Affiliation:
1. Department of Neurology, Brain Center Utrecht University Medical Center Utrecht Utrecht The Netherlands
2. Department of Medical Technology and Clinical Physics University Medical Center Utrecht Utrecht The Netherlands
Abstract
AbstractBackground and purposeThe lack of reliable early biomarkers still causes substantial diagnostic delays in amyotrophic lateral sclerosis (ALS). The aim was to assess the diagnostic accuracy of a novel electrophysiological protocol in patients with suspected motor neuron disease (MND).MethodsConsecutive patients with suspected MND were prospectively recruited at our tertiary referral centre for MND in Utrecht, The Netherlands. Procedures were performed in accordance with the Standards for Reporting of Diagnostic Accuracy. In addition to the standard diagnostic workup, an electrophysiological protocol of compound muscle action potential (CMAP) scans and nerve excitability tests was performed on patients' thenar muscles. The combined diagnostic yield of nerve excitability and CMAP scan based motor unit number estimation was compared to the Awaji and Gold Coast criteria and their added value was determined.ResultsIn all, 153 ALS or progressive muscular atrophy patients, 63 disease controls and 43 healthy controls were included. Our electrophysiological protocol had high diagnostic accuracy (area under the curve [AUC] 0.85, 95% confidence interval [95% CI] 0.80–0.90), even in muscles with undetectable axon loss (AUC 0.78, 95% CI 0.70–0.85) and in bulbar‐onset patients (AUC 0.85, 95% CI 0.73–0.95). Twenty‐four of 33 (73%) ALS patients who could not be diagnosed during the same visit were correctly identified, as well as 8/13 (62%) ALS patients not meeting the Gold Coast criteria and 49/59 (83%) ALS patients not meeting the Awaji criteria during this first visit.ConclusionsOur practical and non‐invasive electrophysiological protocol may improve early diagnosis in clinically challenging patients with suspected ALS. Routine incorporation may boost early diagnosis, enhance patient selection and generate baseline measures for clinical trials.
Subject
Neurology (clinical),Neurology
Cited by
2 articles.
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