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De Brouwer E, Becker T, Werthen-Brabants L, Dewulf P, Iliadis D, Dekeyser C, Laureys G, Van Wijmeersch B, Popescu V, Dhaene T, Deschrijver D, Waegeman W, De Baets B, Stock M, Horakova D, Patti F, Izquierdo G, Eichau S, Girard M, Prat A, Lugaresi A, Grammond P, Kalincik T, Alroughani R, Grand’Maison F, Skibina O, Terzi M, Lechner-Scott J, Gerlach O, Khoury SJ, Cartechini E, Van Pesch V, Sà MJ, Weinstock-Guttman B, Blanco Y, Ampapa R, Spitaleri D, Solaro C, Maimone D, Soysal A, Iuliano G, Gouider R, Castillo-Triviño T, Sánchez-Menoyo JL, Laureys G, van der Walt A, Oh J, Aguera-Morales E, Altintas A, Al-Asmi A, de Gans K, Fragoso Y, Csepany T, Hodgkinson S, Deri N, Al-Harbi T, Taylor B, Gray O, Lalive P, Rozsa C, McGuigan C, Kermode A, Sempere AP, Mihaela S, Simo M, Hardy T, Decoo D, Hughes S, Grigoriadis N, Sas A, Vella N, Moreau Y, Peeters L. Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study. PLOS DIGITAL HEALTH 2024; 3:e0000533. [PMID: 39052668 PMCID: PMC11271865 DOI: 10.1371/journal.pdig.0000533] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 05/14/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND Disability progression is a key milestone in the disease evolution of people with multiple sclerosis (PwMS). Prediction models of the probability of disability progression have not yet reached the level of trust needed to be adopted in the clinic. A common benchmark to assess model development in multiple sclerosis is also currently lacking. METHODS Data of adult PwMS with a follow-up of at least three years from 146 MS centers, spread over 40 countries and collected by the MSBase consortium was used. With basic inclusion criteria for quality requirements, it represents a total of 15, 240 PwMS. External validation was performed and repeated five times to assess the significance of the results. Transparent Reporting for Individual Prognosis Or Diagnosis (TRIPOD) guidelines were followed. Confirmed disability progression after two years was predicted, with a confirmation window of six months. Only routinely collected variables were used such as the expanded disability status scale, treatment, relapse information, and MS course. To learn the probability of disability progression, state-of-the-art machine learning models were investigated. The discrimination performance of the models is evaluated with the area under the receiver operator curve (ROC-AUC) and under the precision recall curve (AUC-PR), and their calibration via the Brier score and the expected calibration error. All our preprocessing and model code are available at https://gitlab.com/edebrouwer/ms_benchmark, making this task an ideal benchmark for predicting disability progression in MS. FINDINGS Machine learning models achieved a ROC-AUC of 0⋅71 ± 0⋅01, an AUC-PR of 0⋅26 ± 0⋅02, a Brier score of 0⋅1 ± 0⋅01 and an expected calibration error of 0⋅07 ± 0⋅04. The history of disability progression was identified as being more predictive for future disability progression than the treatment or relapses history. CONCLUSIONS Good discrimination and calibration performance on an external validation set is achieved, using only routinely collected variables. This suggests machine-learning models can reliably inform clinicians about the future occurrence of progression and are mature for a clinical impact study.
