Affiliations: [a] Clinical Neurosciences, University ofSouthampton, Life Sciences Building, Highfield Campus, Southampton, UK
| [b] Wessex Neurological Centre, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| [c] Wessex NIHR CLAHRC, University ofSouthampton, Life Sciences Building, Highfield Campus, Southampton, UK
Correspondence:
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Correspondence to: Dr. Christopher Kipps, Wessex Neurological Centre, University Hospital Southampton NHS Foundation Trust, Tremona Road, Southampton SO16 6YD, UK. Tel.: +44 23 8077 7222; E-mail: christopher.kipps@soton.ac.uk.
Abstract: Background: In Huntington’s disease (HD), it remains unclear how symptom severity and rate of symptomatic change relates to age and CAG repeat number (CAGn). It is often difficult for clinicians to assess whether an affected individual’s symptoms are progressing at a similar rate to their affected peers, limiting their ability to intervene at the most appropriate time. Objective: To develop a clinical dashboard that compares an individual’s total motor score (TMS), total functional capacity (TFC) and symbol digit modality test (SDMT) scores against a global cohort, controlling for age and CAGn. The dashboard could then be used by clinicians to identify individuals progressing at a disproportionate rate to his or her peers. Methods: Annualised longitudinal clinical assessment scores from the Enroll-HD dataset were used to generate decline trajectories of the global cohort, allowing cross-sectional (TMS n = 734; TFC n = 734; SDMT n = 694) and longitudinal (TMS n = 270; TFC n = 270; SDMT n = 247) comparison with individual clinical symptom rating scores, to assess decline relative to affected peers. Results: An electronic dashboard with a dynamic output display was created that rapidly compares clinical symptom rating scores of a specific individual against affected peers from a global cohort of comparable CAGn. Conclusions: This study shows the potential for use of multi-centre trial data in allowing comparison of the individual to a larger group to facilitate improved decision-making for individual patients. Visualisation of these metrics via a clinical dashboard demonstrates how it may aid identification of those with disproportionate decline, offering potential for intervention at specific critical points in the disease course.
