Multivariate prediction of motor diagnosis in Huntington's disease: 12 years of PREDICT-HD

Long, Jeffrey D., Paulsen, Jane S., Rosser, Anne ORCID: https://orcid.org/0000-0002-4716-4753 and Hunt, Sarah 2015. Multivariate prediction of motor diagnosis in Huntington's disease: 12 years of PREDICT-HD. Movement Disorders 30 (12) , pp. 1664-1672. 10.1002/mds.26364

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Abstract

Background: It is well known in Huntington's disease that cytosine-adenine-guanine expansion and age at study entry are predictive of the timing of motor diagnosis. The goal of this study was to assess whether additional motor, imaging, cognitive, functional, psychiatric, and demographic variables measured at study entry increased the ability to predict the risk of motor diagnosis over 12 years. Methods: One thousand seventy-eight Huntington's disease gene–expanded carriers (64% female) from the Neurobiological Predictors of Huntington's Disease study were followed up for up to 12 y (mean = 5, standard deviation = 3.3) covering 2002 to 2014. No one had a motor diagnosis at study entry, but 225 (21%) carriers prospectively received a motor diagnosis. Analysis was performed with random survival forests, which is a machine learning method for right-censored data. Results: Adding 34 variables along with cytosine-adenine-guanine and age substantially increased predictive accuracy relative to cytosine-adenine-guanine and age alone. Adding six of the common motor and cognitive variables (total motor score, diagnostic confidence level, Symbol Digit Modalities Test, three Stroop tests) resulted in lower predictive accuracy than the full set, but still had twice the 5-y predictive accuracy than when using cytosine-adenine-guanine and age alone. Additional analysis suggested interactions and nonlinear effects that were characterized in a post hoc Cox regression model. Conclusions: Measurement of clinical variables can substantially increase the accuracy of predicting motor diagnosis over and above cytosine-adenine-guanine and age (and their interaction). Estimated probabilities can be used to characterize progression level and aid in future studies' sample selection

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Biosciences
Additional Information:	Sarah Hunt and Anne Rosser are listed at the end of the article as members of the PREDICT-HD Investigators and Coordinators.
Publisher:	John Wiley & Sons
ISSN:	0885-3185
Date of Acceptance:	13 July 2015
Last Modified:	31 Oct 2022 09:13
URI:	https://orca.cardiff.ac.uk/id/eprint/80224

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