Literature DB >> 29793847

Improvement in LDL is associated with decrease in non-calcified plaque volume on coronary CTA as measured by automated quantitative software.

Balaji Tamarappoo1, Yuka Otaki2, Mhairi Doris2, Yoav Arnson2, Heidi Gransar2, Sean Hayes2, John Friedman2, Louise Thomson2, Frances Wang2, Alan Rozanski2, Piotr Slomka3, Damini Dey3, Daniel Berman4.   

Abstract

BACKGROUND: Computed tomography coronary angiography (CTA) can be used for assessment of plaque characteristics; however, quantitative assessment of changes in plaque composition in response to LDL lowering has not been performed with CTA. We sought to assess the association between LDL reduction and changes in plaque composition with quantitative CTA.
METHODS: Quantification of total, calcified, non-calcified and low-density non-calcified plaque volumes (TPV, CPV, NCPV and LD-NCPV) was performed using semi-automated software in 234 vessels from 116 consecutive patients (89 men, 60 ± 10 years) with baseline LDL>70 mg/dl. Significant reduction in LDL was defined as a decrease by >10% of baseline LDL. Changes (Δ) in plaque volumes between the second and baseline study were compared between patients with LDL reduction (n = 63) and those with no decrease in LDL (n = 53).
RESULTS: Median LDL at baseline was 98 mg/dl [interquartile range (IQR) 83-119 mg/dl] and median ΔLDL was -14 mg/dl (IQR -38 to 3 mg/dl). Mean interval between sequential CTA was 3.5 ± 1.6 years. TPV, NCPV, and LD-NCPV decreased in patients with a reduction in LDL compared to baseline; whereas, patients without reduction in LDL experienced an increase in TPV, NCPV and LD-NCPV. After adjusting for age, statin use, diabetes, baseline LDL and baseline TPV, reduction in LDL was associated with a decrease in TPV (P = 0.005), NCPV (P = 0.002) and LD-NCPV (P = 0.011) compared to patients without a reduction in LDL.
CONCLUSION: Reduction in LDL was associated with beneficial changes in the amount and composition of noncalcified plaque as measured using semi-automated quantitative software by CTA.
Copyright © 2018 Society of Cardiovascular Computed Tomography. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Atherosclerosis; Computerized tomography; Imaging; Lipids; cholesterol

Mesh:

Substances:

Year:  2018        PMID: 29793847     DOI: 10.1016/j.jcct.2018.05.004

Source DB:  PubMed          Journal:  J Cardiovasc Comput Tomogr        ISSN: 1876-861X


  6 in total

Review 1.  Emerging Role of Coronary Computed Tomography Angiography in Lipid-Lowering Therapy: a Bridge to Image-Guided Personalized Medicine.

Authors:  Toru Miyoshi; Kazuhiro Osawa; Keishi Ichikawa; Kazuki Suruga; Takashi Miki; Masashi Yoshida; Koji Nakagawa; Hironobu Toda; Kazufumi Nakamura; Hiroshi Morita; Hiroshi Ito
Journal:  Curr Cardiol Rep       Date:  2019-06-21       Impact factor: 2.931

2.  Relationship between changes in pericoronary adipose tissue attenuation and coronary plaque burden quantified from coronary computed tomography angiography.

Authors:  Markus Goeller; Balaji K Tamarappoo; Alan C Kwan; Sebastien Cadet; Frederic Commandeur; Aryabod Razipour; Piotr J Slomka; Heidi Gransar; Xi Chen; Yuka Otaki; John D Friedman; J Jane Cao; Moritz H Albrecht; Daniel O Bittner; Mohamed Marwan; Stephan Achenbach; Daniel S Berman; Damini Dey
Journal:  Eur Heart J Cardiovasc Imaging       Date:  2019-06-01       Impact factor: 6.875

3.  Improved Evaluation of Lipid-Rich Plaque at Coronary CT Angiography: Head-to-Head Comparison with Intravascular US.

Authors:  Hidenari Matsumoto; Satoshi Watanabe; Eisho Kyo; Takafumi Tsuji; Yosuke Ando; Evann Eisenberg; Yuka Otaki; Osamu Manabe; Sebastien Cadet; Piotr J Slomka; Balaji K Tamarappoo; Daniel S Berman; Damini Dey
Journal:  Radiol Cardiothorac Imaging       Date:  2019-12-19

4.  Association of Cardiovascular Disease Risk Factor Burden With Progression of Coronary Atherosclerosis Assessed by Serial Coronary Computed Tomographic Angiography.

Authors:  Donghee Han; Daniel S Berman; Robert J H Miller; Daniele Andreini; Matthew J Budoff; Filippo Cademartiri; Kavitha Chinnaiyan; Jung Hyun Choi; Edoardo Conte; Hugo Marques; Pedro de Araújo Gonçalves; Ilan Gottlieb; Martin Hadamitzky; Jonathon Leipsic; Erica Maffei; Gianluca Pontone; Sangshoon Shin; Yong-Jin Kim; Byoung Kwon Lee; Eun Ju Chun; Ji Min Sung; Sang-Eun Lee; Renu Virmani; Habib Samady; Peter Stone; Jagat Narula; Jeroen J Bax; Leslee J Shaw; Fay Y Lin; James K Min; Hyuk-Jae Chang
Journal:  JAMA Netw Open       Date:  2020-07-01

5.  Fully Automated CT Quantification of Epicardial Adipose Tissue by Deep Learning: A Multicenter Study.

Authors:  Frederic Commandeur; Markus Goeller; Aryabod Razipour; Sebastien Cadet; Michaela M Hell; Jacek Kwiecinski; Xi Chen; Hyuk-Jae Chang; Mohamed Marwan; Stephan Achenbach; Daniel S Berman; Piotr J Slomka; Balaji K Tamarappoo; Damini Dey
Journal:  Radiol Artif Intell       Date:  2019-11-27

6.  Cardiac CT: Technological Advances in Hardware, Software, and Machine Learning Applications.

Authors:  Frederic Commandeur; Markus Goeller; Damini Dey
Journal:  Curr Cardiovasc Imaging Rep       Date:  2018-06-29
  6 in total

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