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Affiliation(s)
| | - Thijs Becker
- I-Biostat, Hasselt University, Belgium
- Data Science Institute, Hasselt University, Belgium
| | | | - Pieter Dewulf
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium
| | - Dimitrios Iliadis
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium
| | - Cathérine Dekeyser
- Department of Neurology, Ghent University, Belgium
- 4 Brain, Ghent University, Belgium
- Biomedical Research Institute, Hasselt University, Belgium
| | - Guy Laureys
- Department of Neurology, Ghent University, Belgium
- 4 Brain, Ghent University, Belgium
| | - Bart Van Wijmeersch
- Noorderhart ziekenhuizen Pelt, Belgium
- Universitair MS Centrum Hasselt-Pelt, Belgium
| | - Veronica Popescu
- Noorderhart ziekenhuizen Pelt, Belgium
- Universitair MS Centrum Hasselt-Pelt, Belgium
| | - Tom Dhaene
- SUMO, IDLAB, Ghent University - imec, Belgium
| | | | - Willem Waegeman
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium
| | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium
| | - Michiel Stock
- KERMIT, Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium
- Biobix, Department of Data Analysis and Mathematical Modelling, Ghent University, Belgium
| | - Dana Horakova
- Charles University in Prague and General University Hospital, Prague, Czech Republic
| | - Francesco Patti
- Department of Medical and Surgical Sciences and Advanced Technologies, GF Ingrassia, Catania, Italy
| | | | - Sara Eichau
- Hospital Universitario Virgen Macarena, Sevilla, Spain
| | - Marc Girard
- CHUM and Université de Montreal, Montreal, Canada
| | | | - Alessandra Lugaresi
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italia and Dipartimento di Scienze Biomediche e Neuromotorie, Università di Bologna, Bologna, Italia
| | | | - Tomas Kalincik
- Melbourne MS Centre, Department of Neurology, Royal Melbourne Hospital, Melbourne, Australia
- CORe, Department of Medicine, University of Melbourne, Melbourne, Australia
| | | | | | | | | | | | - Oliver Gerlach
- Academic MS Center Zuyderland, Department of Neurology, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
- School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Samia J. Khoury
- American University of Beirut Medical Center, Beirut, Lebanon
| | | | | | - Maria José Sà
- Centro Hospitalar Universitario de Sao Joao, Porto, Portugal
| | | | | | | | - Daniele Spitaleri
- Azienda Ospedaliera di Rilievo Nazionale San Giuseppe Moscati Avellino, Avellino, Italy
| | - Claudio Solaro
- Dept. of Rehabilitation, CRFF Mons. Luigi Novarese, Moncrivello, Italy
| | - Davide Maimone
- MS center, UOC Neurologia, ARNAS Garibaldi, Catania, Italy
| | - Aysun Soysal
- Bakirkoy Education and Research Hospital for Psychiatric and Neurological Diseases, Istanbul, Turkey
| | | | | | | | | | | | | | - Jiwon Oh
- St. Michael’s Hospital, Toronto, Canada
| | | | - Ayse Altintas
- Koc University, School of Medicine, Istanbul, Turkey
| | - Abdullah Al-Asmi
- College of Medicine & Health Sciences and Sultan Qaboos University Hospital, SQU, Oman
| | | | - Yara Fragoso
- Universidade Metropolitana de Santos, Santos, Brazil
| | | | | | - Norma Deri
- Hospital Fernandez, Capital Federal, Argentina
| | - Talal Al-Harbi
- King Fahad Specialist Hospital-Dammam, Khobar, Saudi Arabia
| | | | - Orla Gray
- South Eastern HSC Trust, Belfast, United Kingdom
| | | | - Csilla Rozsa
- Jahn Ferenc Teaching Hospital, Budapest, Hungary
| | | | - Allan Kermode
- University of Western Australia, Nedlands, Australia
| | | | - Simu Mihaela
- Emergency Clinical County Hospital Pius Brinzeu, Timisoara, Romania and University of Medicine and Pharmacy Victor Babes, Timisoara, Romania
| | | | - Todd Hardy
- Concord Repatriation General Hospital, Sydney, Australia
| | - Danny Decoo
- AZ Alma Ziekenhuis, Sijsele - Damme, Belgium
| | | | | | | | | | | | - Liesbet Peeters
- Data Science Institute, Hasselt University, Belgium
- Universitair MS Centrum Hasselt-Pelt, Belgium
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Gonçalves R, De Decker S, Walmsley G, Maddox TW. Prognosis in meningoencephalitis of unknown origin in dogs: Risk factors associated with survival, clinical relapse, and long-term disability. J Vet Intern Med 2024; 38:1583-1590. [PMID: 38483069 PMCID: PMC11099754 DOI: 10.1111/jvim.17037] [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] [Received: 09/13/2023] [Accepted: 02/26/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Meningoencephalitis of unknown origin (MUO) comprises a group of noninfectious inflammatory diseases affecting the central nervous system of dogs. Previous studies have reported individual risk factors for survival but prognostication for MUO remains challenging. OBJECTIVES Identify clinical prognostic variables in dogs with MUO. ANIMALS A retrospective study of 447 dogs presented to 2 UK referral hospitals and diagnosed with MUO. METHODS Medical records of dogs diagnosed with MUO were retrospectively reviewed. Multivariable logistic regression was used for the identification of risk factors for survival and Cox proportional hazards analysis for the identification of risk factors for clinical relapse. RESULTS Eighty-two percent (366/447) of dogs with presumptive MUO survived to discharge and 63.5% (284/447) were alive at 6 months; 36% of the latter (103/284) had persistent neurological deficits. Breed (pugs; P = .03), epileptic seizures (P < .001), paresis (P < .001), and higher neurodisability scale (NDS) score (P < .001) at presentation were negatively associated with survival to 6 months. Dogs with persistent deficits had higher NDS scores on presentation (P = .001). Median follow-up time was 11 months (interquartile range [IQR], 1-24) and 50.6% (160/316) relapsed during treatment (median time to relapse, 7 months; IQR, 2-15). Incomplete resolution of the clinical signs during the 6 months after diagnosis (P < .001), higher NDS score (P < .001), and longer duration of the clinical signs (P < .001) were associated with relapse. CONCLUSIONS AND CLINICAL IMPORTANCE Knowledge of risk factors associated with survival, incomplete recovery and clinical relapse in MUO can help guide monitoring and treatment and improve owner communications regarding prognosis for this debilitating disease.
