Toh TH, Abdul-Aziz NA, Yahya MA, Goh KJ, Loh EC, Capelle DP, Shahrizaila N. A model incorporating ultrasound to predict the probability of fast disease progression in amyotrophic lateral sclerosis.
Clin Neurophysiol 2021;
132:2722-2728. [PMID:
34312065 DOI:
10.1016/j.clinph.2021.05.034]
[Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/21/2021] [Accepted: 05/26/2021] [Indexed: 11/18/2022]
Abstract
OBJECTIVE
We aimed to develop a model to predict amyotrophic lateral sclerosis (ALS) disease progression based on clinical and neuromuscular ultrasound (NMUS) parameters.
METHODS
ALS patients were prospectively recruited. Muscle fasciculation (≥2 over 30-seconds, examined in biceps brachii-brachialis (BB), brachioradialis, tibialis anterior and vastus medialis) and nerve cross-sectional area (CSA) (median, ulnar, tibial, fibular nerve) were evaluated through NMUS. Ultrasound parameters were correlated with clinical data, including revised ALS Functional Rating Scale (ALSFRS-R) progression at one year. A predictive model was constructed to differentiate fast progressors (ALSFRS-R decline ≥ 1/month) from non-fast progressors.
RESULTS
40 ALS patients were recruited. Three parameters emerged as strong predictors of fast progressors: (i) ALSFRS-R slope at time of NMUS (p = 0.041), (ii) BB fasciculation count (p = 0.027) and (iii) proximal to distal median nerve CSA ratio < 1.22 (p = 0.026). A predictive model (scores 0-5) was built with excellent discrimination (area under curve: 0.915). Using a score of ≥ 3, the model demonstrated good sensitivity (81.3%) and specificity (91.0%) in differentiating fast from non-fast progressors.
CONCLUSION
The current model is simple and can predict the probability of fast disease progression.
SIGNIFICANCE
This model has potential as a surrogate biomarker of ALS disease progression.
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