"Non-invasive measurement of fasciculation frequency demonstrates diagnostic accuracy in amyotrophic lateral sclerosis". Issue 1 (7th September 2020)

• Record Type: Journal Article
• Authors: Tamborska, Arina; Bashford, James; Wickham, Aidan; Iniesta, Raquel; Masood, Urooba; Cabassi, Cristina; Planinc, Domen; Hodson-Tole, Emma; Drakakis, Emmanuel; Boutelle, Martyn; Mills, Kerry; Shaw, Chris
• Is Part Of: Brain communications. Volume 2:Issue 1(2020)
• Publication Date: 2020-09-07
• Subjects: Amyotrophic lateral sclerosis -- Surface electromyography -- Fasciculation potential -- Fasciculation Frequency -- Biomarker; 616
• DOI: 10.1093/braincomms/fcaa141
• Languages: English
• ISSNs: 2632-1297

Abstract

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 the amyotrophic lateral sclerosis, preceding muscle weakness and atrophy. To assess the potential diagnostic utility of fasciculations measured by high-density surface EMG, we carried out 30-minute biceps brachii recordings in 39 patients with amyotrophic lateral sclerosis, seven patients with benign fasciculation syndrome, one patient with multifocal motor neuropathy and 17 healthy individuals. We employed the Surface Potential Quantification Engine (SPiQE) 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 <100ms 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% CI 0.81-0.98). The cut-off of more than 14 fasciculation potentials per minute achieved 80% sensitivity (95% CI 63%-90%) and 96% specificity (95% CI 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. Graphical Abstract: