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Ferreira VR, Brayne C, Ragonese P, Ketzoian C, Piccioli M, Tinti L, Casali C, di Lorenzo C, Ramos C, Azevedo J, Gomes A, Stewart R, Haas H, Hoppenbrouwer S, Metting E, Gallo V. A Delphi consensus to identify the key screening tests/questions for a digital neurological examination for epidemiological research. J Neurol 2024; 271:2694-2703. [PMID: 38378908 PMCID: PMC11055750 DOI: 10.1007/s00415-024-12254-y] [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: 12/15/2023] [Revised: 02/06/2024] [Accepted: 02/11/2024] [Indexed: 02/22/2024]
Abstract
BACKGROUND Most neurological diseases have no curative treatment; therefore, focusing on prevention is key. Continuous research to uncover the protective and risk factors associated with different neurological diseases is crucial to successfully inform prevention strategies. eHealth has been showing promising advantages in healthcare and public health and may therefore be relevant to facilitate epidemiological studies. OBJECTIVE In this study, we performed a Delphi consensus exercise to identify the key screening tests to inform the development of a digital neurological examination tool for epidemiological research. METHODS Twelve panellists (six experts in neurological examination, five experts in data collection-two were also experts in the neurological examination, and three experts in participant experience) of different nationalities joined the Delphi exercise. Experts in the neurological examination provided a selection of items that allow ruling out neurological impairment and can be performed by trained health workers. The items were then rated by them and other experts in terms of their feasibility and acceptability. RESULTS Ten tests and seven anamnestic questions were included in the final set of screening items for the digital neurological examination. Three tests and five anamnestic questions were excluded from the final selection due to their low ratings on feasibility. CONCLUSION This work identifies the key feasible and acceptable screening tests and anamnestic questions to build an electronic tool for performing the neurological examination, in the absence of a neurologist.
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Affiliation(s)
- Vasco Ribeiro Ferreira
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands.
| | - Carol Brayne
- Cambridge Public Health, University of Cambridge, Cambridge, United Kingdom
| | - Paolo Ragonese
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University of Palermo, Palermo, Italy
| | - Carlos Ketzoian
- Institute of Neurology, School of Medicine, Neuroepidemiology Section, University of the Republic, Montevideo, Uruguay
| | - Marta Piccioli
- UOC of Neurology, PO San Filippo Neri, ASL Roma 1, Rome, Italy
| | - Lorenzo Tinti
- Laboratory of Neurology, Mario Negri Institute for Pharmacological Research (IRCCS), Milan, Italy
| | - Carlo Casali
- Department of Medico-Surgical Sciences and Biotechnologies (SBMC), University Rome Sapienza, Rome, Italy
| | - Cherubino di Lorenzo
- Department of Medico-Surgical Sciences and Biotechnologies (SBMC), University Rome Sapienza, Rome, Italy
| | - Claudia Ramos
- Grupo de Neurociencias de Antioquia (GNA), Faculty of Medicine, University of Antioquia, Medellín, Colombia
- Grupo de Neuropsicología y Conducta (GRUNECO), Faculty of Medicine, University of Antioquia, Medellín, Colombia
| | - João Azevedo
- Agrupamento de Centros de Saúde de Gaia, Unidade de Saúde Familiar Nova Salus, Vila Nova de Gaia, Portugal
| | | | | | - Hein Haas
- Parkinson Vereniging, Bunnik, The Netherlands
| | | | - Esther Metting
- University Medical Center Groningen, Groningen, The Netherlands
- Faculty of Economics and Business, University of Groningen, Groningen, The Netherlands
| | - Valentina Gallo
- Department of Sustainable Health, University of Groningen, Campus Fryslân, Wirdumerdijk 34, 8911 CE, Leeuwarden, The Netherlands
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Kosa P, Barbour C, Varosanec M, Wichman A, Sandford M, Greenwood M, Bielekova B. Molecular models of multiple sclerosis severity identify heterogeneity of pathogenic mechanisms. Nat Commun 2022; 13:7670. [PMID: 36509784 PMCID: PMC9744737 DOI: 10.1038/s41467-022-35357-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 11/29/2022] [Indexed: 12/14/2022] Open
Abstract
While autopsy studies identify many abnormalities in the central nervous system (CNS) of subjects dying with neurological diseases, without their quantification in living subjects across the lifespan, pathogenic processes cannot be differentiated from epiphenomena. Using machine learning (ML), we searched for likely pathogenic mechanisms of multiple sclerosis (MS). We aggregated cerebrospinal fluid (CSF) biomarkers from 1305 proteins, measured blindly in the training dataset of untreated MS patients (N = 129), into models that predict past and future speed of disability accumulation across all MS phenotypes. Healthy volunteers (N = 24) data differentiated natural aging and sex effects from MS-related mechanisms. Resulting models, validated (Rho 0.40-0.51, p < 0.0001) in an independent longitudinal cohort (N = 98), uncovered intra-individual molecular heterogeneity. While candidate pathogenic processes must be validated in successful clinical trials, measuring them in living people will enable screening drugs for desired pharmacodynamic effects. This will facilitate drug development making, it hopefully more efficient and successful.
