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Petros FE, Santos AM, Adeniyi A, Teruya S, De Los Santos J, Maurer MS, Agrawal SK. Gait abnormalities in older adults with transthyretin cardiac amyloidosis. Amyloid 2024; 31:116-123. [PMID: 38433466 PMCID: PMC11116048 DOI: 10.1080/13506129.2024.2319133] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Accepted: 02/10/2024] [Indexed: 03/05/2024]
Abstract
BACKGROUND Transthyretin cardiac amyloidosis (ATTR cardiac amyloidosis) is caused by variant (ATTRv) or wild type (ATTRwt) transthyretin. While gait abnormalities have been studied in younger patients with ATTRv amyloidosis, research on gait in older adults with ATTR cardiac amyloidosis is lacking. Given ATTR cardiac amyloidosis' association with neuropathy and orthopedic manifestations, we explore the gait in this population. METHODS Twenty-eight older male ATTR cardiac amyloidosis patients and 11 healthy older male controls walked overground with and without a dual cognitive task. Gait parameters: stride width, length, velocity and stance time percentage were measured using an instrumented mat. ATTR amyloidosis patients were further categorized based on clinical and functional assessments. RESULTS We found significant gait differences between ATTR cardiac amyloidosis patients and healthy controls; patients had more variable, slower, narrower and shorter strides, with their feet spending more time in contact with the ground as opposed to in swing. However, the observed gait differences did not correlate with clinical and functional measures of ATTR cardiac amyloidosis severity. CONCLUSIONS Our results suggest that gait analysis could be a complementary tool for characterizing ATTR cardiac amyloidosis patients and may inform clinical care as it relates to falls, management of anticoagulation, and functional independence.
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Affiliation(s)
- Fitsum E Petros
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
| | | | - Adedeji Adeniyi
- Vagelos College of Physicians & Surgeons, Irvine Medical Center, Columbia University, New York, NY, USA
| | - Sergio Teruya
- Department of Medicine, Division of Cardiology, Columbia University, New York, NY, USA
| | - Jeffeny De Los Santos
- Department of Medicine, Division of Cardiology, Columbia University, New York, NY, USA
| | - Mathew S Maurer
- Department of Medicine, Division of Cardiology, Columbia University, New York, NY, USA
| | - Sunil K Agrawal
- Department of Mechanical Engineering, Columbia University, New York, NY, USA
- Rehabilitation and Regenerative Medicine, Columbia University, New York, NY, USA
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Dasgupta NR. Care of Patients With Transthyretin Amyloidosis: the Roles of Nutrition, Supplements, Exercise, and Mental Health. Am J Cardiol 2022; 185 Suppl 1:S35-S42. [PMID: 36549789 DOI: 10.1016/j.amjcard.2022.10.053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/03/2022] [Revised: 10/24/2022] [Accepted: 10/27/2022] [Indexed: 12/24/2022]
Abstract
Transthyretin (ATTR) amyloidosis is a debilitating disease that results in organ failure and eventual death. As the disease progresses, patients experience neurologic, gastrointestinal, and cardiovascular symptoms that increasingly compromise their nutritional status and exercise capacity. These symptoms cause considerable emotional stress and mental health challenges for patients and caregivers. This review summarizes common symptoms and mechanisms associated with malnutrition and exercise intolerance, and sources of emotional stress, and offers therapeutic strategies to address these issues. Although earlier diagnosis and disease-specific treatment are central to caring for patients with ATTR amyloidosis, additional attention to symptom-focused treatments to improve nutritional status, maintain exercise tolerance and capacity, and improve and maintain mental health are also important. In conclusion, a team-based approach involving multiple clinicians and providers can offer more comprehensive and coordinated care, support, and education for patients and caregivers.
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Affiliation(s)
- Noel R Dasgupta
- Department of Cardiology, Indiana University School of Medicine, Indianapolis, Indiana.
