Sao Paulo Medical School, São Paulo, Brazil.The Ottawa Hospital Research Institute and the Department of Radiology, The University of Ottawa Faculty of Medicine, Ottawa, Canada.Department of Radiology, Brigham and Women's Hospital, Harvard University, Boston, MA, USA.Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA.Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.Department of Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA. Electronic address: azadeh1@jhmi.edu.

Pub Type(s)

Comparative Study
Journal Article
Multicenter Study
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

26817414

Citation

Kishi, Satoru, et al. "Total Coronary Atherosclerotic Plaque Burden Assessment By CT Angiography for Detecting Obstructive Coronary Artery Disease Associated With Myocardial Perfusion Abnormalities." Journal of Cardiovascular Computed Tomography, vol. 10, no. 2, 2016, pp. 121-7.
Kishi S, Magalhães TA, Cerci RJ, et al. Total coronary atherosclerotic plaque burden assessment by CT angiography for detecting obstructive coronary artery disease associated with myocardial perfusion abnormalities. J Cardiovasc Comput Tomogr. 2016;10(2):121-7.
Kishi, S., Magalhães, T. A., Cerci, R. J., Matheson, M. B., Vavere, A., Tanami, Y., Kitslaar, P. H., George, R. T., Brinker, J., Miller, J. M., Clouse, M. E., Lemos, P. A., Niinuma, H., Reiber, J. H., Rochitte, C. E., Rybicki, F. J., Di Carli, M. F., Cox, C., Lima, J. A., & Arbab-Zadeh, A. (2016). Total coronary atherosclerotic plaque burden assessment by CT angiography for detecting obstructive coronary artery disease associated with myocardial perfusion abnormalities. Journal of Cardiovascular Computed Tomography, 10(2), 121-7. https://doi.org/10.1016/j.jcct.2016.01.005
Kishi S, et al. Total Coronary Atherosclerotic Plaque Burden Assessment By CT Angiography for Detecting Obstructive Coronary Artery Disease Associated With Myocardial Perfusion Abnormalities. J Cardiovasc Comput Tomogr. 2016 Mar-Apr;10(2):121-7. PubMed PMID: 26817414.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Total coronary atherosclerotic plaque burden assessment by CT angiography for detecting obstructive coronary artery disease associated with myocardial perfusion abnormalities. AU - Kishi,Satoru, AU - Magalhães,Tiago A, AU - Cerci,Rodrigo J, AU - Matheson,Matthew B, AU - Vavere,Andrea, AU - Tanami,Yutaka, AU - Kitslaar,Pieter H, AU - George,Richard T, AU - Brinker,Jeffrey, AU - Miller,Julie M, AU - Clouse,Melvin E, AU - Lemos,Pedro A, AU - Niinuma,Hiroyuki, AU - Reiber,Johan H C, AU - Rochitte,Carlos E, AU - Rybicki,Frank J, AU - Di Carli,Marcelo F, AU - Cox,Christopher, AU - Lima,Joao A C, AU - Arbab-Zadeh,Armin, Y1 - 2016/01/14/ PY - 2014/11/19/received PY - 2015/12/08/revised PY - 2016/01/11/accepted PY - 2017/03/01/pmc-release PY - 2016/1/29/entrez PY - 2016/1/29/pubmed PY - 2016/12/15/medline KW - Atherosclerotic plaque KW - CT angiography KW - Coronary artery disease KW - Coronary heart disease KW - Myocardial perfusion SP - 121 EP - 7 JF - Journal of cardiovascular computed tomography JO - J Cardiovasc Comput Tomogr VL - 10 IS - 2 N2 - BACKGROUND: Total atherosclerotic plaque burden assessment by CT angiography (CTA) is a promising tool for diagnosis and prognosis of coronary artery disease (CAD) but its validation is restricted to small clinical studies. We tested the feasibility of semi-automatically derived coronary atheroma burden assessment for identifying patients with hemodynamically significant CAD in a large cohort of patients with heterogenous characteristics. METHODS: This study focused on the CTA component of the CORE320 study population. A semi-automated contour detection algorithm quantified total coronary atheroma volume defined as the difference between vessel and lumen volume. Percent atheroma volume (PAV = [total atheroma volume/total vessel volume] × 100) was the primary metric for assessment (n = 374). The area under the receiver operating characteristic curve (AUC) determined the diagnostic accuracy for identifying patients with hemodynamically significant CAD defined as ≥50% stenosis by quantitative coronary angiography and associated myocardial perfusion abnormality by SPECT. RESULTS: Of 374 patients, 139 (37%) had hemodynamically significant CAD. The AUC for PAV was 0.78 (95% confidence interval [CI] 0.73-0.83) compared with 0.84 [0.79-0.88] by standard expert CTA interpretation (p = 0.02). Accuracy for both CTA (0.91 [0.87, 0.96]) and PAV (0.86 [0.81-0.91]) increased after excluding patients with history of CAD (p < 0.01 for both). Bland-Altman analysis revealed good agreement between two observers (bias of 280.2 mm(3) [161.8, 398.7]). CONCLUSIONS: A semi-automatically derived index of total coronary atheroma volume yields good accuracy for identifying patients with hemodynamically significant CAD, though marginally inferior to CTA expert reading. These results convey promise for rapid, reliable evaluation of clinically relevant CAD. SN - 1876-861X UR - https://www.unboundmedicine.com/medline/citation/26817414/Total_coronary_atherosclerotic_plaque_burden_assessment_by_CT_angiography_for_detecting_obstructive_coronary_artery_disease_associated_with_myocardial_perfusion_abnormalities_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1934-5925(16)30009-0 DB - PRIME DP - Unbound Medicine ER -