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Affiliation(s)
- Peter Kosa
- grid.94365.3d0000 0001 2297 5165Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Christopher Barbour
- grid.94365.3d0000 0001 2297 5165Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Mihael Varosanec
- grid.94365.3d0000 0001 2297 5165Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Alison Wichman
- grid.94365.3d0000 0001 2297 5165Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Mary Sandford
- grid.94365.3d0000 0001 2297 5165Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
| | - Mark Greenwood
- grid.41891.350000 0001 2156 6108Department of Mathematical Sciences, Montana State University, Bozeman, MT USA
| | - Bibiana Bielekova
- grid.94365.3d0000 0001 2297 5165Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD USA
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Kim Y, Varosanec M, Kosa P, Bielekova B. Confounder-adjusted MRI-based predictors of multiple sclerosis disability. FRONTIERS IN RADIOLOGY 2022; 2:971157. [PMID: 37492673 PMCID: PMC10365278 DOI: 10.3389/fradi.2022.971157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/02/2022] [Indexed: 07/27/2023]
Abstract
Introduction Both aging and multiple sclerosis (MS) cause central nervous system (CNS) atrophy. Excess brain atrophy in MS has been interpreted as "accelerated aging." Current paper tests an alternative hypothesis: MS causes CNS atrophy by mechanism(s) different from physiological aging. Thus, subtracting effects of physiological confounders on CNS structures would isolate MS-specific effects. Methods Standardized brain MRI and neurological examination were acquired prospectively in 646 participants enrolled in ClinicalTrials.gov Identifier: NCT00794352 protocol. CNS volumes were measured retrospectively, by automated Lesion-TOADS algorithm and by Spinal Cord Toolbox, in a blinded fashion. Physiological confounders identified in 80 healthy volunteers were regressed out by stepwise multiple linear regression. MS specificity of confounder-adjusted MRI features was assessed in non-MS cohort (n = 158). MS patients were randomly split into training (n = 277) and validation (n = 131) cohorts. Gradient boosting machine (GBM) models were generated in MS training cohort from unadjusted and confounder-adjusted CNS volumes against four disability scales. Results Confounder adjustment highlighted MS-specific progressive loss of CNS white matter. GBM model performance decreased substantially from training to cross-validation, to independent validation cohorts, but all models predicted cognitive and physical disability with low p-values and effect sizes that outperform published literature based on recent meta-analysis. Models built from confounder-adjusted MRI predictors outperformed models from unadjusted predictors in the validation cohort. Conclusion GBM models from confounder-adjusted volumetric MRI features reflect MS-specific CNS injury, and due to stronger correlation with clinical outcomes compared to brain atrophy these models should be explored in future MS clinical trials.
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Kosa P, Masvekar R, Komori M, Phillips J, Ramesh V, Varosanec M, Sandford M, Bielekova B. Enhancing clinical value of serum neurofilament light chain measurement. JCI Insight 2022; 7:161415. [PMID: 35737460 PMCID: PMC9462467 DOI: 10.1172/jci.insight.161415] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Accepted: 06/20/2022] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Serum neurofilament light chain (sNFL) is becoming an important biomarker of neuro-axonal injury. Though sNFL correlates with CSF NFL (cNFL), 40% to 60% of variance remains unexplained. We aimed to mathematically adjust sNFL to strengthen its clinical value. METHODS We measured NFL in a blinded fashion in 1138 matched CSF and serum samples from 571 patients. Multiple linear regression (MLR) models constructed in the training cohort were validated in an independent cohort. RESULTS An MLR model that included age, blood urea nitrogen, alkaline phosphatase, creatinine, and weight improved correlations of cNFL with sNFL (from R2 = 0.57 to 0.67). Covariate adjustment significantly improved the correlation of sNFL with the number of contrast-enhancing lesions (from R2 = 0.18 to 0.28; 36% improvement) in the validation cohort of patients with multiple sclerosis (MS). Unexpectedly, only sNFL, but not cNFL, weakly but significantly correlated with cross-sectional MS severity outcomes. Investigating 2 nonoverlapping hypotheses, we showed that patients with proportionally higher sNFL to cNFL had higher clinical and radiological evidence of spinal cord (SC) injury and probably released NFL from peripheral axons into blood, bypassing the CSF. CONCLUSION sNFL captures 2 sources of axonal injury, central and peripheral, the latter reflecting SC damage, which primarily drives disability progression in MS. TRIAL REGISTRATION ClinicalTrials.gov NCT00794352. FUNDING Division of Intramural Research, National Institute of Allergy and Infectious Diseases, NIH (AI001242 and AI001243).
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Affiliation(s)
- Peter Kosa
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and , NIAD/NIH, Bethesda, United States of America
| | - Ruturaj Masvekar
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and , NIAD/NIH, Bethesda, United States of America
| | - Mika Komori
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and , NIAD/NIH, Bethesda, United States of America
| | - Jonathan Phillips
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and , NIAD/NIH, Bethesda, United States of America
| | - Vighnesh Ramesh
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and , NIAD/NIH, Bethesda, United States of America
| | - Mihael Varosanec
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and , NIAD/NIH, Bethesda, United States of America
| | - Mary Sandford
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and , NIAD/NIH, Bethesda, United States of America
| | - Bibiana Bielekova
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and , NIAD/NIH, Bethesda, United States of America
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Messan KS, Pham L, Harris T, Kim Y, Morgan V, Kosa P, Bielekova B. Assessment of Smartphone-Based Spiral Tracing in Multiple Sclerosis Reveals Intra-Individual Reproducibility as a Major Determinant of the Clinical Utility of the Digital Test. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 3:714682. [PMID: 35178527 PMCID: PMC8844508 DOI: 10.3389/fmedt.2021.714682] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 11/12/2021] [Indexed: 11/13/2022] Open
Abstract
Technological advances, lack of medical professionals, high cost of face-to-face encounters, and disasters such as the COVID-19 pandemic fuel the telemedicine revolution. Numerous smartphone apps have been developed to measure neurological functions. However, their psychometric properties are seldom determined. It is unclear which designs underlie the eventual clinical utility of the smartphone tests. We have developed the smartphone Neurological Function Tests Suite (NeuFun-TS) and are systematically evaluating their psychometric properties against the gold standard of complete neurological examination digitalized into the NeurExTM app. This article examines the fifth and the most complex NeuFun-TS test, the "Spiral tracing." We generated 40 features in the training cohort (22 healthy donors [HD] and 89 patients with multiple sclerosis [MS]) and compared their intraclass correlation coefficient, fold change between HD and MS, and correlations with relevant clinical and imaging outcomes. We assembled the best features into machine-learning models and examined their performance in the independent validation cohort (45 patients with MS). We show that by involving multiple neurological functions, complex tests such as spiral tracing are susceptible to intra-individual variations, decreasing their reproducibility and clinical utility. Simple tests, reproducibly measuring single function(s) that can be aggregated to increase sensitivity, are preferable in app design.
