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Hobbs NZ, Papoutsi M, Delva A, Kinnunen KM, Nakajima M, Van Laere K, Vandenberghe W, Herath P, Scahill RI. Neuroimaging to Facilitate Clinical Trials in Huntington's Disease: Current Opinion from the EHDN Imaging Working Group. J Huntingtons Dis 2024; 13:163-199. [PMID: 38788082 PMCID: PMC11307036 DOI: 10.3233/jhd-240016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/22/2024] [Indexed: 05/26/2024]
Abstract
Neuroimaging is increasingly being included in clinical trials of Huntington's disease (HD) for a wide range of purposes from participant selection and safety monitoring, through to demonstration of disease modification. Selection of the appropriate modality and associated analysis tools requires careful consideration. On behalf of the EHDN Imaging Working Group, we present current opinion on the utility and future prospects for inclusion of neuroimaging in HD trials. Covering the key imaging modalities of structural-, functional- and diffusion- MRI, perfusion imaging, positron emission tomography, magnetic resonance spectroscopy, and magnetoencephalography, we address how neuroimaging can be used in HD trials to: 1) Aid patient selection, enrichment, stratification, and safety monitoring; 2) Demonstrate biodistribution, target engagement, and pharmacodynamics; 3) Provide evidence for disease modification; and 4) Understand brain re-organization following therapy. We also present the challenges of translating research methodology into clinical trial settings, including equipment requirements and cost, standardization of acquisition and analysis, patient burden and invasiveness, and interpretation of results. We conclude, that with appropriate consideration of modality, study design and analysis, imaging has huge potential to facilitate effective clinical trials in HD.
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Affiliation(s)
- Nicola Z. Hobbs
- HD Research Centre, UCL Institute of Neurology, UCL, London, UK
| | - Marina Papoutsi
- HD Research Centre, UCL Institute of Neurology, UCL, London, UK
- IXICO plc, London, UK
| | - Aline Delva
- Department of Neurosciences, KU Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Belgium
| | | | | | - Koen Van Laere
- Department of Imaging and Pathology, Nuclear Medicine and Molecular Imaging, KU Leuven, Belgium
- Division of Nuclear Medicine, University Hospitals Leuven, Belgium
| | - Wim Vandenberghe
- Department of Neurosciences, KU Leuven, Belgium
- Department of Neurology, University Hospitals Leuven, Belgium
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Morgan KE, White IR, Frost C. How important is the linearity assumption in a sample size calculation for a randomised controlled trial where treatment is anticipated to affect a rate of change? BMC Med Res Methodol 2023; 23:274. [PMID: 37990159 PMCID: PMC10664473 DOI: 10.1186/s12874-023-02093-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 11/03/2023] [Indexed: 11/23/2023] Open
Abstract
BACKGROUND For certain conditions, treatments aim to lessen deterioration over time. A trial outcome could be change in a continuous measure, analysed using a random slopes model with a different slope in each treatment group. A sample size for a trial with a particular schedule of visits (e.g. annually for three years) can be obtained using a two-stage process. First, relevant (co-) variances are estimated from a pre-existing dataset e.g. an observational study conducted in a similar setting. Second, standard formulae are used to calculate sample size. However, the random slopes model assumes linear trajectories with any difference in group means increasing proportionally to follow-up time. The impact of these assumptions failing is unclear. METHODS We used simulation to assess the impact of a non-linear trajectory and/or non-proportional treatment effect on the proposed trial's power. We used four trajectories, both linear and non-linear, and simulated observational studies to calculate sample sizes. Trials of this size were then simulated, with treatment effects proportional or non-proportional to time. RESULTS For a proportional treatment effect and a trial visit schedule matching the observational study, powers are close to nominal even for non-linear trajectories. However, if the schedule does not match the observational study, powers can be above or below nominal levels, with the extent of this depending on parameters such as the residual error variance. For a non-proportional treatment effect, using a random slopes model can lead to powers far from nominal levels. CONCLUSIONS If trajectories are suspected to be non-linear, observational data used to inform power calculations should have the same visit schedule as the proposed trial where possible. Additionally, if the treatment effect is expected to be non-proportional, the random slopes model should not be used. A model allowing trajectories to vary freely over time could be used instead, either as a second line analysis method (bearing in mind that power will be lost) or when powering the trial.
