Amyotrophic lateral sclerosis (ALS) is a devastating disease recognized as a priority by the Veteran Health Administration because of its increased incidence in veterans, thus earning it a service connection status. Current treatment is largely supportive, and there is dire need for the development of therapeutics that slow disease progression. However, better outcome measures and prediction tools are required to improve research efficiency and facilitate the discovery of successful treatments for ALS. This proposal is to develop novel and important research tools that can be used to design more conclusive ALS clinical trials. First, a new ALS disability scale will be developed with improved sensitivity and reliability to be used as an outcome measure for ALS clinical trials. The current clinical outcome scale for ALS trials is the revised ALS Functional Rating Scale (ALSFRS-R). The ALSFRS-R is easy to administer and correlates with patient survival. However, the scale is relatively insensitive to changes in function and is not specific for changes in true disease status. Indeed, changes in patient comfort level due to palliative interventions are recorded as ?improvements? in the ALSFRS-R even though the underlying disease is progressing. Better mathematical tools are available for the development of outcome measure scales. Rasch analysis is a modern test-theory technique that produces validated, reliable, responsive, linearly-weighted scales with meaningful overall sum- scores for use in clinical trials. Rasch analysis has been used to improve outcome questionnaires for other neurologic diseases. By combining clinical expertise with the mathematically rigorous Rasch methodology, a new ALS disability scale will be created that will overcome many of the limitations of the ALSFRS-R. Secondly, predictive models will be developed and validated in collaboration with a data analytics expert to allow for stringent patient stratification during clinical trial enrollment, improving trial efficiency and reducing costs. Simulations with the best existing models, which rely on non-parametric, non-linear modeling techniques, show the ability to reduce the number of patients needed for an ALS trial by 20%, and these models significantly outperformed the predictions of expert ALS clinicians. Refinement of these models to utilize only baseline data, incorporate outcome measures with higher sensitivity, and use biomarkers as predictor variables, will improve predictive power and provide a valuable tool for trial design and patient care. Finally, immune factors will be investigated as biomarkers of disease activity and as biologic variables in ALS prediction models. Previous studies indicate that pro-inflammatory markers are associated with faster disease progression in ALS, while anti-inflammatory markers are seen with slower disease course. Markers of T-cell populations are promising prognostic biomarkers based on animal and human studies and based on widespread availability of commercial laboratory testing, warranting further study to determine if T-cell markers can be used to make predictions about disease progression and guide selection of clinical trial populations. Formal coursework in biostatistics and epidemiology will be completed. Valuable training and development of outcomes will also include working closely with an expert team of mentors and collaborators, including an accomplished senior VA neuroscience researcher, an accomplished senior ALS researcher, an experienced biostatistician, and an expert data scientist. Together this team will enable the candidate to execute the research proposal and develop a new skillset in modern test method-based outcome measures as well as non- linear, non-parametric modeling techniques. As understanding of the biologic mechanisms that contribute to ALS grows, it will be necessary for outcome measures and predictive models to adapt and evolve over time. The training and experience from this proposal will provide the candidate with a unique skillset separate from that of her primary mentors and will lay the foundation for an independent research career in clinical research with the ultimate goal to improve outcomes and delivery of care in veterans with ALS.

Public Health Relevance

Amyotrophic Lateral Sclerosis (ALS) is a presumed service-connected condition within the VA due to a higher risk of ALS in veterans. The VHA Handbook 1101.07 released in July 2014 states that an average of 1,520 veterans with ALS were seen per year in the VHA between 2005-2009, and the future Veteran ALS prevalence is projected to be 4,220. As a result, ALS is a high priority issue for veterans and the clinicians who care for them. ALS is heterogeneous in terms of phenotype and rate of progression, and therefore clinical trials for efficacy require large numbers of patients and are time- consuming and expensive. This proposal, which will develop improved outcome measures and prediction tools for ALS, will improve research efficiency to facilitate development of new treatments for this devastating disorder.

National Institute of Health (NIH)
Veterans Affairs (VA)
Veterans Administration (IK2)
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Neurobiology E (NURE)
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Veterans Health Administration
United States
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