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Vilas-Boas MDC, Fonseca PFP, Sousa IM, Cardoso MN, Cunha JPS, Coelho T. Gait Characterization and Analysis of Hereditary Amyloidosis Associated with Transthyretin Patients: A Case Series. J Clin Med 2022; 11:3967. [PMID: 35887731 PMCID: PMC9320786 DOI: 10.3390/jcm11143967] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 06/30/2022] [Accepted: 07/02/2022] [Indexed: 02/04/2023] Open
Abstract
Hereditary amyloidosis associated with transthyretin (ATTRv), is a rare autosomal dominant disease characterized by length-dependent symmetric polyneuropathy that has gait impairment as one of its consequences. The gait pattern of V30M ATTRv amyloidosis patients has been described as similar to that of diabetic neuropathy, associated with steppage, but has never been quantitatively characterized. In this study we aim to characterize the gait pattern of patients with V30M ATTRv amyloidosis, thus providing information for a better understanding and potential for supporting diagnosis and disease progression evaluation. We present a case series in which we conducted two gait analyses, 18 months apart, of five V30M ATTRv amyloidosis patients using a 12-camera, marker based, optical system as well as six force platforms. Linear kinematics, ground reaction forces, and angular kinematics results are analyzed for all patients. All patients, except one, showed a delayed toe-off in the second assessment, as well as excessive pelvic rotation, hip extension and external transverse rotation and knee flexion (in stance and swing phases), along with reduced vertical and mediolateral ground reaction forces. The described gait anomalies are not clinically quantified; thus, gait analysis may contribute to the assessment of possible disease progression along with the clinical evaluation.
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Affiliation(s)
- Maria do Carmo Vilas-Boas
- Centro Hospitalar Universitário do Porto, Hospital Santo António, Unidade Corino de Andrade, E.P.E., Largo do Prof. Abel Salazar, 4099-001 Porto, Portugal; (M.N.C.); (T.C.)
- INESC TEC (Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência), FEUP (Faculdade de Engenharia da Universidade do Porto), University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal;
| | - Pedro Filipe Pereira Fonseca
- LABIOMEP: Porto Biomechanics Laboratory, University of Porto, R. Dr. Plácido de Costa, 91, 4200-450 Porto, Portugal; (P.F.P.F.); (I.M.S.)
| | - Inês Martins Sousa
- LABIOMEP: Porto Biomechanics Laboratory, University of Porto, R. Dr. Plácido de Costa, 91, 4200-450 Porto, Portugal; (P.F.P.F.); (I.M.S.)
- Escola Superior de Biotecnologia, Universidade Católica Portuguesa Rua de Diogo Botelho, 1327, 4169-005 Porto, Portugal
| | - Márcio Neves Cardoso
- Centro Hospitalar Universitário do Porto, Hospital Santo António, Unidade Corino de Andrade, E.P.E., Largo do Prof. Abel Salazar, 4099-001 Porto, Portugal; (M.N.C.); (T.C.)
| | - João Paulo Silva Cunha
- INESC TEC (Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciência), FEUP (Faculdade de Engenharia da Universidade do Porto), University of Porto, R. Dr. Roberto Frias, 4200-465 Porto, Portugal;
- LABIOMEP: Porto Biomechanics Laboratory, University of Porto, R. Dr. Plácido de Costa, 91, 4200-450 Porto, Portugal; (P.F.P.F.); (I.M.S.)
| | - Teresa Coelho
- Centro Hospitalar Universitário do Porto, Hospital Santo António, Unidade Corino de Andrade, E.P.E., Largo do Prof. Abel Salazar, 4099-001 Porto, Portugal; (M.N.C.); (T.C.)
