Year: 2024
Type: conference-abstract
Abstract: Background The introduction of Huntington’s Disease Integrated Staging System (HD-ISS) facilitated highly targeted enrolment in HD clinical trials. However, each of the four stages in HD-ISS could benefit from prognostic enrichment, which aims to enhance the assessment of therapeutic effects by identifying high-risk individuals for inclusion. Aims We aimed to classify HD individuals into homogeneous groups based on forthcoming lateral ventricular enlargement rate (LVER). Methods We used data from 451 HD gene expansion carriers from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and used linear regression to measure LVER over 3 years (mm3/year). Subsequently, it was used as the target variable for our random forest stratification model. HD individuals were classified into two homogenous groups: low-risk and high-risk. The model was trained using different combinations of features at baseline including the genetic load, cognitive and motor biomarkers, as well as brain imaging-derived features. Proposed as a metric for prognostic enrichment, the probability of LVER surpassing a threshold of 750 mm3/year was compared with normalized prognostic index (PIN) figure 1. Results The stratification model had a cross-validated accuracy of 81%. Compared to the PIN score, the proposed probabilistic metric had lower Pearson’s kurtosis for HD-ISS stages 1 and 2 indicating higher discriminative power among individuals within these two stages. Conclusions This study validated ML-based predictive modelling for identifying high-risk HD individuals. The proposed method has the potential to enhance clinical trials by enabling more effective participant enrolment and improved post-hoc evaluation of therapeutic effects.
Language: English
Subfield: Cellular and Molecular Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
OpenAlex ID: W4402374664