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Affiliation(s)
- Rita Gonçalves
- Department of Veterinary ScienceSmall Animal Teaching Hospital, University of LiverpoolNestonUK
- Department of Musculoskeletal and Ageing ScienceInstitute of Lifecourse and Medical Sciences, University of LiverpoolNestonUK
| | - Steven De Decker
- Department of Clinical Science and ServicesRoyal Veterinary College, University of LondonHatfieldUK
| | - Gemma Walmsley
- Department of Veterinary ScienceSmall Animal Teaching Hospital, University of LiverpoolNestonUK
- Department of Musculoskeletal and Ageing ScienceInstitute of Lifecourse and Medical Sciences, University of LiverpoolNestonUK
| | - Thomas W. Maddox
- Department of Veterinary ScienceSmall Animal Teaching Hospital, University of LiverpoolNestonUK
- Department of Musculoskeletal and Ageing ScienceInstitute of Lifecourse and Medical Sciences, University of LiverpoolNestonUK
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AlTokhis AI, AlAmrani A, Alotaibi A, Podlasek A, Constantinescu CS. Magnetic Resonance Imaging as a Prognostic Disability Marker in Clinically Isolated Syndrome and Multiple Sclerosis: A Systematic Review and Meta-Analysis. Diagnostics (Basel) 2022; 12:270. [PMID: 35204361 PMCID: PMC8871297 DOI: 10.3390/diagnostics12020270] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 01/17/2022] [Accepted: 01/18/2022] [Indexed: 01/27/2023] Open
Abstract
To date, there are no definite imaging predictors for long-term disability in multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the key prognostic tool for MS, primarily at the early stage of the disease. Recent findings showed that white matter lesion (WML) counts and volumes could predict long-term disability for MS. However, the prognostic value of MRI in the early stage of the disease and its link to long-term physical disability have not been assessed systematically and quantitatively. A meta-analysis was conducted using studies from four databases to assess whether MS lesion counts and volumes at baseline MRI scans could predict long-term disability, assessed by the expanded disability status scale (EDSS). Fifteen studies were eligible for the qualitative analysis and three studies for meta-analysis. T2 brain lesion counts and volumes after the disease onset were associated with disability progression after 10 years. Four or more lesions at baseline showed a highly significant association with EDSS 3 and EDSS 6, with a pooled OR of 4.10 and 4.3, respectively. The risk increased when more than 10 lesions were present. This review and meta-analysis confirmed that lesion counts and volumes could be associated with disability and might offer additional valid guidance in treatment decision making. Future work is essential to determine whether these prognostic markers have high predictive potential.
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Affiliation(s)
- Amjad I. AlTokhis
- Mental and Clinical Neuroscience Academic Unit, School of Medicine, Nottingham University Hospitals NHS Trust, University of Nottingham, Nottingham NG7 2UH, UK; (A.A.); (A.P.)
- Division of Health and Rehabilitation Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11564, Saudi Arabia
| | - Abrar AlAmrani
- Faculty of Health, York University, Toronto, ON M3J 1P3, Canada;
| | - Abdulmajeed Alotaibi
- Mental and Clinical Neuroscience Academic Unit, School of Medicine, Nottingham University Hospitals NHS Trust, University of Nottingham, Nottingham NG7 2UH, UK; (A.A.); (A.P.)
