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Szmulewicz DJ, Galli R, Tarnutzer AA. Patient-Related Outcome Measures for Oculomotor Symptoms in the Cerebellar Ataxias: Insights from Non-Cerebellar Disorders. CEREBELLUM (LONDON, ENGLAND) 2024; 23:1435-1448. [PMID: 38214833 PMCID: PMC11269357 DOI: 10.1007/s12311-024-01656-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 01/05/2024] [Indexed: 01/13/2024]
Abstract
In patients with cerebellar ataxia (CA), symptoms related to oculomotor dysfunction significantly affect quality of life (QoL). This study aimed to analyze the literature on patient-related outcome measures (PROMs) assessing QoL impacts of vestibular and cerebellar oculomotor abnormalities in patients with CA to identify the strengths and limitations of existing scales and highlight any areas of unmet need. A systematic review was conducted (Medline, Embase) of English-language original articles reporting on QoL measures in patients with vertigo, dizziness or CA. Pre-specified parameters were retrieved, including diseases studied, scales applied and conclusions drawn. Our search yielded 3671 articles of which 467 studies (n = 111,606 participants) were deemed relevant. The most frequently studied disease entities were (a) non-specific dizziness/gait imbalance (114 studies; 54,581 participants), (b) vestibular schwannomas (66; 15,360), and (c) vestibular disorders not further specified (66; 10,259). The Dizziness Handicap Inventory (DHI) was the most frequently used PROM to assess QoL (n = 91,851), followed by the Penn Acoustic Neuroma Quality-of-Life Scale (n = 12,027) and the Activities-Specific Balance Confidence Scale (n = 2'471). QoL-scores capturing symptoms related to oculomotor abnormalities in CA were rare, focused on visual impairments (e.g., National-Eye-Institute Visual Function Questionnaire, Oscillopsia Functional Impact, oscillopsia severity score) and were unvalidated. The DHI remains the most widely used and versatile scale for evaluating dizziness. A lack of well-established PROMs for assessing the impact of oculomotor-related symptoms on QoL in CA was noted, emphasizing the need for developing and validating a new QoL-score dedicated to the oculomotor domain for individuals with CA.
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Affiliation(s)
- David J Szmulewicz
- Balance Disorders and Ataxia Service, Royal Victoria Eye and Ear Hospital, Melbourne, VIC, Australia
- The Bionics Institute, Melbourne, VIC, Australia
- University of Melbourne AU, Melbourne, VIC, Australia
| | - Rocco Galli
- Faculty of Medicine, University of Zurich, Zurich, Switzerland
| | - Alexander A Tarnutzer
- Faculty of Medicine, University of Zurich, Zurich, Switzerland.
- Department of Neurology, Cantonal Hospital of Baden, Baden, Switzerland.
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Németh AH, Antoniades CA, Dukart J, Minnerop M, Rentz C, Schuman BJ, van de Warrenburg B, Willemse I, Bertini E, Gupta AS, de Mello Monteiro CB, Almoajil H, Quinn L, Perlman SB, Horak F, Ilg W, Traschütz A, Vogel AP, Dawes H. Using Smartphone Sensors for Ataxia Trials: Consensus Guidance by the Ataxia Global Initiative Working Group on Digital-Motor Biomarkers. CEREBELLUM (LONDON, ENGLAND) 2024; 23:912-923. [PMID: 38015365 PMCID: PMC11102363 DOI: 10.1007/s12311-023-01608-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 09/25/2023] [Indexed: 11/29/2023]
Abstract
Smartphone sensors are used increasingly in the assessment of ataxias. To date, there is no specific consensus guidance regarding a priority set of smartphone sensor measurements, or standard assessment criteria that are appropriate for clinical trials. As part of the Ataxia Global Initiative Digital-Motor Biomarkers Working Group (AGI WG4), aimed at evaluating key ataxia clinical domains (gait/posture, upper limb, speech and oculomotor assessments), we provide consensus guidance for use of internal smartphone sensors to assess key domains. Guidance was developed by means of a literature review and a two stage Delphi study conducted by an Expert panel, which surveyed members of AGI WG4, representing clinical, research, industry and patient-led experts, and consensus meetings by the Expert panel to agree on standard criteria and map current literature to these criteria. Seven publications were identified that investigated ataxias using internal smartphone sensors. The Delphi 1 survey ascertained current practice, and systems in use or under development. Wide variations in smartphones sensor use for assessing ataxia were identified. The Delphi 2 survey identified seven measures that were strongly endorsed as priorities in assessing 3/4 domains, namely gait/posture, upper limb, and speech performance. The Expert panel recommended 15 standard criteria to be fulfilled in studies. Evaluation of current literature revealed that none of the studies met all criteria, with most being early-phase validation studies. Our guidance highlights the importance of consensus, identifies priority measures and standard criteria, and will encourage further research into the use of internal smartphone sensors to measure ataxia digital-motor biomarkers.
