Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Dynamic Prediction of Motor Diagnosis in Huntington’s Disease Using a Joint Modeling Approach

Dynamic Prediction of Motor Diagnosis in Huntington’s Disease Using a Joint Modeling Approach Background: Prediction of motor diagnosis in Huntington’s disease (HD) can be improved by incorporating other phenotypic and biological clinical measures in addition to cytosine-adenine-guanine (CAG) repeat length and age.Objective: The objective was to compare various clinical and biomarker trajectories for tracking HD progression and predicting motor conversion.Methods: Participants were 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 trajectories of biomarkers for three hypothetical patients.Results: 1078 individuals were included in this analysis. Five longitudinal clinical and imaging measures were compared. The putamen volume had the best discrimination performance with area under the curve (AUC) ranging from 0.74 to 0.82 over time. The total motor score showed a comparable discriminative ability with AUC ranging from 0.69 to 0.78 over time. The model showed that decreasing putamen volume was a significant predictor of motor conversion. 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. If validated, this could be a valuable tool to guide the clinician in predicting age of onset and potentially rate of progression. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Huntington's Disease IOS Press

Dynamic Prediction of Motor Diagnosis in Huntington’s Disease Using a Joint Modeling Approach

Download PDF
11 pages

Loading next page...
 
/lp/ios-press/dynamic-prediction-of-motor-diagnosis-in-huntington-s-disease-using-a-whW0CmnO9j

References

References for this paper are not available at this time. We will be adding them shortly, thank you for your patience.

Publisher
IOS Press
Copyright
Copyright © 2017 IOS Press and the authors. All rights reserved
ISSN
1879-6397
eISSN
1879-6400
DOI
10.3233/JHD-170236
pmid
28582868
Publisher site
See Article on Publisher Site

Abstract

Background: Prediction of motor diagnosis in Huntington’s disease (HD) can be improved by incorporating other phenotypic and biological clinical measures in addition to cytosine-adenine-guanine (CAG) repeat length and age.Objective: The objective was to compare various clinical and biomarker trajectories for tracking HD progression and predicting motor conversion.Methods: Participants were 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 trajectories of biomarkers for three hypothetical patients.Results: 1078 individuals were included in this analysis. Five longitudinal clinical and imaging measures were compared. The putamen volume had the best discrimination performance with area under the curve (AUC) ranging from 0.74 to 0.82 over time. The total motor score showed a comparable discriminative ability with AUC ranging from 0.69 to 0.78 over time. The model showed that decreasing putamen volume was a significant predictor of motor conversion. 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. If validated, this could be a valuable tool to guide the clinician in predicting age of onset and potentially rate of progression.

Journal

Journal of Huntington's Disease – IOS Press

Published: Jan 1, 2017

References

  • A novel gene containing a trinucleotide repeat that is expanded and unstable on Huntington’s disease chromosomes
    MacDonald
  • Trinucleotide repeat length instability and age of onset in Huntington’s disease
    Duyao
  • CAG-repeat length and the age of onset in Huntington disease (HD): A review and validation study of statistical approaches
    Langbehn
  • Indexing disease progression at study entry with individuals at-risk for Huntington disease
    Zhang
  • Prediction of manifest Huntington disease with clinical and imaging measures: A 12-year prospective observational study
    Paulsen
  • Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data
    Rizopoulos
  • Preparing for preventive clinical trials: The predict-hd study
    Paulsen
  • Detection of Huntington’s disease decades before diagnosis:The Predict-HD study
    Paulsen