- Department of Radiological Sciences, School of Applied Medical Sciences, King Saud bin Abdul-Aziz University for Health Sciences, Riyadh 14611, Saudi Arabia
| | - Anna Podlasek
- Mental and Clinical Neuroscience Academic Unit, School of Medicine, Nottingham University Hospitals NHS Trust, University of Nottingham, Nottingham NG7 2UH, UK; (A.A.); (A.P.)
- Tayside Innovation MedTech Ecosystem, Division of Imaging Science and Technology, School of Medicine, University of Dundee, Dundee DD1 4HN, UK
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Roura E, Maclair G, Andorrà M, Juanals F, Pulido-Valdeolivas I, Saiz A, Blanco Y, Sepulveda M, Llufriu S, Martínez-Heras E, Solana E, Martinez-Lapiscina EH, Villoslada P. Cortical fractal dimension predicts disability worsening in Multiple Sclerosis patients. Neuroimage Clin 2021; 30:102653. [PMID: 33838548 PMCID: PMC8045041 DOI: 10.1016/j.nicl.2021.102653] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 03/14/2021] [Accepted: 03/26/2021] [Indexed: 11/26/2022]
Abstract
BACKGROUND Fractal geometry measures the morphology of the brain and detects CNS damage. We aimed to assess the longitudinal changes on brain's fractal geometry and its predictive value for disease worsening in patients with Multiple Sclerosis (MS). METHODS We prospectively analyzed 146 consecutive patients with relapsing-remitting MS with up to 5 years of clinical and brain MRI (3 T) assessments. The fractal dimension and lacunarity were calculated for brain regions using box-counting methods. Longitudinal changes were analyzed in mixed-effect models and the risk of disability accumulation were assessed using Cox Proportional Hazard regression analysis. RESULTS There was a significant decrease in the fractal dimension and increases of lacunarity in different brain regions over the 5-year follow-up. Lower cortical fractal dimension increased the risk of disability accumulation for the Expanded Disability Status Scale [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.038], 9-hole peg test [HR 0.9734, CI 0.8420-0.9125; Harrell C 0.59; Wald p 0.0083], 2.5% low contrast vision [HR 0.4311, CI 0.2035-0.9133; Harrell C 0.58; Wald p 0.0403], symbol digit modality test [HR 2.215, CI 1.043-4.705; Harrell C 0.65; Wald p 0.0384] and MS Functional Composite-4 [HR 0.55, CI 0.317-0.955; Harrell C 0.59; Wald p 0.0029]. CONCLUSIONS Fractal geometry analysis of brain MRI identified patients at risk of increasing their disability in the next five years.
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Affiliation(s)
| | | | - Magí Andorrà
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | | | - Irene Pulido-Valdeolivas
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Albert Saiz
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Yolanda Blanco
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Maria Sepulveda
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Sara Llufriu
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Eloy Martínez-Heras
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Elisabeth Solana
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Elena H Martinez-Lapiscina
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain
| | - Pablo Villoslada
- Institut d'Investigacions Biomèdiques August Pi Sunyer - Hospital Clinic, University of Barcelona, Spain; Stanford University, Stanford, CA, USA.
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Lassau N, Bousaid I, Chouzenoux E, Lamarque J, Charmettant B, Azoulay M, Cotton F, Khalil A, Lucidarme O, Pigneur F, Benaceur Y, Sadate A, Lederlin M, Laurent F, Chassagnon G, Ernst O, Ferreti G, Diascorn Y, Brillet P, Creze M, Cassagnes L, Caramella C, Loubet A, Dallongeville A, Abassebay N, Ohana M, Banaste N, Cadi M, Behr J, Boussel L, Fournier L, Zins M, Beregi J, Luciani A, Cotten A, Meder J. Three artificial intelligence data challenges based on CT and MRI. Diagn Interv Imaging 2020; 101:783-788. [DOI: 10.1016/j.diii.2020.03.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Accepted: 03/12/2020] [Indexed: 02/07/2023]
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Simmatis LE, Jin AY, Taylor SW, Bisson EJ, Scott SH, Baharnoori M. The feasibility of assessing cognitive and motor function in multiple sclerosis patients using robotics. Mult Scler J Exp Transl Clin 2020; 6:2055217320964940. [PMID: 33149931 PMCID: PMC7580159 DOI: 10.1177/2055217320964940] [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: 04/14/2020] [Accepted: 09/18/2020] [Indexed: 12/12/2022] Open
Abstract
Background Multiple sclerosis (MS) causes pervasive motor, sensory and cognitive dysfunction. The Expanded Disability Status Scale (EDSS) is the gold standard for assessing MS disability. The EDSS is biased towards mobility and may not accurately measure MS-related disabilities in the upper limb or in cognitive functions (e.g. executive function). Objective Our objectives were to determine the feasibility of using the Kinarm robotic system to quantify neurological deficits related to arm function and cognition in MS patients, and examine relationships between traditional clinical assessments and Kinarm variables. Methods Individuals with MS performed 8 robotic tasks assessing motor, cognitive, and sensory ability. We additionally collected traditional clinical assessments and compared these to the results of the robotic assessment. Results Forty-three people with MS were assessed. Most participants could complete the robotic assessment. Twenty-six (60%) were impaired on at least one cognitive task and twenty-six (60%) were impaired on at least one upper-limb motor task. Cognitive domain task performance correlated most strongly with the EDSS. Conclusions Kinarm robotic assessment of people with MS is feasible, can identify a broad range of upper-limb motor and sensory, as well as cognitive, impairments, and complements current clinical rating scales in the assessment of MS-related disability.
