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.
PhullJ, HallJ. Clinical dashboards and their use in an adult mental health inpatient setting, a pilot study. Clin Gov. 2015;20(4):199–207.
2.
SwartzJL, CiminoJJ, FredMR, GreenRA, VawdreyDK. Designing a clinical dashboard to fill information gaps in the emergency department. AMIA Annu Symp Proc. 2014;2014:1098–104.
3.
StaibA, SullivanC, JonesM, GriffinB, BellA, ScottI. The ED-inpatient dashboard: Uniting emergency and inpatient clinicians to improve the efficiency and quality of care for patients requiring emergency admission to hospital. Emerg Med Australas. 2016. doi: 10.1111/1742-6723.12661
4.
RabanMS, BamfordC, JoolayY, HarrisonMC. Impact of an educational intervention and clinical performance dashboard on neonatal bloodstream infections. S Afr Med J. 2015;105(7):564–6.
5.
ChandraharanE, ArulkumaranS. The role of clinical dashboards in improving patient care: Experience with the ‘Maternity Dashboard’. Ceylon Med J. 2016;61(2):83–5.
6.
Department of Health (DoH). “High quality care for all”, NHS Next Stage Review final report, DoH, London. 2008.
7.
JohnsonJ. Designing with the Mind in Mind: Simple Guide to Understanding User Interface Design Rules. Massachusetts, USAMorgan Kaufmann; 2010.
8.
ChandraharanE. Clinical dashboards: Do they actually work in practice? Three-year experience with the Maternity Dashboard. Clin Risk. 2010;16(5):176–82.
9.
TabriziSJ, LangbehnDR, LeavittBR, RoosRA, DurrA, CraufurdD, et al. Biological and clinical manifestations of Huntington’s disease in the longitudinal TRACK-HD study: Cross-sectional analysis of baseline data. Lancet Neurol. 2009;8(9):791–801.
10.
AndrewSE, GoldbergYP, KremerB, TeleniusH, TheilmannJ, AdamS, et al. The relationship between trinucleotide (CAG) repeat length and clinical features of Huntington’s disease. Nat Genet. 1993;4, 398–403.
11.
BeglingerLJ, DuffK, AllisonJ, TheriaultD, O’RourkeJJ, LesermanA, et al. Cognitive change in patients with Huntington disease on the Repeatable Battery for the Assessment of Neuropsychological Status. J Clin Exp Neuropsychol. 2010;32(6):573–8.
12.
KuanWL, KasisA, YuanY, MasonSL, LazarAS, BarkerRA, et al. Modelling the natural history of Huntington’s disease progression. J Neurol Neurosurg Psychiatry. 2015;86(10):1143–9.
13.
BrandtJ, BylsmaFW, GrossR, StineOC, RanenN, RossCA. Trinucleotide repeat length and clinical progression in Huntington’s disease. Neurology. 1996;46(2):527–31.
14.
RavinaB, RomerM, ConstantinescuR, BiglanK, BrochtA, KieburtzK, et al. The relationship between CAG repeat length and clinical progression in Huntington’s disease. Mov Disord. 2008;23(9):1223–7.
15.
RosenblattA, LiangKY, ZhouH, AbbottMH, GourleyLM, MargolisRL, et al. The association of CAG repeat length with clinical progression in Huntington disease. Neurology. 2006;66(7):1016–20.
16.
MahantN, McCuskerEA, BythK, GrahamS, GroupHS. Huntington’s disease: Clinical correlates of disability and progression. Neurology. 2003;61(8):1085–92.
17.
KieburtzK, MacDonaldM, ShihC, FeiginA, SteinbergK, BordwellK, et al. Trinucleotide repeat length and progression of illnessin Huntington’s disease. J Med Genet. 1994;31(11):872–4.
18.
BadgeleyMA, ShameerK, GlicksbergBS, TomlinsonMS, LevinMA, McCormickPJ, et al. EHDViz: Clinicaldashboard development using open-source technologies. BMJOpen. 2016;6(3):e010579.
19.
MouldDR, UptonRN, WojciechowskiJ. Dashboard systems:Implementing pharmacometrics from bench to bedside. AAPS J. 2014;16(5):925–37.
20.
The Enroll-HD Study Group. Enroll-HD: A prospective registry study in a global Huntington’s disease cohort. A CHDI Foundation Project. Enroll-HD Data Handling Manual, Version 2015-01-R1. 2015.
LongJD, PaulsenJS, MarderK, ZhangY, KimJI, MillsJA, et al. Tracking motor impairments in the progression of Huntington’s disease. Mov Disord. 2014;29(3):311–9.
23.
KirkwoodSC, SuJL, ConneallyP, ForoudT. Progression of symptoms in the early and middle stages of Huntington disease. Arch Neurol. 2001;58(2):273–8.
24.
DorseyER, BeckCA, DarwinK, NicholsP, BrochtAF, BiglanKM, et al. Natural history of Huntington disease. JAMA Neurol. 2013;70(12):1520–30.
25.
LemiereJ, DecruyenaereM, Evers-KieboomsG, VandenbusscheE, DomR. Cognitive changes in patients with Huntington’s disease (HD) and asymptomatic carriers of the HD mutation–a longitudinal follow-up study. J Neurol. 2004;251(8):935–42.
26.
GhoshBC, CarpenterRH, RoweJB. A longitudinal study of motor, oculomotor and cognitive function in progressive supranuclear palsy. PLoS One. 2013;8(9):e74486.
27.
El-NimrG, TamsS. Working together: A one-stop shop for Huntington’s disease. Prog Neurol Psychiatry. 2011;15(1):15–7.
28.
FoxSH, BrotchieJM. Levodopa-induced dyskinesia in Parkinson’s disease. New York City, USA: Springer; 2014.
29.
BeghiE, RegioV, PapantonioA, BentivoglioAR, FasanoA, FogliD, et al. Reliability of clinical diagnosis of dystonia. Neuroepidemiology. 2014;43(3-4):213–9.
30.
NovotnýM, RuszJ, ČmejlaR, RůžičkováH, KlempířJ, RůžičkaE. Hypernasalityassociated with basal ganglia dysfunction: Evidence fromParkinson’s disease and Huntington’s disease. Peer J. 2016;4:e2530.
31.
McCuskerE, RichardsF, SillenceD, WilsonM, TrentRJ. Huntington’s disease: Neurological assessment of potential genecarriers presenting for predictive DNA testing. J Clin Neurosci. 2000;7(1):38–41.
32.
MyersRH, SaxDS, KoroshetzWJ, MastromauroC, CupplesLA, KielyDK, et al. Factors associated with slow progression in Huntington’s disease. Arch Neurol. 1991;48(8):800–4.
33.
TedroffJ, WatersS, BarkerRA, RoosR, SquitieriF, GroupERS. Antidopaminergic medication is associated with more rapidly progressive Huntington’s disease. J Huntingtons Dis. 2015;4(2):131–40.
34.
Consortium GMoHsDG-H. Identification of genetic factors that modify clinical onset of Huntington’s disease. Cell. 2015;162(3):516–26.
35.
de Diego-BalaguerR, SchrammC, RebeixI, DupouxE, DurrA, BriceA, et al. COMT Val158Met polymorphism modulates Huntington’s disease progression. PLoS One. 2016;11(9):e0161106.
36.
FelkeyBG, FoxBI. Health System Dashboard: It’s all coming together. Hosp Pharm. 2014;49(5):485–6.
