Objective: Compare various clinical and biomarker trajectories for tracking Huntington’s disease (HD) progression and predicting motor conversion.

Background: Huntington’s disease (HD) is a fatal neurodegenerative disease caused by a cytosine-adenine-guanine (CAG) repeat expansion in the Hungtintgin (HTT) gene. Prediction of motor diagnosis in HD can be improved by incorporating other phenotypic and biological clinical measures in addition to cytosine-adenine-guanine (CAG) repeat length and age. It has long been recognized that the mean age of disease onset is inversely correlated to the length of the CAG expansion. Thus, CAG and age and possibly their interaction are generally used as progression indexes to predict time of motor onset. However, prediction based on CAG and age is far from perfect.

Methods: Data used in this analysis were obtained from the PREDICT-HD study.  We constructed a mixed-effect model to describe the change of measures while jointly modeling the process with time to HD diagnosis. The model was then used for subject-specific prediction. We employed the time-dependent receiver operating characteristic (ROC) method to assess the discriminating capability of the measures to identify high and low risk patients. The strongest predictor was used to illustrate the dynamic prediction of the disease risk and future health outcome for three hypothetical patients.

Results: 1078 individuals were included in this analysis. The study followed 1078 participants, who had genetic risk (with more than 35 HTT CAG repeats) of HD without a motor diagnosis at study entry, from 2002 to 2014 annually. Five longitudinal clinical and imaging measures were compared. The putamen volume had the best discrimination performance with AUC ranging from 0.71 to 0.81 over time. The total motor score showed a comparable discriminative ability with AUC ranging from 0.73 to 0.78 over time. The model showed that a decreasing trend of putamen volume was a significant predictor of motor convention. A web-based calculator was developed for implementing the methods.

Conclusions: By jointly modeling longitudinal data with time-to-event outcomes, it is possible to construct an individualized dynamic event prediction model that renews over time with accumulating evidence. It provides a valuable tool to guide the personalized assessment and facilitate earlier diagnoses of Huntington’s disease.

References: Paulsen JS, Long JD, Ross CA, et al. Prediction of manifest Huntington disease with clinical and imaging measures: A 12-year prospective observational study. Lancet Neurol 2014; 13: 1193–1201

Long JD, Paulsen JS, PREDICT-HD Investigators and Coordinators of the Huntington Study Group. Multivariate prediction of motor diagnosis in Huntington’s disease: 12 years of PREDICT-HD. Mov Disord 2015; 30: 1664–1672.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/dynamic-prediction-of-motor-diagnosis-in-huntingtons-disease-using-a-joint-modeling-approach/