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Affiliation(s)
- Komi S. Messan
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Office of Data Science and Emerging Technologies, Rockville, MD, United States
| | - Linh Pham
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, Bethesda, MD, United States
| | - Thomas Harris
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, Bethesda, MD, United States
| | - Yujin Kim
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, Bethesda, MD, United States
| | - Vanessa Morgan
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, Bethesda, MD, United States
| | - Peter Kosa
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, Bethesda, MD, United States
| | - Bibiana Bielekova
- National Institutes of Health, National Institute of Allergy and Infectious Diseases, Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, Bethesda, MD, United States
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Voigt I, Inojosa H, Dillenseger A, Haase R, Akgün K, Ziemssen T. Digital Twins for Multiple Sclerosis. Front Immunol 2021; 12:669811. [PMID: 34012452 PMCID: PMC8128142 DOI: 10.3389/fimmu.2021.669811] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2021] [Accepted: 04/16/2021] [Indexed: 12/16/2022] Open
Abstract
An individualized innovative disease management is of great importance for people with multiple sclerosis (pwMS) to cope with the complexity of this chronic, multidimensional disease. However, an individual state of the art strategy, with precise adjustment to the patient's characteristics, is still far from being part of the everyday care of pwMS. The development of digital twins could decisively advance the necessary implementation of an individualized innovative management of MS. Through artificial intelligence-based analysis of several disease parameters - including clinical and para-clinical outcomes, multi-omics, biomarkers, patient-related data, information about the patient's life circumstances and plans, and medical procedures - a digital twin paired to the patient's characteristic can be created, enabling healthcare professionals to handle large amounts of patient data. This can contribute to a more personalized and effective care by integrating data from multiple sources in a standardized manner, implementing individualized clinical pathways, supporting physician-patient communication and facilitating a shared decision-making. With a clear display of pre-analyzed patient data on a dashboard, patient participation and individualized clinical decisions as well as the prediction of disease progression and treatment simulation could become possible. In this review, we focus on the advantages, challenges and practical aspects of digital twins in the management of MS. We discuss the use of digital twins for MS as a revolutionary tool to improve diagnosis, monitoring and therapy refining patients' well-being, saving economic costs, and enabling prevention of disease progression. Digital twins will help make precision medicine and patient-centered care a reality in everyday life.
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Affiliation(s)
| | | | | | | | | | - Tjalf Ziemssen
- Center of Clinical Neuroscience, Department of Neurology, University Hospital Carl Gustav Carus, Technical University of Dresden, Dresden, Germany
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Smartphone-based symbol-digit modalities test reliably captures brain damage in multiple sclerosis. NPJ Digit Med 2021; 4:36. [PMID: 33627777 PMCID: PMC7904910 DOI: 10.1038/s41746-021-00401-y] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 01/06/2021] [Indexed: 02/07/2023] Open
Abstract
As the burden of neurodegenerative diseases increases, time-limited clinic encounters do not allow quantification of complex neurological functions. Patient-collected digital biomarkers may remedy this, if they provide reliable information. However, psychometric properties of digital tools remain largely un-assessed. We developed a smartphone adaptation of the cognitive test, the Symbol-Digit Modalities Test (SDMT) by randomizing the test’s symbol-number codes and testing sequences. The smartphone SDMT showed comparable psychometric properties in 154 multiple sclerosis (MS) patients and 39 healthy volunteers (HV). E.g., smartphone SDMT achieved slightly higher correlations with cognitive subscores of neurological examinations and with brain injury measured by MRI (R2 = 0.75, Rho = 0.83, p < 0.0001) than traditional SDMT. Mathematical adjustment for motoric disability of the dominant hand, measured by another smartphone test, compensates for the disadvantage of touch-based test. Averaging granular home measurements of the digital biomarker also increases accuracy of identifying true neurological decline.
