Predicting disease progression in amyotrophic lateral sclerosis

• Authors: Albert A. Taylor, Origent Data Sciences, Inc.; Christina Fournier, Emory University; Meraida Polak, Emory University; Liuxia Wang, Sentrana Inc; Neta Zach, Prize4Life; Mike Keymer, Origent Data Sciences, Inc.; Jonathan D Glass, Emory University; David L. Ennist, Origent Data Sciences, Inc.
• Journal Title: Annals of Clinical and Translational Neurology
• Volume: Volume 3, Number 11
• Publisher: Wiley Open Access: Creative Commons Attribution Non-Commercial No Derivatives | 2016-11-01, Pages 866-875
• Type of Work: Article | Final Publisher PDF
• Publisher DOI: http://doi.org/10.1002/acn3.348
• Subjects: Biology, Neuroscience
• Keywords: Science & Technology; Life Sciences & Biomedicine; Clinical Neurology; Neurosciences; Neurosciences & Neurology; EPIDEMIOLOGY; CARE

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.