eprintid: 10129873
rev_number: 16
eprint_status: archive
userid: 608
dir: disk0/10/12/98/73
datestamp: 2021-06-21 14:11:04
lastmod: 2021-09-24 22:05:43
status_changed: 2021-06-21 14:11:04
type: article
metadata_visibility: show
creators_name: Ghazaleh, N
creators_name: Houghton, R
creators_name: Palermo, G
creators_name: Schobel, SA
creators_name: Wijeratne, PA
creators_name: Long, JD
title: Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F48
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
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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.
date: 2021-05-20
date_type: published
publisher: FRONTIERS MEDIA SA
official_url: http://doi.org/10.3389/fneur.2021.678484
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1871147
doi: 10.3389/fneur.2021.678484
lyricists_name: Wijeratne, Peter
lyricists_id: PAWIJ75
actors_name: Flynn, Bernadette
actors_id: BFFLY94
actors_role: owner
full_text_status: public
publication: Frontiers in Neurology
volume: 12
article_number: 678484
pages: 8
citation:        Ghazaleh, N;    Houghton, R;    Palermo, G;    Schobel, SA;    Wijeratne, PA;    Long, JD;      (2021)    Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease.                   Frontiers in Neurology , 12     , Article 678484.  10.3389/fneur.2021.678484 <https://doi.org/10.3389/fneur.2021.678484>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10129873/1/fneur-12-678484.pdf