Strain Patterns With Ultrasound for Assessment of Abdominal Aortic Aneurysm Vessel Wall Biomechanics.

Abdominal aortic aneurysms Biomechanics Strain Ultrasound

Journal

Ultrasound in medicine & biology
ISSN: 1879-291X
Titre abrégé: Ultrasound Med Biol
Pays: England
ID NLM: 0410553

Informations de publication

Date de publication:
03 Oct 2024
Historique:
received: 04 07 2024
revised: 27 08 2024
accepted: 16 09 2024
medline: 5 10 2024
pubmed: 5 10 2024
entrez: 4 10 2024
Statut: aheadofprint

Résumé

Abdominal aortic aneurysms (AAAs) are an important cause of death. Small AAAs are surveyed with ultrasound (US) until a defined diameter threshold, often triggering a computer tomography scan and surgical repair. Nevertheless, 5%-10% of AAA ruptures are below threshold, and some large AAAs never rupture. AAA wall biomechanics may reveal vessel wall degradation with potential for patient-centred risk assessment. This clinical study investigated AAA vessel wall biomechanics and deformation patterns, including reproducibility. In 50 patients with AAA, 183 video clips were recorded by two sonographers. Prototype software extracted AAA vessel wall principal strain characteristics and patterns. Functional principal component analysis (FPCA) derived strain pattern statistics. Strain patterns demonstrated reduced AAA wall strains close to the spine. The strain pattern "topography" (i.e., curve phases or "peaks" and "valleys") had a 3.9 times lower variance than simple numeric assessment of strain amplitudes, which allowed for clustering in two groups with FPCA. A high mean reproducibility of these clusters of 87.6% was found. Median pulse pressure-normalised mean principal strain (PPPS) was 0.038%/mm Hg (interquartile range: 0.029-0.051%/mm Hg) with no correlation to AAA size (Spearman's ρ = 0.02, false discovery rate-p = 0.15). Inter-operator reproducibility of PPPS was poor (limits of agreement: ±0.031%/mm Hg). Strain patterns challenge previous numeric stiffness measures based on anterior-posterior-diameter and are reproducible for clustering. This study's PPPS aligned with prior findings, although clinical reproducibility was poor. In contrast, US-based strain patterns hold promising potential to enhance AAA risk assessment beyond traditional diameter-based metrics.

Sections du résumé

BACKGROUND BACKGROUND
Abdominal aortic aneurysms (AAAs) are an important cause of death. Small AAAs are surveyed with ultrasound (US) until a defined diameter threshold, often triggering a computer tomography scan and surgical repair. Nevertheless, 5%-10% of AAA ruptures are below threshold, and some large AAAs never rupture. AAA wall biomechanics may reveal vessel wall degradation with potential for patient-centred risk assessment. This clinical study investigated AAA vessel wall biomechanics and deformation patterns, including reproducibility.
METHODS METHODS
In 50 patients with AAA, 183 video clips were recorded by two sonographers. Prototype software extracted AAA vessel wall principal strain characteristics and patterns. Functional principal component analysis (FPCA) derived strain pattern statistics.
RESULTS RESULTS
Strain patterns demonstrated reduced AAA wall strains close to the spine. The strain pattern "topography" (i.e., curve phases or "peaks" and "valleys") had a 3.9 times lower variance than simple numeric assessment of strain amplitudes, which allowed for clustering in two groups with FPCA. A high mean reproducibility of these clusters of 87.6% was found. Median pulse pressure-normalised mean principal strain (PPPS) was 0.038%/mm Hg (interquartile range: 0.029-0.051%/mm Hg) with no correlation to AAA size (Spearman's ρ = 0.02, false discovery rate-p = 0.15). Inter-operator reproducibility of PPPS was poor (limits of agreement: ±0.031%/mm Hg).
DISCUSSION CONCLUSIONS
Strain patterns challenge previous numeric stiffness measures based on anterior-posterior-diameter and are reproducible for clustering. This study's PPPS aligned with prior findings, although clinical reproducibility was poor. In contrast, US-based strain patterns hold promising potential to enhance AAA risk assessment beyond traditional diameter-based metrics.

Identifiants

pubmed: 39366791
pii: S0301-5629(24)00357-0
doi: 10.1016/j.ultrasmedbio.2024.09.014
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

Copyright © 2024 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Conflict of interest Jonas Eiberg has received a research grant, speaker honorarium and sits on an advisory board for Philips Ultrasound. Laurence Rouet is currently employed at Philips Ultrasound. Marta Bracco was employed at Philips Ultrasound. Ulver Lorenzen, Alexander Zielinski, Magdalena Broda and Stéphane Avril have no relevant conflicts of interest.

Auteurs

Ulver S Lorenzen (US)

Department of Vascular Surgery, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark. Electronic address: ulver.spangsberg.lorenzen@regionh.dk.

Marta I Bracco (MI)

Philips Health Technology Innovation, Paris, France; Centre for Biomedical and Healthcare Engineering, Soft Tissue BIOmechanics (STBio), MINES Saint-Étienne, Campus of Saint-Étienne, Saint-Priest-en-Jarez, France.

Alexander H Zielinski (AH)

Department of Vascular Surgery, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark.

Magdalena Broda (M)

Department of Vascular Surgery, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark.

Stéphane Avril (S)

Centre for Biomedical and Healthcare Engineering, Soft Tissue BIOmechanics (STBio), MINES Saint-Étienne, Campus of Saint-Étienne, Saint-Priest-en-Jarez, France.

Laurence Rouet (L)

Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.

Jonas P Eiberg (JP)

Department of Vascular Surgery, Copenhagen University Hospital-Rigshospitalet, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Copenhagen Academy for Medical Education and Simulation (CAMES), The Capital Region, Copenhagen, Denmark.

Classifications MeSH