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Affiliation(s)
- Katy E Morgan
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK.
| | - Ian R White
- MRC Clinical Trials Unit at UCL, University College London, London, UK
| | - Chris Frost
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
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Shand C, Markiewicz PJ, Cash DM, Alexander DC, Donohue MC, Barkhof F, Oxtoby NP. Heterogeneity in Preclinical Alzheimer's Disease Trial Cohort Identified by Image-based Data-Driven Disease Progression Modelling. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.07.23285572. [PMID: 36798314 PMCID: PMC9934776 DOI: 10.1101/2023.02.07.23285572] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Importance Undetected biological heterogeneity adversely impacts trials in Alzheimer's disease because rate of cognitive decline - and perhaps response to treatment - differs in subgroups. Recent results show that data-driven approaches can unravel the heterogeneity of Alzheimer's disease progression. The resulting stratification is yet to be leveraged in clinical trials. Objective Investigate whether image-based data-driven disease progression modelling could identify baseline biological heterogeneity in a clinical trial, and whether these subgroups have prognostic or predictive value. Design Screening data from the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) Study collected between April 2014 and December 2017, and longitudinal data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) observational study downloaded in February 2022 were used. Setting The A4 Study is an interventional trial involving 67 sites in the US, Canada, Australia, and Japan. ADNI is a multi-center observational study in North America. Participants Cognitively unimpaired amyloid-positive participants with a 3-Tesla T1-weighted MRI scan. Amyloid positivity was determined using florbetapir PET imaging (in A4) and CSF Aβ(1-42) (in ADNI). Main Outcomes and Measures Regional volumes estimated from MRI scans were used as input to the Subtype and Stage Inference (SuStaIn) algorithm. Outcomes included cognitive test scores and SUVr values from florbetapir and flortaucipir PET. Results We included 1,240 Aβ+ participants (and 407 Aβ- controls) from the A4 Study, and 731 A4-eligible ADNI participants. SuStaIn identified three neurodegeneration subtypes - Typical, Cortical, Subcortical - comprising 523 (42%) individuals. The remainder are designated subtype zero (insufficient atrophy). Baseline PACC scores (A4 primary outcome) were significantly worse in the Cortical subtype (median = -1.27, IQR=[-3.34,0.83]) relative to both subtype zero (median=-0.013, IQR=[-1.85,1.67], P<.0001) and the Subcortical subtype (median=0.03, IQR=[-1.78,1.61], P=.0006). In ADNI, over a four-year period (comparable to A4), greater cognitive decline in the mPACC was observed in both the Typical (-0.23/yr; 95% CI, [-0.41,-0.05]; P=.01) and Cortical (-0.24/yr; [-0.42,-0.06]; P=.009) subtypes, as well as the CDR-SB (Typical: +0.09/yr, [0.06,0.12], P<.0001; and Cortical: +0.07/yr, [0.04,0.10], P<.0001). Conclusions and Relevance In a large secondary prevention trial, our image-based model detected neurodegenerative heterogeneity predictive of cognitive heterogeneity. We argue that such a model is a valuable tool to be considered in future trial design to control for previously undetected variance.
