Predicting Huntington Disease Integrated Staging System Stage Transitions to Inform Clinical Trial Enrichment

Thomas M. Menino Convention & Exhibition Center 

Background:
Clinical trials for Huntington disease (HD) increasingly aim to intervene before the onset of irreversible neurodegeneration. Achieving this goal requires identifying individuals at higher risk of transitioning between Huntington Disease Integrated Staging System (HD-ISS) stages and understanding how such risk stratification may improve clinical trial efficiency.

Methods:
We analyzed data from the Enroll-HD observational study (Periodic Dataset 7; 2012--2024) to model time to HD-ISS stage transitions (Stage 0 → 1, 1 → 2, and 2 → 3). Parametric accelerated failure time (AFT) models were fitted using candidate distributions selected via AIC/BIC and comparison with Kaplan--Meier estimates. Predictive performance of candidate markers---including CAP score, prognostic index normalized (PIN), composite Unified Huntington's Disease Rating Scale (cUHDRS), and individual components (CAG repeat length, TMS, SDMT)---was evaluated using the area under the receiver operating characteristic curve (AUC) with cross-validation. Individuals were stratified into risk quartiles based on the strongest predictor for each transition. These risk groups were then used to evaluate clinical trial enrichment strategies through sample size calculations under AFT-based treatment effect assumptions.

Results:
The generalized gamma distribution provided the best fit across stage transitions. CAP score was the strongest predictor for the Stage 0 → 1 transition (cross-validated AUC 0.87), whereas PIN was most predictive for Stage 1 → 2 (AUC 0.64), and cUHDRS for Stage 2 → 3 (AUC 0.54). Risk stratification demonstrated substantially shorter transition times among higher-risk quartiles. Enrichment strategies that restricted enrollment to higher-risk subgroups markedly reduced required sample sizes. For example, in a one-year trial targeting the Stage 0 → 1 transition, restricting enrollment to the highest CAP quartile reduced required sample size by approximately four-fold for detecting a moderate treatment effect.

Conclusions:
Predictive markers of HD progression can identify individuals at elevated risk of stage transition. Incorporating risk stratification into trial enrollment may substantially improve the efficiency of HD clinical trials targeting early disease stages.

multi-state modeling

neurodegenerative disease

censoring

survival analysis

clinical trial enrichment

disease progression 

Biometrics Section