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Multivariate prediction of motor diagnosis in Huntington's disease: 12 years of PREDICT-HD
Journal article   Open access   Peer reviewed

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

Jeffrey D Long, Jane S Paulsen and PREDICT-HD Investigators and Coordinators of the Huntington Study Group
Movement disorders, Vol.30(12), pp.1664-1672
10/2015
DOI: 10.1002/mds.26364
PMCID: PMC4795466
PMID: 26340420
url
https://doi.org/10.1002/mds.26364View
Published (Version of record) Open Access

Abstract

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. 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. 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. 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.
 Machine Learning Severity of Illness Index Motor Activity - physiology Humans Middle Aged Proportional Hazards Models Male Trinucleotide Repeats - genetics Nerve Tissue Proteins - genetics Disease Progression Neuropsychological Tests Young Adult Cognition Disorders - diagnosis Huntingtin Protein Cognition Disorders - etiology Aged, 80 and over Huntington Disease - genetics Adult Female Huntington Disease - diagnosis Aged Longitudinal Studies Huntington Disease - physiopathology

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