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.
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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.
| Type: | Article |
|---|---|
| Title: | Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3389/fneur.2021.678484 |
| Publisher version: | http://doi.org/10.3389/fneur.2021.678484 |
| Language: | English |
| Additional information: | 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/ |
| 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 |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10129873 |
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