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Affiliation(s)
- Cameron Shand
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Pawel J Markiewicz
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
| | - David M Cash
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Michael C Donohue
- Alzheimer's Therapeutic Research Institute, Keck School of Medicine, University of Southern California, San Diego, USA
| | - Frederik Barkhof
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Department of Radiology and Nuclear Medicine, VU University Medical Center, Amsterdam, Netherlands
| | - Neil P Oxtoby
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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4
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Kinnunen KM, Schwarz AJ, Turner EC, Pustina D, Gantman EC, Gordon MF, Joules R, Mullin AP, Scahill RI, Georgiou-Karistianis N. Volumetric MRI-Based Biomarkers in Huntington's Disease: An Evidentiary Review. Front Neurol 2021; 12:712555. [PMID: 34621236 PMCID: PMC8490802 DOI: 10.3389/fneur.2021.712555] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 08/10/2021] [Indexed: 01/02/2023] Open
Abstract
Huntington's disease (HD) is an autosomal-dominant inherited neurodegenerative disorder that is caused by expansion of a CAG-repeat tract in the huntingtin gene and characterized by motor impairment, cognitive decline, and neuropsychiatric disturbances. Neuropathological studies show that disease progression follows a characteristic pattern of brain atrophy, beginning in the basal ganglia structures. The HD Regulatory Science Consortium (HD-RSC) brings together diverse stakeholders in the HD community—biopharmaceutical industry, academia, nonprofit, and patient advocacy organizations—to define and address regulatory needs to accelerate HD therapeutic development. Here, the Biomarker Working Group of the HD-RSC summarizes the cross-sectional evidence indicating that regional brain volumes, as measured by volumetric magnetic resonance imaging, are reduced in HD and are correlated with disease characteristics. We also evaluate the relationship between imaging measures and clinical change, their longitudinal change characteristics, and within-individual longitudinal associations of imaging with disease progression. This analysis will be valuable in assessing pharmacodynamics in clinical trials and supporting clinical outcome assessments to evaluate treatment effects on neurodegeneration.
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Affiliation(s)
| | - Adam J Schwarz
- Takeda Pharmaceuticals, Ltd., Cambridge, MA, United States
| | | | - Dorian Pustina
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Emily C Gantman
- CHDI Management/CHDI Foundation, Princeton, NJ, United States
| | - Mark F Gordon
- Teva Pharmaceuticals, West Chester, PA, United States
| | | | - Ariana P Mullin
- Critical Path Institute, Tucson, AZ, United States.,Wave Life Sciences, Ltd., Cambridge, MA, United States
| | - Rachael I Scahill
- Huntington's Disease Research Centre, UCL Institute of Neurology, London, United Kingdom
| | - Nellie Georgiou-Karistianis
- School of Psychological Sciences and Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
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Nash S, Morgan KE, Frost C, Mulick A. Power and sample-size calculations for trials that compare slopes over time: Introducing the slopepower command. THE STATA JOURNAL 2021; 21:575-601. [PMID: 37476648 PMCID: PMC7614632 DOI: 10.1177/1536867x211045512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Trials of interventions that aim to slow disease progression may analyze a continuous outcome by comparing its change over time-its slope-between the treated and the untreated group using a linear mixed model. To perform a sample-size calculation for such a trial, one must have estimates of the parameters that govern the between- and within-subject variability in the outcome, which are often unknown. The algebra needed for the sample-size calculation can also be complex for such trial designs. We have written a new user-friendly command, slopepower, that performs sample-size or power calculations for trials that compare slope outcomes. The package is based on linear mixed-model methodology, described for this setting by Frost, Kenward, and Fox (2008, Statistics in Medicine 27: 3717-3731). In the first stage of this approach, slopepower obtains estimates of mean slopes together with variances and covariances from a linear mixed model fit to previously collected user-supplied data. In the second stage, these estimates are combined with user input about the target effectiveness of the treatment and design of the future trial to give an estimate of either a sample size or a statistical power. In this article, we present the slopepower command, briefly explain the methodology behind it, and demonstrate how it can be used to help plan a trial and compare the sample sizes needed for different trial designs.
