Abstract

Background An original primary aim of PREDICT-HD was identification and quantification of clinical marker sets predictive of HD diagnosis. Such models would add substantial power and efficiency to eventual clinical trials to prevent or delay HD onset. Since the study's inception, skepticism has grown regarding the utility of “diagnosis” as an HD concept. For this analysis, we assume that illness transition can at least be crudely dichotomized by an abstract point of “diagnosis.” Nonetheless, we see substantial evidence of inter-rater variability in defining this point and make appropriate statistical adjustments. Methods As of April 2009, 2179 years of pre-diagnosis follow-up were available on 718 participants, 126 of whom had been diagnosed by UHDRS confidence level 4. We constructed multivariate models predicting “diagnosis” from the study's non–brain-imaging baseline measures. Various criteria consistently selected from among candidate variables each individually predictive of diagnosis were used. Models were tested for additional prediction beyond that achieved by CAG length and age. Cox proportional hazard modeling was used, with stratification by rater in order to minimize systematic differences in diagnostic thresholds and motor scoring. Results The combined baseline measures of maximum tapping speed, paced tapping precision, Stroop inference, and smell identification accuracy were highly predictive. Estimated hazard ratio increased 5.39 across the interquartile range of estimated risk ( p Conclusion The relative risk of transition to HD “diagnosis” is predictable using measures that have played little traditional role defining diagnosis. This demonstrates important practical utility for these predictors. Absent a highly reliable, continuous measure of early HD, the success of these results also affirms the potential value of reliable discrete endpoints for early-HD trials.

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