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Scott B, Seyres M, Philp F, Chadwick EK, Blana D. Healthcare applications of single camera markerless motion capture: a scoping review. PeerJ 2022; 10:e13517. [PMID: 35642200 PMCID: PMC9148557 DOI: 10.7717/peerj.13517] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/09/2022] [Indexed: 01/17/2023] Open
Abstract
Background Single camera markerless motion capture has the potential to facilitate at home movement assessment due to the ease of setup, portability, and affordable cost of the technology. However, it is not clear what the current healthcare applications of single camera markerless motion capture are and what information is being collected that may be used to inform clinical decision making. This review aims to map the available literature to highlight potential use cases and identify the limitations of the technology for clinicians and researchers interested in the collection of movement data. Survey Methodology Studies were collected up to 14 January 2022 using Pubmed, CINAHL and SPORTDiscus using a systematic search. Data recorded included the description of the markerless system, clinical outcome measures, and biomechanical data mapped to the International Classification of Functioning, Disability and Health Framework (ICF). Studies were grouped by patient population. Results A total of 50 studies were included for data collection. Use cases for single camera markerless motion capture technology were identified for Neurological Injury in Children and Adults; Hereditary/Genetic Neuromuscular Disorders; Frailty; and Orthopaedic or Musculoskeletal groups. Single camera markerless systems were found to perform well in studies involving single plane measurements, such as in the analysis of infant general movements or spatiotemporal parameters of gait, when evaluated against 3D marker-based systems and a variety of clinical outcome measures. However, they were less capable than marker-based systems in studies requiring the tracking of detailed 3D kinematics or fine movements such as finger tracking. Conclusions Single camera markerless motion capture offers great potential for extending the scope of movement analysis outside of laboratory settings in a practical way, but currently suffers from a lack of accuracy where detailed 3D kinematics are required for clinical decision making. Future work should therefore focus on improving tracking accuracy of movements that are out of plane relative to the camera orientation or affected by occlusion, such as supination and pronation of the forearm.
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Affiliation(s)
- Bradley Scott
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
| | - Martin Seyres
- School of Engineering, University of Aberdeen, Aberdeen, United Kingdom
| | - Fraser Philp
- School of Health Sciences, University of Liverpool, Liverpool, United Kingdom
| | | | - Dimitra Blana
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, United Kingdom
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Vilas-Boas MDC, Rocha AP, Cardoso MN, Fernandes JM, Coelho T, Cunha JPS. Supporting the Assessment of Hereditary Transthyretin Amyloidosis Patients Based On 3-D Gait Analysis and Machine Learning. IEEE Trans Neural Syst Rehabil Eng 2021; 29:1350-1362. [PMID: 34252029 DOI: 10.1109/tnsre.2021.3096433] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Hereditary Transthyretin Amyloidosis (vATTR-V30M) is a rare and highly incapacitating sensorimotor neuropathy caused by an inherited mutation (Val30Met), which typically affects gait, among other symptoms. In this context, we investigated the possibility of using machine learning (ML) techniques to build a model(s) that can be used to support the detection of the Val30Met mutation (possibility of developing the disease), as well as symptom onset detection for the disease, given the gait characteristics of a person. These characteristics correspond to 24 gait parameters computed from 3-D body data, provided by a Kinect v2 camera, acquired from a person while walking towards the camera. To build the model(s), different ML algorithms were explored: k-nearest neighbors, decision tree, random forest, support vector machines (SVM), and multilayer perceptron. For a dataset corresponding to 66 subjects (25 healthy controls, 14 asymptomatic mutation carriers, and 27 patients) and several gait cycles per subject, we were able to obtain a model that distinguishes between controls and vATTR-V30M mutation carriers (with or without symptoms) with a mean accuracy of 92% (SVM). We also obtained a model that distinguishes between asymptomatic and symptomatic carriers with a mean accuracy of 98% (SVM). These results are very relevant, since this is the first study that proposes a ML approach to support vATTR-V30M patient assessment based on gait, being a promising foundation for the development of a computer-aided diagnosis tool to help clinicians in the identification and follow-up of this disease. Furthermore, the proposed method may also be used for other neuropathies.
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