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Affiliation(s)
- Leif Er Simmatis
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada
| | | | - Sean W Taylor
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada
| | - Etienne J Bisson
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada
| | - Stephen H Scott
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada
| | - Moogeh Baharnoori
- Centre for Neuroscience Studies, Queen's University, Kingston, Canada
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Systematic review of prediction models in relapsing remitting multiple sclerosis. PLoS One 2020; 15:e0233575. [PMID: 32453803 PMCID: PMC7250448 DOI: 10.1371/journal.pone.0233575] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Accepted: 05/07/2020] [Indexed: 12/02/2022] Open
Abstract
The natural history of relapsing remitting multiple sclerosis (RRMS) is variable and prediction of individual prognosis challenging. The inability to reliably predict prognosis at diagnosis has important implications for informed decision making especially in relation to disease modifying therapies. We conducted a systematic review in order to collate, describe and assess the methodological quality of published prediction models in RRMS. We searched Medline, Embase and Web of Science. Two reviewers independently screened abstracts and full text for eligibility and assessed risk of bias. Studies reporting development or validation of prediction models for RRMS in adults were included. Data collection was guided by the checklist for critical appraisal and data extraction for systematic reviews (CHARMS) and applicability and methodological quality assessment by the prediction model risk of bias assessment tool (PROBAST). 30 studies were included in the review. Applicability was assessed as high risk of concern in 27 studies. Risk of bias was assessed as high for all studies. The single most frequently included predictor was baseline EDSS (n = 11). T2 Lesion volume or number and brain atrophy were each retained in seven studies. Five studies included external validation and none included impact analysis. Although a number of prediction models for RRMS have been reported, most are at high risk of bias and lack external validation and impact analysis, restricting their application to routine clinical practice.
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Lotan I, Benninger F, Mendel R, Hellmann MA, Steiner I. Does CSF pleocytosis have a predictive value for disease course in MS? NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2019; 6:e584. [PMID: 31355320 PMCID: PMC6624148 DOI: 10.1212/nxi.0000000000000584] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 04/26/2019] [Indexed: 12/02/2022]
Abstract
Objective MS is a demyelinating CNS disorder with a spectrum of clinical patterns regarding course and prognosis. Although several prognostic factors are considered in the initial evaluation of patients, biological markers defining the disease course and guiding treatments are currently lacking. It is unknown whether patients with CSF pleocytosis differ in regard to symptoms, disease course, and prognosis from those without. The aim of this study was to evaluate whether CSF pleocytosis during the initial presentation has an impact on the clinical course and progression of MS. Methods We retrospectively evaluated patients attending the MS Clinic at Rabin Medical Center between January 1999 and January 2016 who underwent lumbar puncture (LP) at disease presentation, considering CSF cell count, clinical diagnosis (clinically isolated syndrome [CIS] and relapsing-remitting MS [RRMS]), annualized relapse rate (ARR), paraclinical findings (imaging, CSF oligoclonal bands, and evoked potentials), and disease progression, expressed by the Expanded Disability Status Scale (EDSS). Results One hundred fourteen patients (72 females) underwent LP at disease presentation (RRMS: n = 100, CIS: n = 14). Age at diagnosis was 32.4 ± 12.2 years, and the follow-up time was 9.4 ± 3.8 years. Forty-six patients showed a pleocytic CSF (≥5 cells per μL). Compared with patients with <4 cells per μL, patients with pleocytosis had a higher ARR (0.60 ± 0.09 vs 0.48 ± 0.04; p = 0.0267) and a steeper increase (slope) in the EDSS score throughout the follow-up period (correlation coefficient: r2 = 0.04; p = 0.0251). Conclusions CSF pleocytosis may be considered a biological unfavorable predictive factor regarding disease course and progression in MS.