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Hannikainen PA, Kosa P, Barbour C, Bielekova B. Extensive Healthy Donor Age/Gender Adjustments and Propensity Score Matching Reveal Physiology of Multiple Sclerosis Through Immunophenotyping. Front Neurol 2020; 11:565957. [PMID: 33329307 PMCID: PMC7732581 DOI: 10.3389/fneur.2020.565957] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Accepted: 09/04/2020] [Indexed: 01/09/2023] Open
Abstract
Quantifying cell subpopulations in biological fluids aids in diagnosis and understanding of the mechanisms of injury. Although much has been learned from cerebrospinal fluid (CSF) flow cytometry in neuroimmunological disorders, such as multiple sclerosis (MS), previous studies did not contain enough healthy donors (HD) to derive age- and gender-related normative data and sufficient heterogeneity of other inflammatory neurological disease (OIND) controls to identify MS specific changes. The goals of this blinded training and validation study of MS patients and embedded controls, representing 1,240 prospectively acquired paired CSF/blood samples from 588 subjects was (1) to define physiological age-/gender-related changes in CSF cells, (2) to define/validate cellular abnormalities in blood and CSF of untreated MS through disease duration (DD) and determine which are MS-specific, and (3) to compare effect(s) of low-efficacy (i.e., interferon-beta [IFN-beta] and glatiramer acetate [GA]) and high-efficacy drugs (i.e., natalizumab, daclizumab, and ocrelizumab) on MS-related cellular abnormalities using propensity score matching. Physiological gender differences are less pronounced in the CSF compared to blood, and age-related changes suggest decreased immunosurveillance of CNS by activated HLA-DR+T cells associated with natural aging. Results from patient samples support the concept of MS being immunologically single disease evolving in time. Initially, peripherally activated innate and adaptive immune cells migrate into CSF to form MS lesions. With progression, T cells (CD8+ > CD4+), NK cells, and myeloid dendritic cells are depleted from blood as they continue to accumulate, together with B cells, in the CSF and migrate to CNS tissue, forming compartmentalized inflammation. All MS drugs inhibit non-physiological accumulation of immune cells in the CSF. Although low-efficacy drugs tend to normalize it, high-efficacy drugs overshoot some aspects of CSF physiology, suggesting impairment of CNS immunosurveillance. Comparable inhibition of MS-related CSF abnormalities advocates changes within CNS parenchyma responsible for differences in drug efficacy on MS disability progression. Video summarizing all results may become useful educational tool.
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Affiliation(s)
| | | | | | - Bibiana Bielekova
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, United States
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Ziemssen T, Kern R, Voigt I, Haase R. Data Collection in Multiple Sclerosis: The MSDS Approach. Front Neurol 2020; 11:445. [PMID: 32612566 PMCID: PMC7308591 DOI: 10.3389/fneur.2020.00445] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2019] [Accepted: 04/27/2020] [Indexed: 01/17/2023] Open
Abstract
Multiple sclerosis (MS) is a frequent chronic inflammatory disease of the central nervous system that affects patients over decades. As the monitoring and treatment of MS become more personalized and complex, the individual assessment and collection of different parameters ranging from clinical assessments via laboratory and imaging data to patient-reported data become increasingly important for innovative patient management in MS. These aspects predestine electronic data processing for use in MS documentation. Such technologies enable the rapid exchange of health information between patients, practitioners, and caregivers, regardless of time and location. In this perspective paper, we present our digital strategy from Dresden, where we are developing the Multiple Sclerosis Documentation System (MSDS) into an eHealth platform that can be used for multiple purposes. Various use cases are presented that implement this software platform and offer an important perspective for the innovative digital patient management in the future. A holistic patient management of the MS, electronically supported by clinical pathways, will have an important impact on other areas of patient care, such as neurorehabilitation.
