Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease

Front Neurol. 2021 May 20:12:678484. doi: 10.3389/fneur.2021.678484. eCollection 2021.


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.

Keywords: Huntington's disease; disease progression; machine learning; prognostic variables; random forest.