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Affiliation(s)
- Stephen Nash
- Department of Infectious Disease Epidemiology
London School of Hygiene and Tropical Medicine
London, UK
| | - Katy E. Morgan
- Department of Medical Statistics London School
of Hygiene and Tropical Medicine London, UK
| | - Chris Frost
- Department of Medical Statistics London School
of Hygiene and Tropical Medicine London, UK
| | - Amy Mulick
- Department of Non-communicable Disease
Epidemiology London School of Hygiene and Tropical
Medicine London, UK
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Ghazaleh N, Houghton R, Palermo G, Schobel SA, Wijeratne PA, Long JD. Ranking the Predictive Power of Clinical and Biological Features Associated With Disease Progression in Huntington's Disease. Front Neurol 2021; 12:678484. [PMID: 34093422 PMCID: PMC8176643 DOI: 10.3389/fneur.2021.678484] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Accepted: 04/26/2021] [Indexed: 12/01/2022] Open
Abstract
Huntington's disease (HD) is characterised by a triad of cognitive, behavioural, and motor symptoms which lead to functional decline and loss of independence. With potential disease-modifying therapies in development, there is interest in accurately measuring HD progression and characterising prognostic variables to improve efficiency of clinical trials. Using the large, prospective Enroll-HD cohort, we investigated the relative contribution and ranking of potential prognostic variables in patients with manifest HD. A random forest regression model was trained to predict change of clinical outcomes based on the variables, which were ranked based on their contribution to the prediction. The highest-ranked variables included novel predictors of progression—being accompanied at clinical visit, cognitive impairment, age at diagnosis and tetrabenazine or antipsychotics use—in addition to established predictors, cytosine adenine guanine (CAG) repeat length and CAG-age product. The novel prognostic variables improved the ability of the model to predict clinical outcomes and may be candidates for statistical control in HD clinical studies.
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Affiliation(s)
| | | | | | | | - Peter A Wijeratne
- Department of Computer Science, Centre for Medical Imaging Computing, University College London, London, United Kingdom.,Department of Neurodegenerative Disease, Huntington's Disease Research Centre, Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Jeffrey D Long
- Department of Psychiatry, University of Iowa, Iowa City, IA, United States.,Department of Biostatistics, University of Iowa, Iowa City, IA, United States
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Schwarz AJ. The Use, Standardization, and Interpretation of Brain Imaging Data in Clinical Trials of Neurodegenerative Disorders. Neurotherapeutics 2021; 18:686-708. [PMID: 33846962 PMCID: PMC8423963 DOI: 10.1007/s13311-021-01027-4] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/15/2021] [Indexed: 12/11/2022] Open
Abstract
Imaging biomarkers play a wide-ranging role in clinical trials for neurological disorders. This includes selecting the appropriate trial participants, establishing target engagement and mechanism-related pharmacodynamic effect, monitoring safety, and providing evidence of disease modification. In the early stages of clinical drug development, evidence of target engagement and/or downstream pharmacodynamic effect-especially with a clear relationship to dose-can provide confidence that the therapeutic candidate should be advanced to larger and more expensive trials, and can inform the selection of the dose(s) to be further tested, i.e., to "de-risk" the drug development program. In these later-phase trials, evidence that the therapeutic candidate is altering disease-related biomarkers can provide important evidence that the clinical benefit of the compound (if observed) is grounded in meaningful biological changes. The interpretation of disease-related imaging markers, and comparability across different trials and imaging tools, is greatly improved when standardized outcome measures are defined. This standardization should not impinge on scientific advances in the imaging tools per se but provides a common language in which the results generated by these tools are expressed. PET markers of pathological protein aggregates and structural imaging of brain atrophy are common disease-related elements across many neurological disorders. However, PET tracers for pathologies beyond amyloid β and tau are needed, and the interpretability of structural imaging can be enhanced by some simple considerations to guard against the possible confound of pseudo-atrophy. Learnings from much-studied conditions such as Alzheimer's disease and multiple sclerosis will be beneficial as the field embraces rarer diseases.
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Affiliation(s)
- Adam J Schwarz
- Takeda Pharmaceuticals Ltd., 40 Landsdowne Street, Cambridge, MA, 02139, USA.
