A Machine-Learning Derived Huntington's Disease Progression Model: Insights for Clinical Trial Design

Background: Applying machine-learning algorithms to large datasets such as those available in Huntington's disease offers the opportunity to discover hidden patterns, often not discernible to clinical observation. Objectives: To develop and validate a model of Huntington's disease progression using probabilistic machine learning methods. Methods: Longitudinal data encompassing 2079 assessment measures from four observational studies (PREDICT-HD, REGISTRY, TRACK-HD, and Enroll-HD) were integrated and machine-learning methods (Bayesian latent-variable analysis and continuous-time hidden Markov models) were applied to develop a probabilistic model of disease progression. The model was validated using a separate Enroll-HD dataset and compared with existing clinical reference assessments (Unified Huntington's Disease Rating Scale [UHDRS] diagnostic confidence level, total functional capacity, and total motor scores) and CAG-age product. Results: Nine disease states were discovered based on 44 motor, cognitive, and functional measures, which correlated with reference assessments. The validation set included 3158 participants (mean age, 48.4 years) of whom 61.5% had manifest disease. Analysis of transition times showed that early-disease states 1 and 2, which occur before motor diagnosis, lasted similar to 16 years. Increasing numbers of participants had motor onset during transition states 3 to 5, which collectively lasted similar to 10 years, and the late-disease states 6 to 9 also lasted similar to 10 years. The annual probability of conversion from one of the nine identified disease states to the next ranged from 5% to 27%. Conclusions: The natural history of Huntington's disease can be described by nine disease states of increasing severity. The ability to derive characteristics of disease states and probabilities for progression through these states will improve trial design and participant selection. (C) 2021 International Parkinson and Movement Disorder Society

Automated summary

A study developed and validated a model of Huntington's disease progression using machine-learning methods, identifying nine disease states and revealing transition probabilities ranging from 5% to 27%. The findings can improve trial design and participant selection.