Open AccessPredicting disease progression in amyotrophic lateral sclerosis
Author(s) -
Taylor Albert A.,
Fournier Christina,
Polak Meraida,
Wang Liuxia,
Zach Neta,
Keymer Mike,
Glass Jonathan D.,
Ennist David L.
Publication year - 2016
Publication title -
annals of clinical and translational neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.824
H-Index - 42
ISSN - 2328-9503
DOI - 10.1002/acn3.348
Subject(s) - overfitting , medicine , amyotrophic lateral sclerosis , clinical trial , population , disease , mixed model , random effects model , random forest , baseline (sea) , physical therapy , physical medicine and rehabilitation , statistics , artificial intelligence , machine learning , computer science , meta analysis , mathematics , environmental health , artificial neural network , geology , oceanography
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.