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Affiliation(s)
- Tjalf Ziemssen
- Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden, Germany
| | | | - Isabel Voigt
- Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden, Germany
| | - Rocco Haase
- Center of Clinical Neuroscience, University Hospital Carl Gustav Carus, Dresden, Germany
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Boukhvalova AK, Fan O, Weideman AM, Harris T, Kowalczyk E, Pham L, Kosa P, Bielekova B. Smartphone Level Test Measures Disability in Several Neurological Domains for Patients With Multiple Sclerosis. Front Neurol 2019; 10:358. [PMID: 31191424 PMCID: PMC6546929 DOI: 10.3389/fneur.2019.00358] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2019] [Accepted: 03/25/2019] [Indexed: 11/13/2022] Open
Abstract
Our long-term goal is to employ smartphone-embedded sensors to measure various neurological functions in a patient-autonomous manner. The interim goal is to develop simple smartphone tests (apps) and evaluate the clinical utility of these tests by selecting optimal outcomes that correlate well with clinician-measured disability in different neurological domains. In this paper, we used prospectively-acquired data from 112 multiple sclerosis (MS) patients and 15 healthy volunteers (HV) to assess the performance and optimize outcomes of a Level Test. The goal of the test is to tilt the smartphone so that a free-rolling ball travels to and remains in the center of the screen. An accelerometer detects tilting and records the coordinates of the ball at set time intervals. From this data, we derived five features: path length traveled, time spent in the center of the screen, average distance from the center, average speed while in the center, and number of direction changes underwent by the ball. Time in center proved to be the most sensitive feature to differentiate MS patients from HV and had the strongest correlations with clinician-derived scales. Its superiority was validated in an independent validation cohort of 29 MS patients. A linear combination of different Level features failed to outperform time in center in an independent validation cohort. Limited longitudinal data demonstrated that the Level features were relatively stable intra-individually within 4 months, without definitive evidence of learning. In contrast to previously developed smartphone tests that predominantly measure motoric functions, Level features correlated strongly with reaction time and moderately with cerebellar functions and proprioception, validating its complementary clinical value in the MS app suite. The Level Test measures neurological disability in several domains in two independent cross-sectional cohorts (original and validation). An ongoing longitudinal cohort further investigates whether patient-autonomous collection of granular functional data allows measurement of patient-specific trajectories of disability progression to better guide treatment decisions.
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Affiliation(s)
- Alexandra K Boukhvalova
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Olivia Fan
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Ann Marie Weideman
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Thomas Harris
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Emily Kowalczyk
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States.,Department of Computer Science, University of Maryland, College Park, MD, United States
| | - Linh Pham
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Peter Kosa
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Bibiana Bielekova
- Neuroimmunological Diseases Section, Laboratory of Clinical Immunology and Microbiology, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
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Boukhvalova AK, Kowalczyk E, Harris T, Kosa P, Wichman A, Sandford MA, Memon A, Bielekova B. Identifying and Quantifying Neurological Disability via Smartphone. Front Neurol 2018; 9:740. [PMID: 30233487 PMCID: PMC6131483 DOI: 10.3389/fneur.2018.00740] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Accepted: 08/14/2018] [Indexed: 11/13/2022] Open
Abstract
Embedded sensors of the smartphones offer opportunities for granular, patient-autonomous measurements of neurological dysfunctions for disease identification, management, and for drug development. We hypothesized that aggregating data from two simple smartphone tests of fine finger movements with differing contribution of specific neurological domains (i.e., strength & cerebellar functions, vision, and reaction time) will allow establishment of secondary outcomes that reflect domain-specific deficit. This hypothesis was tested by assessing correlations of smartphone-derived outcomes with relevant parts of neurological examination in multiple sclerosis (MS) patients. We developed MS test suite on Android platform, consisting of several simple functional tests. This paper compares cross-sectional and longitudinal performance of Finger tapping and Balloon popping tests by 76 MS patients and 19 healthy volunteers (HV). The primary outcomes of smartphone tests, the average number of taps (per two 10-s intervals) and the average number of pops (per two 26-s intervals) differentiated MS from HV with similar power to traditional, investigator-administered test of fine finger movements, 9-hole peg test (9HPT). Additionally, the secondary outcomes identified patients with predominant cerebellar dysfunction, motor fatigue and poor eye-hand coordination and/or reaction time, as evidenced by significant correlations between these derived outcomes and relevant parts of neurological examination. The intra-individual variance in longitudinal sampling was low. In the time necessary for performing 9HPT, smartphone tests provide much richer and reliable measurements of several distinct neurological functions. These data suggest that combing more creatively-construed smartphone apps may one day recreate the entire neurological examination.
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Affiliation(s)
- Alexandra K. Boukhvalova
- Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Emily Kowalczyk
- Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
- Department of Computer Science, University of Maryland, College Park, MD, United States
| | - Thomas Harris
- Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Peter Kosa
- Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Alison Wichman
- Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Mary A. Sandford
- Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
| | - Atif Memon
- Department of Computer Science, University of Maryland, College Park, MD, United States
| | - Bibiana Bielekova
- Laboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH), Bethesda, MD, United States
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