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

Kan Li; Erin Furr-Stimming; Jane S Paulsen; Sheng Luo; PREDICT-HD Investigators of the Huntington Study Group

doi:10.3233/JHD-170236

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

Journal article

Open access

Peer reviewed

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

Kan Li, Erin Furr-Stimming, Jane S Paulsen, Sheng Luo and PREDICT-HD Investigators of the Huntington Study Group

Journal of Huntington's disease, Vol.6(2), pp.127-137

01/01/2017

DOI: 10.3233/JHD-170236

PMCID: PMC5505650

PMID: 28582868

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https://www.ncbi.nlm.nih.gov/pmc/articles/5505650View

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Abstract

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. The objective was to compare various clinical and biomarker trajectories for tracking HD progression and predicting motor conversion. 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. 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. 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.

Adolescent

Adult

Aged

Disease Progression

Female

Follow-Up Studies

Humans

Huntington Disease - diagnosis

Longitudinal Studies

Male

Middle Aged

Motor Activity

Patient-Specific Modeling

Precision Medicine

Prospective Studies

Survival Analysis

Trinucleotide Repeat Expansion

Young Adult

Details

Title: Subtitle: Dynamic Prediction of Motor Diagnosis in Huntington's Disease Using a Joint Modeling Approach
Creators: Kan Li - The University of Texas Health Science Center at Houston
Erin Furr-Stimming - The University of Texas Health Science Center at Houston
Jane S Paulsen - University of Iowa
Sheng Luo - The University of Texas Health Science Center at Houston
PREDICT-HD Investigators of the Huntington Study Group
Resource Type: Journal article
Publication Details: Journal of Huntington's disease, Vol.6(2), pp.127-137
DOI: 10.3233/JHD-170236
PMID: 28582868
PMCID: PMC5505650
NLM abbreviation: J Huntingtons Dis
ISSN: 1879-6397
eISSN: 1879-6400
Grant note: U01 NS043127 / NINDS NIH HHS R01 NS040068 / NINDS NIH HHS R01 NS091307 / NINDS NIH HHS
Language: English
Date published: 01/01/2017
Academic Unit: Psychiatry; Psychological and Brain Sciences
Record Identifier: 9984383303202771

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