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Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease
Published on: June 9, 2018
Disease Progression in Huntington Disease: An Analysis of Multiple Longitudinal Outcomes
Tanya P Garcia1, Yuanjia Wang2, Ira Shoulson3
1Department of Statistics, Texas A&M University, College Station, TX, USA.
Insights
Predicting Huntington disease (HD) progression is crucial for new therapies. The Joint Progression of Risk Assessment Tool (JPRAT) improves prediction accuracy by accounting for outcome dependencies in premanifest HD patients.
Area of Science:
- Neuroscience
- Genetics
- Clinical Trials
Background:
- Huntington disease (HD) requires precise predictive methods for targeted therapy development.
- Accurate prediction of clinical outcomes in premanifest HD patients is essential.
Purpose of the Study:
- To predict the onset of motor diagnosis, cognitive impairment, and Stage II Total Functional Capacity (TFC) in premanifest HD.
- To account for the dependencies between these clinical outcomes using a novel statistical approach.
Main Methods:
- Utilized data from three longitudinal observational studies (PHAROS, PREDICT, COHORT) of premanifest HD participants (n=1685).
- Employed the Joint Progression of Risk Assessment Tool (JPRAT) to estimate outcome probabilities, considering interdependencies.
- Pooled probability estimates across studies for robust analysis.
Main Results:
- JPRAT demonstrated a 43% reduction in variability compared to models ignoring outcome dependencies.
- Provided specific 5-year risk probabilities for motor diagnosis, cognitive impairment, and Stage II TFC based on age and CAG repeat counts.
- Illustrated risk differences between 40- and 45-year-old individuals with 42 CAG repeats.
Conclusions:
- The JPRAT model offers improved prediction accuracy for Huntington disease progression.
- This tool can benefit rare disease treatment studies and offers an alternative to composite outcomes for individual outcome interpretation.
Background:
Critical to discovering targeted therapies for Huntington disease (HD) are validated methods that more precisely predict when clinical outcomes occur for different patient profiles.
Objective:
To more precisely predict the probability of when motor diagnosis (diagnostic confidence level 4) on the Unified Huntington's Disease Rating Scale (UHDRS), cognitive impairment (two or more neuropsychological scores on the UHDRS were 1.5 standard deviations below normative means) and Stage II Total Functional Capacity (TFC) first occur by accounting for dependencies between these outcomes.
Methods:
Adult premanifest participants with ≥36 CAG repeats were selected from multi-center, longitudinal, observational studies: Prospective Huntington At Risk Observational Study (PHAROS, n = 346), Neurobiological Predictors of Huntington Disease (PREDICT, n = 909); and Cooperative Huntington Observational Research Trial (COHORT, n = 430). Probabilities were estimated for each study, and pooled using the Joint Progression of Risk Assessment Tool (JPRAT) which accounts for dependencies between outcomes.
Results:
All studies had similar probabilities of when motor diagnosis, cognitive impairment, and Stage II TFC first occurred. Probability estimates from JPRAT were 43% less variable than from models that ignored dependencies between outcomes. The probability of experiencing motor-diagnosis, cognitive impairment, and Stage II TFC within 5 years was 10%, 18%, and 7%, respectively for 45-year-olds with 42 CAG repeats, and was 4%, 10% and 5%, respectively, for 40 year olds with 42 CAG repeats.
Conclusions:
Improved predictions from JPRAT may benefit treatment studies of rare diseases and is an alternative to composite outcomes when the objective is interpreting individual outcomes within the same model.
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