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Sex and regional differences in myocardial plasticity in aortic stenosis are revealed by 3D model machine learning.

Authors
  • Bhuva, Anish N1, 2
  • Treibel, Thomas A1, 2
  • De Marvao, Antonio3
  • Biffi, Carlo3, 4
  • Dawes, Timothy J W3, 5
  • Doumou, Georgia3
  • Bai, Wenjia4
  • Patel, Kush1, 2
  • Boubertakh, Redha2
  • Rueckert, Daniel4
  • O'Regan, Declan P3
  • Hughes, Alun D1
  • Moon, James C1, 2
  • Manisty, Charlotte H1, 2
  • 1 Institute for Cardiovascular Science, University College London, Chenies Mews, London WC1E6HX, UK.
  • 2 Department of Cardiovascular Imaging, Barts Heart Centre, Barts Health NHS Trust, King George V Building, West Smithfield, London EC1A 7BE, UK.
  • 3 MRC London Institute of Medical Sciences, Imperial College London, Hammersmith Hospital Campus, Du Cane Road, London W120NN, UK.
  • 4 Department of Computing, Imperial College London, South Kensington Campus, 180 Queen's Gate, London SW72RH, UK.
  • 5 National Heart and Lung Institute, Imperial College London, Du Cane Road, London W120NN, UK.
Type
Published Article
Journal
European Heart Journal - Cardiovascular Imaging
Publisher
Oxford University Press
Publication Date
Apr 01, 2020
Volume
21
Issue
4
Pages
417–427
Identifiers
DOI: 10.1093/ehjci/jez166
PMID: 31280289
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

Left ventricular hypertrophy (LVH) in aortic stenosis (AS) varies widely before and after aortic valve replacement (AVR), and deeper phenotyping beyond traditional global measures may improve risk stratification. We hypothesized that machine learning derived 3D LV models may provide a more sensitive assessment of remodelling and sex-related differences in AS than conventional measurements. One hundred and sixteen patients with severe, symptomatic AS (54% male, 70 ± 10 years) underwent cardiovascular magnetic resonance pre-AVR and 1 year post-AVR. Computational analysis produced co-registered 3D models of wall thickness, which were compared with 40 propensity-matched healthy controls. Preoperative regional wall thickness and post-operative percentage wall thickness regression were analysed, stratified by sex. AS hypertrophy and regression post-AVR was non-uniform-greatest in the septum with more pronounced changes in males than females (wall thickness regression: -13 ± 3.6 vs. -6 ± 1.9%, respectively, P < 0.05). Even patients without LVH (16% with normal indexed LV mass, 79% female) had greater septal and inferior wall thickness compared with controls (8.8 ± 1.6 vs. 6.6 ± 1.2 mm, P < 0.05), which regressed post-AVR. These differences were not detectable by global measures of remodelling. Changes to clinical parameters post-AVR were also greater in males: N-terminal pro-brain natriuretic peptide (NT-proBNP) [-37 (interquartile range -88 to -2) vs. -1 (-24 to 11) ng/L, P = 0.008], and systolic blood pressure (12.9 ± 23 vs. 2.1 ± 17 mmHg, P = 0.009), with changes in NT-proBNP correlating with percentage LV mass regression in males only (ß 0.32, P = 0.02). In patients with severe AS, including those without overt LVH, LV remodelling is most plastic in the septum, and greater in males, both pre-AVR and post-AVR. Three-dimensional machine learning is more sensitive than conventional analysis to these changes, potentially enhancing risk stratification. Regression of myocardial fibrosis after aortic valve replacement (RELIEF-AS); NCT02174471. https://clinicaltrials.gov/ct2/show/NCT02174471. Published on behalf of the European Society of Cardiology. All rights reserved. © The Author(s) 2019. For permissions, please email: [email protected]

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