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Affiliation(s)
- Itay Lotan
- Neuro-Immunology Service and Department of Neurology (I.L., M.A.H.), Rabin Medical Center; Department of Neurology (I.L., F.B., R.M., M.A.H., I.S.), Rabin Medical Center; and Sackler Faculty of Medicine (I.L., F.B., R.M., M.A.H., I.S.), Tel Aviv University, Israel
| | - Felix Benninger
- Neuro-Immunology Service and Department of Neurology (I.L., M.A.H.), Rabin Medical Center; Department of Neurology (I.L., F.B., R.M., M.A.H., I.S.), Rabin Medical Center; and Sackler Faculty of Medicine (I.L., F.B., R.M., M.A.H., I.S.), Tel Aviv University, Israel
| | - Rom Mendel
- Neuro-Immunology Service and Department of Neurology (I.L., M.A.H.), Rabin Medical Center; Department of Neurology (I.L., F.B., R.M., M.A.H., I.S.), Rabin Medical Center; and Sackler Faculty of Medicine (I.L., F.B., R.M., M.A.H., I.S.), Tel Aviv University, Israel
| | - Mark A Hellmann
- Neuro-Immunology Service and Department of Neurology (I.L., M.A.H.), Rabin Medical Center; Department of Neurology (I.L., F.B., R.M., M.A.H., I.S.), Rabin Medical Center; and Sackler Faculty of Medicine (I.L., F.B., R.M., M.A.H., I.S.), Tel Aviv University, Israel
| | - Israel Steiner
- Neuro-Immunology Service and Department of Neurology (I.L., M.A.H.), Rabin Medical Center; Department of Neurology (I.L., F.B., R.M., M.A.H., I.S.), Rabin Medical Center; and Sackler Faculty of Medicine (I.L., F.B., R.M., M.A.H., I.S.), Tel Aviv University, Israel
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Marques VD, Passos GRD, Mendes MF, Callegaro D, Lana-Peixoto MA, Comini-Frota ER, Vasconcelos CCF, Sato DK, Ferreira MLB, Parolin MKF, Damasceno A, Grzesiuk AK, Muniz A, Matta APDC, Oliveira BESD, Tauil CB, Maciel DRK, Diniz DS, Corrêa EC, Coronetti F, Jorge FMH, Sato HK, Gonçalves MVM, Sousa NADC, Nascimento OJM, Gama PDD, Domingues R, Simm RF, Thomaz RB, Morales RDR, Dias RM, Apóstolos-Pereira SD, Machado SCN, Junqueira TDF, Becker J. Brazilian Consensus for the Treatment of Multiple Sclerosis: Brazilian Academy of Neurology and Brazilian Committee on Treatment and Research in Multiple Sclerosis. ARQUIVOS DE NEURO-PSIQUIATRIA 2019; 76:539-554. [PMID: 30231128 DOI: 10.1590/0004-282x20180078] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2018] [Accepted: 05/16/2018] [Indexed: 12/21/2022]
Abstract
The expanding therapeutic arsenal in multiple sclerosis (MS) has allowed for more effective and personalized treatment, but the choice and management of disease-modifying therapies (DMTs) is becoming increasingly complex. In this context, experts from the Brazilian Committee on Treatment and Research in Multiple Sclerosis and the Neuroimmunology Scientific Department of the Brazilian Academy of Neurology have convened to establish this Brazilian Consensus for the Treatment of MS, based on their understanding that neurologists should be able to prescribe MS DMTs according to what is better for each patient, based on up-to-date evidence and practice. We herein propose practical recommendations for the treatment of MS, with the main focus on the choice and management of DMTs, as well as present a review of the scientific rationale supporting therapeutic strategies in MS.
