Prognostic enrichment for early-stage Huntington's disease: An explainable machine learning approach for clinical trial.

BACKGROUND: In Huntington's disease clinical trials, recruitment and stratification approaches primarily rely on genetic load, cognitive and motor assessment scores. They focus less on in vivo brain imaging markers, which reflect neuropathology well before clinical diagnosis. Machine learning methods offer a degree of sophistication which could significantly improve prognosis and stratification by leveraging multimodal biomarkers from large datasets. Such models specifically tailored to HD gene expansion carriers could further enhance the efficacy of the stratification process. OBJECTIVES: To improve stratification of Huntington's disease individuals for clinical trials. METHODS: We used data from 451 gene positive individuals with Huntington's disease (both premanifest and diagnosed) from previously published cohorts (PREDICT, TRACK, TrackON, and IMAGE). We applied whole-brain parcellation to longitudinal brain scans and measured the rate of lateral ventricular enlargement, over 3 years, which was used as the target variable for our prognostic random forest regression models. The models were trained on various combinations of features at baseline, including genetic load, cognitive and motor assessment score biomarkers, as well as brain imaging-derived features. Furthermore, a simplified stratification model was developed to classify individuals into two homogenous groups (low risk and high risk) based on their anticipated rate of ventricular enlargement. RESULTS: The predictive accuracy of the prognostic models substantially improved by integrating brain imaging features alongside genetic load, cognitive and motor biomarkers: a 24 % reduction in the cross-validated mean absolute error, yielding an error of 530 mm3/year. The stratification model had a cross-validated accuracy of 81 % in differentiating between moderate and fast progressors (precision = 83 %, recall = 80 %). CONCLUSIONS: This study validated the effectiveness of machine learning in differentiating between low- and high-risk individuals based on the rate of ventricular enlargement. The models were exclusively trained using features from HD individuals, which offers a more disease-specific, simplified, and accurate approach for prognostic enrichment compared to relying on features extracted from healthy control groups, as done in previous studies. The proposed method has the potential to enhance clinical utility by: i) enabling more targeted recruitment of individuals for clinical trials, ii) improving post-hoc evaluation of individuals, and iii) ultimately leading to better outcomes for individuals through personalized treatment selection.

Tracking Huntington's Disease Progression Using Motor, Functional, Cognitive, and Imaging Markers.

BACKGROUND: Potential therapeutic targets and clinical trials for Huntington's disease have grown immensely in the last decade. However, to improve clinical trial outcomes, there is a need to better characterize profiles of signs and symptoms across different epochs of the disease to improve selection of participants. OBJECTIVE: The objective of the present study was to best distinguish longitudinal trajectories across different Huntington's disease progression groups. METHODS: Clinical and morphometric imaging data from 1082 participants across IMAGE-HD, TRACK-HD, and PREDICT-HD studies were combined, with longitudinal times ranging between 1 and 10 years. Participants were classified into 4 groups using CAG and age product. Using multivariate linear mixed modeling, 63 combinations of markers were tested for their sensitivity in differentiating CAG and age product groups. Next, multivariate linear mixed modeling was applied to define the best combination of markers to track progression across individual CAG and age product groups. RESULTS: Putamen and caudate volumes, individually and/or combined, were identified as the best variables to both differentiate CAG and age product groups and track progression within them. The model using only caudate volume best described advanced disease progression in the combined data set. Contrary to expectations, combining clinical markers and volumetric measures did not improve tracking longitudinal progression. CONCLUSIONS: Monitoring volumetric changes throughout a trial (alongside primary and secondary clinical end points) may provide a more comprehensive understanding of improvements in functional outcomes and help to improve the design of clinical trials. Alternatively, our results suggest that imaging deserves consideration as an end point in clinical trials because of the prospect of greater sensitivity. © 2021 International Parkinson and Movement Disorder Society.