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8
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Langbehn DR, Hersch S. Clinical Outcomes and Selection Criteria for Prodromal Huntington's Disease Trials. Mov Disord 2020; 35:2193-2200. [PMID: 32686867 PMCID: PMC7818458 DOI: 10.1002/mds.28222] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 06/04/2020] [Accepted: 06/30/2020] [Indexed: 11/08/2022] Open
Abstract
Background Huntington's disease (HD) develops in individuals with extended cytosine‐adenine‐guanine (CAG) repeats within the huntingtin (HTT) gene, causing neurodegeneration and progressive motor and cognitive symptoms. The inclusion of mutant HTT carriers in whom overt symptoms are not yet fully manifest in therapeutic trials would enable the development of treatments that delay or halt the accumulation of significant disability. Objectives The present analyses assess whether screening prediagnosis (preHD) individuals based on a normalized prognostic index (PIN) score would enable the selection of prodromal preHD subjects in whom longitudinal changes in established outcome measures might provide robust signals. It also compares the relative statistical effect size of longitudinal change for these measures. Methods Individual participant data from 2 studies were used to develop mixed effect linear models to assess longitudinal changes in clinical metrics for participants with preHD and PIN‐stratified subcohorts. Relative effect sizes were calculated in 5 preHD studies and internally normalized to evaluate the strength and consistency of each metric across cohorts. Results Longitudinal modeling data demonstrate the amplification of effect sizes when preHD subcohorts were selected by PIN score thresholds of >0.0 and >0.4. These models and relative effect sizes across 5 studies consistently indicate that the Unified Huntington's Disease Rating Scale total motor score exhibits the greatest change in preHD. Conclusions These analyses suggest that the employment of PIN scores to homogenize and stratify preHD cohorts could improve the efficiency of current outcome measures, the most robust of which is the total motor score. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society
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Affiliation(s)
- Douglas R Langbehn
- Department of Psychiatry, University of Iowa Carver College of Medicine, Iowa City, Iowa, USA
| | - Steven Hersch
- Voyager Therapeutics, Inc., Cambridge, Massachusetts, USA.,Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
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Carlozzi NE, Boileau NR, Chou KL, Ready RE, Cella D, McCormack MK, Miner JA, Dayalu P. HDQLIFE and neuro-QoL physical function measures: Responsiveness in persons with huntington's disease. Mov Disord 2019; 35:326-336. [PMID: 31724237 DOI: 10.1002/mds.27908] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2019] [Revised: 09/26/2019] [Accepted: 10/04/2019] [Indexed: 12/21/2022] Open
Abstract
BACKGROUND Huntington's disease (HD) is a neurological disorder that causes severe motor symptoms that adversely impact health-related quality of life. Patient-reported physical function outcome measures in HD have shown cross-sectional evidence of validity, but responsiveness has not yet been assessed. OBJECTIVES This study evaluates the responsiveness of the Huntington Disease Health-Related Quality of Life (HDQLIFE) and the Quality of Life in Neurological Disorders (Neuro-QoL) physical function measures in persons with HD. METHODS A total of 347 participants completed baseline and at least 1 follow-up (12-month and 24-month) measure (HDQLIFE Chorea, HDQLIFE Swallowing Difficulties, HDQLIFE Speech Difficulties, Neuro-QoL Upper Extremity Function, and/or Neuro-QoL Lower Extremity Function). Of the participants that completed the baseline assessment, 338 (90.9%) completed the 12-month assessment, and 293 (78.8%) completed the 24-month assessment. Standardized response means and general linear models evaluated whether the physical function measures were responsive to self-reported and clinician-rated change over time. RESULTS Small to moderate effect sizes for the standardized response means supported 12-month and 24-month responsiveness of the HDQLIFE and Neuro-QoL measures for those with either self-reported or clinician-rated declines in function. General linear models supported 12-month and 24-month responsiveness for all HRQOL measures relative to self-reported declines in health, but generally only 24-month responsiveness was supported relative to clinician-rated declines in function. CONCLUSIONS Longitudinal analyses indicate that the HDQLIFE and the Neuro-QoL physical function measures are sensitive to change over time in individuals with HD. Thus, these scales exhibit evidence of responsiveness and may be useful outcome measures in future clinical trials. © 2019 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Noelle E Carlozzi
- Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, Michigan, USA
| | - Nicholas R Boileau
- Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, Michigan, USA
| | - Kelvin L Chou
- Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA
| | - Rebecca E Ready
- Department of Psychological and Brain Sciences, University of Massachusetts, Amherst, Massachusetts, USA
| | - David Cella
- Department of Medical Social Sciences, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Michael K McCormack
- Department of Psychiatry, Rutgers University-Robert Wood Johnson Medical School, Piscataway, New Jersey, USA.,Department of Pathology, Rowan University-SOM (School of Medicine), Stratford, New Jersey, USA
| | - Jennifer A Miner
- Department of Physical Medicine and Rehabilitation, University of Michigan, Ann Arbor, Michigan, USA
| | - Praveen Dayalu
- Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA
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Gordon MF, Grachev ID, Mazeh I, Dolan Y, Reilmann R, Loupe PS, Fine S, Navon-Perry L, Gross N, Papapetropoulos S, Savola JM, Hayden MR. Quantification of Motor Function in Huntington Disease Patients Using Wearable Sensor Devices. Digit Biomark 2019; 3:103-115. [PMID: 32095771 DOI: 10.1159/000502136] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2019] [Accepted: 07/16/2019] [Indexed: 01/17/2023] Open
Abstract
Previous studies have demonstrated the feasibility and promise of wearable sensors as objective measures of motor impairment in Parkinson disease and essential tremor. However, there are few published studies that have examined such an application in Huntington disease (HD). This report provides an evaluation of the potential to objectively quantify chorea in HD patients using wearable sensor data. Data were derived from a substudy of the phase 2 Open-PRIDE-HD study, where 17 patients were screened and 15 patients enrolled in the substudy and ultimately 10 patients provided sufficient wearable sensor data. The substudy was designed to provide high-resolution data to inform design of predictive algorithms for chorea quantification. During the entire course of the 6-month study, in addition to chorea ratings from 18 in-clinic assessments, 890 home assessments, and 1,388 responses to daily reminders, 33,000 h of high-resolution accelerometer data were captured continuously from wearable smartwatches and smartphones. Despite its limited sample size, our study demonstrates that arm chorea can be characterized using accelerometer data during static assessments. Nonetheless, the small sample size limits the generalizability of the model. The sensor-based model can quantify the chorea level with high correlation to the chorea severity reported by both clinicians and patients. In addition, our analysis shows that the chorea digital signature varies between patients. This work suggests that digital wearable sensors have the potential to support clinical development of medications in patients with movement disorders, such as chorea. However, additional data would be needed from a larger number of HD patients with a full range of chorea severity (none to severe) with and without intervention to validate this potentially predictive technology.
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Affiliation(s)
- Mark Forrest Gordon
- Specialty Clinical Development, Teva Pharmaceutical Industries Ltd., Frazer, Pennsylvania, USA
| | - Igor D Grachev
- Guide Pharmaceutical Consulting, LLC, Millstone Township, New Jersey, USA
| | - Itzik Mazeh
- Advanced Analytics Department, Intel, Petah Tikva, Israel
| | - Yonatan Dolan
- Advanced Analytics Department, Intel, Petah Tikva, Israel
| | - Ralf Reilmann
- George Huntington Institute and Department of Radiology University of Münster, Münster, Germany.,Department of Neurodegenerative Diseases and Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Pippa S Loupe
- Specialty Research and Development, Teva Pharmaceutical Industries Ltd., New Orleans, Louisiana, USA
| | - Shai Fine
- Data Science Institute, Interdisciplinary Center, Herzliya, Israel
| | | | - Nicholas Gross
- Specialty Clinical Development, Teva Pharmaceutical Industries Ltd., Frazer, Pennsylvania, USA
| | | | | | - Michael R Hayden
- Global Research and Development, Teva Pharmaceutical Industries Ltd., Petah Tikva, Israel.,Prilenia Therapeutics, Herzliya, Israel
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