Optimizing Screening for Intrastriatal Interventions in Huntington's Disease Using Predictive Models.

ABSTRACT:

Background

Intrastriatal delivery of potential therapeutics in Huntington's disease (HD) requires sufficient caudate and putamen volumes. Currently, volumetric magnetic resonance imaging is rarely done in clinical practice, and these data are not available in large research cohorts such as Enroll-HD.

Objective

The objective of this study was to investigate whether predictive models can accurately classify HD patients who exceed caudate and putamen volume thresholds required for intrastriatal therapeutic interventions.

Methods

We obtained and merged data for 1374 individuals across three HD cohorts: IMAGE-HD, PREDICT-HD, and TRACK-HD/TRACK-ON. We imputed missing data for clinical variables with >72% non-missing values and used the model-building algorithm BORUTA to ide

SUBMITTER: Barrett MJ 

PROVIDER: S-EPMC11102310 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature