TY  - JOUR
JF  - Frontiers in Neurology
A1  - Ghazaleh, N
A1  - Houghton, R
A1  - Palermo, G
A1  - Schobel, SA
A1  - Wijeratne, PA
A1  - Long, JD
KW  - Science & Technology
KW  -  Life Sciences & Biomedicine
KW  -  Clinical Neurology
KW  -  Neurosciences
KW  -  Neurosciences & Neurology
KW  -  Huntington's disease
KW  -  disease progression
KW  -  prognostic variables
KW  -  machine learning
KW  -  random forest
KW  -  VARIABLE IMPORTANCE
KW  -  PREMANIFEST
KW  -  BIOMARKER
KW  -  ONSET
KW  -  MOTOR
KW  -  HD
N2  - 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.
ID  - discovery10129873
UR  - http://doi.org/10.3389/fneur.2021.678484
PB  - FRONTIERS MEDIA SA
N1  - 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/
TI  - Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease
VL  - 12
AV  - public
Y1  - 2021/05/20/
EP  - 8
ER  -