Literature DB >> 33543131

Non-invasive measurement of fasciculation frequency demonstrates diagnostic accuracy in amyotrophic lateral sclerosis.

Arina Tamborska1, James Bashford1, Aidan Wickham2, Raquel Iniesta3, Urooba Masood1, Cristina Cabassi1, Domen Planinc1, Emma Hodson-Tole4, Emmanuel Drakakis2, Martyn Boutelle2, Kerry Mills1, Chris Shaw1.   

Abstract

Delayed diagnosis of amyotrophic lateral sclerosis prevents early entry into clinical trials at a time when neuroprotective therapies would be most effective. Fasciculations are an early hallmark of amyotrophic lateral sclerosis, preceding muscle weakness and atrophy. To assess the potential diagnostic utility of fasciculations measured by high-density surface electromyography, we carried out 30-min biceps brachii recordings in 39 patients with amyotrophic lateral sclerosis, 7 patients with benign fasciculation syndrome, 1 patient with multifocal motor neuropathy and 17 healthy individuals. We employed the surface potential quantification engine to compute fasciculation frequency, fasciculation amplitude and inter-fasciculation interval. Inter-group comparison was assessed by Welch's analysis of variance. Logistic regression, receiver operating characteristic curves and decision trees discerned the diagnostic performance of these measures. Fasciculation frequency, median fasciculation amplitude and proportion of inter-fasciculation intervals <100 ms showed significant differences between the groups. In the best-fit regression model, increasing fasciculation frequency and median fasciculation amplitude were independently associated with the diagnosis of amyotrophic lateral sclerosis. Fasciculation frequency was the single best measure predictive of the disease, with an area under the curve of 0.89 (95% confidence interval 0.81-0.98). The cut-off of more than 14 fasciculation potentials per minute achieved 80% sensitivity (95% confidence interval 63-90%) and 96% specificity (95% confidence interval 78-100%). In conclusion, non-invasive measurement of fasciculation frequency at a single time-point reliably distinguished amyotrophic lateral sclerosis from its mimicking conditions and healthy individuals, warranting further research into its diagnostic applications.
© The Author(s) (2020). Published by Oxford University Press on behalf of the Guarantors of Brain.

Entities:  

Keywords:  amyotrophic lateral sclerosis; biomarker; fasciculation frequency; fasciculation potential; surface electromyography

Year:  2020        PMID: 33543131      PMCID: PMC7850269          DOI: 10.1093/braincomms/fcaa141

Source DB:  PubMed          Journal:  Brain Commun        ISSN: 2632-1297


  24 in total

1.  Machine Learning for Supporting Diagnosis of Amyotrophic Lateral Sclerosis Using Surface Electromyogram.

Authors:  Xu Zhang; Paul E Barkhaus; William Zev Rymer; Ping Zhou
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2013-08-28       Impact factor: 3.802

Review 2.  The basics of electromyography.

Authors:  K R Mills
Journal:  J Neurol Neurosurg Psychiatry       Date:  2005-06       Impact factor: 10.154

3.  Modulation of fasciculation frequency in amyotrophic lateral sclerosis.

Authors:  Mamede de Carvalho; Antonia Turkman; Susana Pinto; Michael Swash
Journal:  J Neurol Neurosurg Psychiatry       Date:  2015-01-23       Impact factor: 10.154

Review 4.  Defining pre-symptomatic amyotrophic lateral sclerosis.

Authors:  Michael Benatar; Martin R Turner; Joanne Wuu
Journal:  Amyotroph Lateral Scler Frontotemporal Degener       Date:  2019-03-20       Impact factor: 4.092

5.  Preclinical and subclinical events in motor neuron disease.

Authors:  M Swash; D Ingram
Journal:  J Neurol Neurosurg Psychiatry       Date:  1988-02       Impact factor: 10.154

6.  Fasciculation potentials and earliest changes in motor unit physiology in ALS.

Authors:  Mamede de Carvalho; Michael Swash
Journal:  J Neurol Neurosurg Psychiatry       Date:  2013-02-16       Impact factor: 10.154

7.  Characteristics of fasciculations in amyotrophic lateral sclerosis and the benign fasciculation syndrome.

Authors:  Kerry R Mills
Journal:  Brain       Date:  2010-10-19       Impact factor: 13.501

8.  Prevalence and distribution of fasciculations in healthy adults: Effect of age, caffeine consumption and exercise.

Authors:  Jiske Fermont; Ilse M P Arts; Sebastiaan Overeem; Bert U Kleine; H Jurgen Schelhaas; Machiel J Zwarts
Journal:  Amyotroph Lateral Scler       Date:  2010

9.  Muscle ultrasonography as an additional diagnostic tool for the diagnosis of amyotrophic lateral sclerosis.

Authors:  A Grimm; T Prell; B F Décard; U Schumacher; O W Witte; H Axer; J Grosskreutz
Journal:  Clin Neurophysiol       Date:  2014-08-21       Impact factor: 3.708

10.  SPiQE: An automated analytical tool for detecting and characterising fasciculations in amyotrophic lateral sclerosis.

Authors:  J Bashford; A Wickham; R Iniesta; E Drakakis; M Boutelle; K Mills; C Shaw
Journal:  Clin Neurophysiol       Date:  2019-04-19       Impact factor: 3.708

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  1 in total

Review 1.  Neuronal Hyperexcitability and Free Radical Toxicity in Amyotrophic Lateral Sclerosis: Established and Future Targets.

Authors:  Kazumoto Shibuya; Ryo Otani; Yo-Ichi Suzuki; Satoshi Kuwabara; Matthew C Kiernan
Journal:  Pharmaceuticals (Basel)       Date:  2022-03-31
  1 in total

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