Tensor electrical impedance myography identifies bulbar disease progression in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis Biomarker Bulbar Disease progression Electrical impedance myography Tongue

Journal

Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
ISSN: 1872-8952
Titre abrégé: Clin Neurophysiol
Pays: Netherlands
ID NLM: 100883319

Informations de publication

Date de publication:
07 2022
Historique:
received: 14 02 2022
revised: 08 04 2022
accepted: 15 04 2022
pubmed: 16 5 2022
medline: 16 6 2022
entrez: 15 5 2022
Statut: ppublish

Résumé

Electrical impedance myography (EIM) is a promising biomarker for amyotrophic lateral sclerosis (ALS). A key issue is how best to utilise the complex high dimensional, multi-frequency data output by EIM to fully characterise the progression of disease. Muscle volume conduction properties were obtained from EIM recordings of the tongue across three electrode configurations and 14 input frequencies (76 Hz-625 kHz). Analyses of individual frequencies, averaged EIM spectra and non-negative tensor factorisation were undertaken. Longitudinal data were collected from 28 patients and 17 healthy volunteers at 3-monthly intervals for a maximum of 9 months. EIM was evaluated against the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar sub-score, tongue strength and an overall bulbar disease burden score. Longitudinal changes to individual patient EIM spectra demonstrated complex shifts in the spectral shape. At a group level, a clear pattern emerged over time, characterised by an increase in centre frequency and general shift to the right of the spectral shape. Tensor factorisation reduced the spectral data from a total of 168 data points per participant per recording to a single value which captured the complexity of the longitudinal data and which we call tensor EIM (T-EIM). The absolute change in tensor EIM significantly increased within 3 months and continued to do so over the 9-month study duration. In a hypothetical clinical trial scenario tensor EIM required fewer participants (n = 64 at 50% treatment effect), than single frequency measures (n range 87-802) or ALSFRS-R bulbar subscore (n = 298). Changes to tongue EIM spectra over time in ALS are complex. Tensor EIM captured and quantified disease progression and was more sensitive to changes than single frequency EIM measures and other biomarkers of bulbar disease. Objective biomarkers for the assessment of bulbar disease in ALS are lacking. Tensor EIM enhances the biomarker potential of EIM data and can improve bulbar symptom monitoring in clinical trials.

Identifiants

pubmed: 35569295
pii: S1388-2457(22)00256-5
doi: 10.1016/j.clinph.2022.04.015
pii:
doi:

Substances chimiques

Biomarkers 0

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

69-75

Subventions

Organisme : Department of Health
ID : IS-BRC-1215-20017
Pays : United Kingdom
Organisme : Department of Health
ID : NF-SI-0617-10077
Pays : United Kingdom

Informations de copyright

Crown Copyright © 2022. Published by Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Chlöe N Schooling (CN)

Sheffield Institute for Translational Neuroscience, University of Sheffield, UK; Department of Automatic Control and Systems Engineering, University of Sheffield, UK. Electronic address: cnschooling1@sheffield.ac.uk.

T Jamie Healey (TJ)

Department of Clinical Engineering, Sheffield Teaching Hospitals NHS Foundation Trust, UK.

Harry E McDonough (HE)

Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.

Sophie J French (SJ)

Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.

Christopher J McDermott (CJ)

Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.

Pamela J Shaw (PJ)

Sheffield Institute for Translational Neuroscience, University of Sheffield, UK.

Visakan Kadirkamanathan (V)

Department of Automatic Control and Systems Engineering, University of Sheffield, UK.

James J P Alix (JJP)

Sheffield Institute for Translational Neuroscience, University of Sheffield, UK. Electronic address: j.alix@sheffield.ac.uk.

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