Predicting disease progression in amyotrophic lateral sclerosis. Issue 11 (7th September 2016)

• Record Type: Journal Article
• Authors: Taylor, Albert A.; Fournier, Christina; Polak, Meraida; Wang, Liuxia; Zach, Neta; Keymer, Mike; Glass, Jonathan D.; Ennist, David L.
• Is Part Of: Annals of clinical and translational neurology. Volume 3:Issue 11(2016)
• Page Start: 866
• Page End: 875
• Publication Date: 2016-09-07
• Subjects: Nervous system -- Diseases -- Periodicals; Neurology -- Periodicals; 616.8005
• DOI: 10.1002/acn3.348
• Languages: English
• ISSNs: 2328-9503

Abstract

Abstract: Objective: It is essential to develop predictive algorithms for Amyotrophic Lateral Sclerosis (ALS) disease progression to allow for efficient clinical trials and patient care. The best existing predictive models rely on several months of baseline data and have only been validated in clinical trial research datasets. We asked whether a model developed using clinical research patient data could be applied to the broader ALS population typically seen at a tertiary care ALS clinic. Methods: Based on the PRO‐ACT ALS database, we developed random forest (RF), pre‐slope, and generalized linear (GLM) models to test whether accurate, unbiased models could be created using only baseline data. Secondly, we tested whether a model could be validated with a clinical patient dataset to demonstrate broader applicability. Results: We found that a random forest model using only baseline data could accurately predict disease progression for a clinical trial research dataset as well as a population of patients being treated at a tertiary care clinic. The RF Model outperformed a pre‐slope model and was similar to a GLM model in terms of root mean square deviation at early time points. At later time points, the RF Model was far superior to either model. Finally, we found that only the RF Model was unbiased and was less subject to overfitting than either of the other two models when applied to a clinic population. Interpretation: We conclude that the RF Model delivers superior predictions of ALS disease progression.