Login / Signup

Clinical characteristics of a large cohort of US participants enrolled in the National Amyotrophic Lateral Sclerosis (ALS) Registry, 2010-2015.

Jaime RaymondBjörn E OskarssonPaul MehtaKevin Horton
Published in: Amyotrophic lateral sclerosis & frontotemporal degeneration (2019)
Background: Amyotrophic lateral sclerosis (ALS) is a progressive fatal disease with a varying range of clinical characteristics. Objective: To describe the clinical characteristics in a large cohort of ALS participants enrolled in the National ALS Registry. Methods: Data from ALS participants who completed the Registry's online clinical survey module during 2010-2015 were analyzed to determine characteristics, such as site of onset, associated symptoms, time of symptom onset to diagnosis, time of diagnosis to hospice referral, and pharmacological and non-pharmacological interventions. Results: Of the 1758 participants who completed the survey, 60.9% were male, 62.1% were 50-69 years old, and 95.5% white. Approximately, 72.0% reported initial limb weakness onset of disease, followed by bulbar (22.1%), and trunk/global onset (6.1%). Other symptoms ever experienced included cramps (56.7%), fasciculations (56.3%), and dysarthria (33.0%). The median time between an increase of muscle cramps until an ALS diagnosis was 12 months; limb onset participants had cramps longer preceding diagnosis versus those with bulbar onset. The most frequent interventions used included riluzole (48.3% currently using), wheelchairs/scooters (32.8%), and noninvasive breathing equipment (30.0%). Participants with trunk/global onset were referred to hospice almost four times earlier than others. Conclusions: These data show how ALS clinical characteristics differ widely in a large cohort of participants preceding diagnosis and reflect variations in disease onset, progression, and prognosis. Better characterization of symptom onset may assist clinicians in diagnosing ALS sooner, which could lead to earlier therapeutic interventions.
Keyphrases
  • amyotrophic lateral sclerosis
  • physical activity
  • palliative care
  • multiple sclerosis
  • healthcare
  • electronic health record
  • skeletal muscle
  • machine learning
  • depressive symptoms
  • artificial intelligence
  • data analysis