@article{discovery10129873,
         journal = {Frontiers in Neurology},
       publisher = {FRONTIERS MEDIA SA},
           title = {Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease},
            year = {2021},
            note = {This work is licensed under a Creative Commons Attribution 4.0 International License. The images
or other third party material in this article are included in the Creative Commons license,
unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license,
users will need to obtain permission from the license holder to reproduce the material. To view a copy of this
license, visit http://creativecommons.org/licenses/by/4.0/},
           month = {May},
          volume = {12},
        keywords = {Science \& Technology, Life Sciences \& Biomedicine, Clinical Neurology, Neurosciences, Neurosciences \& Neurology, Huntington's disease, disease progression, prognostic variables, machine learning, random forest, VARIABLE IMPORTANCE, PREMANIFEST, BIOMARKER, ONSET, MOTOR, HD},
        abstract = {Huntington's disease (HD) is characterised by a triad of cognitive, behavioural, and motor
symptoms which lead to functional decline and loss of independence. With potential
disease-modifying therapies in development, there is interest in accurately measuring HD
progression and characterising prognostic variables to improve efficiency of clinical trials.
Using the large, prospective Enroll-HD cohort, we investigated the relative contribution
and ranking of potential prognostic variables in patients with manifest HD. A random
forest regression model was trained to predict change of clinical outcomes based on
the variables, which were ranked based on their contribution to the prediction. The
highest-ranked variables included novel predictors of progression-being accompanied
at clinical visit, cognitive impairment, age at diagnosis and tetrabenazine or antipsychotics
use-in addition to established predictors, cytosine adenine guanine (CAG) repeat
length and CAG-age product. The novel prognostic variables improved the ability of the
model to predict clinical outcomes and may be candidates for statistical control in HD
clinical studies.},
          author = {Ghazaleh, N and Houghton, R and Palermo, G and Schobel, SA and Wijeratne, PA and Long, JD},
             url = {http://doi.org/10.3389/fneur.2021.678484}
}