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Affiliation(s)
- Andrea H Németh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Oxford Centre for Genomic Medicine, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Chrystalina A Antoniades
- Neurometrology Group, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Juergen Dukart
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
- Institute of Systems Neuroscience, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Martina Minnerop
- Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Neurology, Center for Movement Disorders and Neuromodulation, Medical Faculty & University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, (INM-1), Research Centre Jülich, Jülich, Germany
| | - Clara Rentz
- Institute of Neuroscience and Medicine, (INM-1), Research Centre Jülich, Jülich, Germany
| | | | - Bart van de Warrenburg
- Department of Neurology, Donders Institute for Brain, Cognition, and Behaviour, Radboud University Medical Center, 6525, Nijmegen, Netherlands
| | - Ilse Willemse
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Enrico Bertini
- Unit of Neuromuscular and Neurodegenerative Disorders, Dept Neurosciences, Bambino Gesu' Children's Research Hospital, IRCCS, Rome, Italy
| | - Anoopum S Gupta
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Carlos Bandeira de Mello Monteiro
- Faculty of Medicine, University of São Paulo, São Paulo, SP, Brazil
- School of Arts, Science and Humanities, University of São Paulo, São Paulo, SP, Brazil
| | - Hajar Almoajil
- Physical Therapy Department, College of Applied Medical Sciences, Imam Abdulrahman Bin Faisal University, Damman, Saudi Arabia
| | - Lori Quinn
- Department of Biobehavioral Sciences, Teachers College, Columbia University, New York, NY, USA
| | | | - Fay Horak
- Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- APDM Precision Motion, Clario, Portland, OR, USA
| | - Winfried Ilg
- Section Computational Sensomotorics, Hertie Institute for Clinical Brain Research, Tübingen, Germany
- Centre for Integrative Neuroscience (CIN), Tübingen, Germany
| | - Andreas Traschütz
- Research Division "Translational Genomics of Neurodegenerative Diseases", Hertie Institute for Clinical Brain Research and Center of Neurology, University of Tübingen, Tübingen, Germany
- German Center for Neurodegenerative Diseases (DZNE), University of Tübingen, Tübingen, Germany
| | - Adam P Vogel
- Centre for Neuroscience of Speech, The University of Melbourne, Melbourne, Australia
- Division of Translational Genomics of Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Center for Neurology, University Hospital Tübingen, Tübingen, Germany
- Redenlab Inc, Melbourne, Australia
| | - Helen Dawes
- NIHR Exeter Biomedical Research Centre, Medical School, Faculty of Health and Life Sciences, College of Medicine and Health, St Lukes Campus, University of Exeter, Heavitree Road, Exeter, UK.
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Lopergolo D, Rosini F, Pretegiani E, Bargagli A, Serchi V, Rufa A. Autosomal recessive cerebellar ataxias: a diagnostic classification approach according to ocular features. Front Integr Neurosci 2024; 17:1275794. [PMID: 38390227 PMCID: PMC10883068 DOI: 10.3389/fnint.2023.1275794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Accepted: 11/10/2023] [Indexed: 02/24/2024] Open
Abstract
Autosomal recessive cerebellar ataxias (ARCAs) are a heterogeneous group of neurodegenerative disorders affecting primarily the cerebellum and/or its afferent tracts, often accompanied by damage of other neurological or extra-neurological systems. Due to the overlap of clinical presentation among ARCAs and the variety of hereditary, acquired, and reversible etiologies that can determine cerebellar dysfunction, the differential diagnosis is challenging, but also urgent considering the ongoing development of promising target therapies. The examination of afferent and efferent visual system may provide neurophysiological and structural information related to cerebellar dysfunction and neurodegeneration thus allowing a possible diagnostic classification approach according to ocular features. While optic coherence tomography (OCT) is applied for the parametrization of the optic nerve and macular area, the eye movements analysis relies on a wide range of eye-tracker devices and the application of machine-learning techniques. We discuss the results of clinical and eye-tracking oculomotor examination, the OCT findings and some advancing of computer science in ARCAs thus providing evidence sustaining the identification of robust eye parameters as possible markers of ARCAs.
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Affiliation(s)
- Diego Lopergolo
- Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy
- UOC Neurologia e Malattie Neurometaboliche, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Francesca Rosini
- UOC Stroke Unit, Department of Emergenza-Urgenza, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Elena Pretegiani
- Unit of Neurology, Centre Hospitalier Universitaire Vaudoise Lausanne, Unit of Neurology and Cognitive Neurorehabilitation, Universitary Hospital of Fribourg, Fribourg, Switzerland
| | - Alessia Bargagli
- Evalab-Neurosense, Department of Medicine Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Valeria Serchi
- Evalab-Neurosense, Department of Medicine Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Alessandra Rufa
- Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy
- UOC Neurologia e Malattie Neurometaboliche, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
- Evalab-Neurosense, Department of Medicine Surgery and Neuroscience, University of Siena, Siena, Italy
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