Describing and characterising variability in ALS disease progression

Muzammil Arif Din Abdul Jabbar, Ling Guo, Yang Guo, Zachary Simmons, Erik P. Pioro, Savitha Ramasamy, Crystal Jing Jing Yeo

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Background, Objectives: Decrease in the revised ALS Functional Rating Scale (ALSFRS-R) score is currently the most widely used measure of disease progression. However, it does not sufficiently encompass the heterogeneity of ALS. We describe a measure of variability in ALSFRS-R scores and demonstrate its utility in disease characterization. Methods: We used 5030 ALS clinical trial patients from the Pooled Resource Open-Access ALS Clinical Trials database to calculate variability in disease progression employing a novel measure and correlated variability with disease span. We characterized the more and less variable populations and designed a machine learning model that used clinical, laboratory and demographic data to predict class of variability. The model was validated with a holdout clinical trial dataset of 84 ALS patients (NCT00818389). Results: Greater variability in disease progression was indicative of longer disease span on the patient-level. The machine learning model was able to predict class of variability with accuracy of 60.1–72.7% across different time periods and yielded a set of predictors based on clinical, laboratory and demographic data. A reduced set of 16 predictors and the holdout dataset yielded similar accuracy. Discussion: This measure of variability is a significant determinant of disease span for fast-progressing patients. The predictors identified may shed light on pathophysiology of variability, with greater variability in fast-progressing patients possibly indicative of greater compensatory reinnervation and longer disease span. Increasing variability alongside decreasing rate of disease progression could be a future aim of trials for faster-progressing patients.

Original languageEnglish (US)
Pages (from-to)34-45
Number of pages12
JournalAmyotrophic Lateral Sclerosis and Frontotemporal Degeneration
Issue number1-2
StatePublished - 2024

All Science Journal Classification (ASJC) codes

  • Neurology
  • Clinical Neurology

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