Describing and characterising variability in ALS disease progression

Abstract

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.

Keywords:

• ALS
• motor neurone disease
• machine learning

Acknowledgements

Our thanks to Dr James Berry, Massachusetts General Hospital, and the NEALS consortium for kindly providing the Lithium trial dataset. Our thanks also to all contributors to the ProACT database. Finally, we thank all patients who have contributed their information to the patient databases and enabled us to work towards improving patient care and understanding ALS through our research using these databases.

Disclosure statement

The authors report there are no competing interests to declare.

Additional information

Funding

There is no funding to be declared.