Amyotrophic lateral sclerosis: Disease progression, variability and outcome measures

Abstract

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease that leads to muscle weakness, respiratory failure and typically death within 3 to 5 years after symptom onset. ALS affects 1 in 300 people and there is a large unmet need for an effective treatment. Unfortunately, conduction of over 120 clinical trials in the past 25 years has not resulted in development of an effective treatment for this debilitating disease. Studies in this thesis aimed to investigate and reduce variation in outcome measures (i.e. ‘the noise’), optimize strategies to measure ALS disease progression and detect treatment effects (i.e. ‘the signal’) in clinical trials. Large variation between patients in symptoms and progression rates complicates monitoring disease progression and identifying effective treatments in ALS clinical trials. Consequently, reducing between-patient variability in clinical trials is important for efficiency reasons and to maximize the likelihood of identifying effective treatments. Multiple factors that can inflate variability were identified and investigated; 1) variation in ALS symptoms, 2) lack of standardization of outcome measures, 3) strategy to analyze outcome measures and 4) insensitivity of outcome measures. The studies in this thesis show that use of refined diagnostic criteria and prediction models for patient selection, adequate standardization of outcome measures and analytical strategies that account for variation of ALS symptoms and use of outcome measures that are optimized to measure disease progression may all present important efficiency gains for clinical trials. In the end, the evidence-based innovations presented in this thesis may accelerate identification of an effective treatment for ALS.