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Affiliation(s)
- Vanessa Daccach Marques
- Universidade de São Paulo, Faculdade de Medicina de Ribeirão Preto, Hospital das Clínicas de Ribeirão Preto, Ribeirão Preto SP, Brasil
| | | | - Maria Fernanda Mendes
- Universidade de São Paulo, Faculdade de Medicina, Hospital das Clínicas, São Paulo SP, Brasil
| | - Dagoberto Callegaro
- Universidade de São Paulo, Faculdade de Medicina, Hospital das Clínicas, São Paulo SP, Brasil
| | - Marco Aurélio Lana-Peixoto
- Universidade Federal de Minas Gerais, Centro de Investigação em Esclerose Múltipla de Minas Gerais, Belo Horizonte MG, Brasil
| | | | | | | | | | | | | | | | | | | | | | - Carlos Bernardo Tauil
- Universidade de Brasília, Brasília DF, Brasil.,Universidade Católica de Brasília, Brasília DF, Brasil
| | | | | | | | | | - Frederico M H Jorge
- Universidade de São Paulo, Faculdade de Medicina, Hospital das Clínicas, São Paulo SP, Brasil
| | | | | | | | | | | | - Renan Domingues
- Senne Líquor Diagnóstico, São Paulo SP, Brasil.,Hospital Cruz Azul, São Paulo SP, Brasil.,Faculdade São Leopoldo Mandic, Campinas SP, Brasil
| | - Renata Faria Simm
- Universidade de São Paulo, Faculdade de Medicina, Hospital das Clínicas, São Paulo SP, Brasil
| | | | | | | | | | | | | | - Jefferson Becker
- Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre RS, Brasil.,Universidade Federal Fluminense, Niterói RJ, Brasil
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Hemond CC, Healy BC, Tauhid S, Mazzola MA, Quintana FJ, Gandhi R, Weiner HL, Bakshi R. MRI phenotypes in MS: Longitudinal changes and miRNA signatures. NEUROLOGY-NEUROIMMUNOLOGY & NEUROINFLAMMATION 2019; 6:e530. [PMID: 30800720 PMCID: PMC6384020 DOI: 10.1212/nxi.0000000000000530] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Accepted: 11/09/2018] [Indexed: 12/20/2022]
Abstract
Objective To classify and immunologically characterize persons with MS based on brain lesions and atrophy and their associated microRNA profiles. Methods Cerebral T2-hyperintense lesion volume (T2LV) and brain parenchymal fraction (BPF) were quantified and used to define MRI phenotypes as follows: type I: low T2LV, low atrophy; type II: high T2LV, low atrophy; type III: low T2LV, high atrophy; type IV: high T2LV, high atrophy, in a large cross-sectional cohort (n = 1,088) and a subset with 5-year lngitudinal follow-up (n = 153). Serum miRNAs were assessed on a third MS cohort with 2-year MRI phenotype stability (n = 98). Results One-third of the patients had lesion-atrophy dissociation (types II or III) in both the cross-sectional and longitudinal cohorts. At 5 years, all phenotypes had progressive atrophy (p < 0.001), disproportionally in type II (BPF -2.28%). Only type IV worsened in physical disability. Types I and II showed a 5-year MRI phenotype conversion rate of 33% and 46%, whereas III and IV had >90% stability. Type II switched primarily to IV (91%); type I switched primarily to II (47%) or III (37%). Baseline higher age (p = 0.006) and lower BPF (p < 0.001) predicted 5-year phenotype conversion. Each MRI phenotype demonstrated an miRNA signature whose underlying biology implicates blood-brain barrier pathology: hsa.miR.22.3p, hsa.miR.361.5p, and hsa.miR.345.5p were the most valid differentiators of MRI phenotypes. Conclusions MRI-defined MS phenotypes show high conversion rates characterized by the continuation of either predominant neurodegeneration or inflammation and support the partial independence of these 2 measures. MicroRNA signatures of these phenotypes suggest a role for blood-brain barrier integrity.
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Affiliation(s)
- Christopher C Hemond
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Brian C Healy
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Shahamat Tauhid
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Maria A Mazzola
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Francisco J Quintana
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Roopali Gandhi
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Howard L Weiner
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
| | - Rohit Bakshi
- Departments of Neurology (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.) and Department of Radiology (R.B.); Brigham and Women's Hospital (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Laboratory for Neuroimaging Research (C.C.H., S.T., R.H.); Partners Multiple Sclerosis Center (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); Ann Romney Center for Neurologic Diseases (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W, R.B.); and Harvard Medical School (C.C.H., B.C.H., S.T., M.A.M., F.J.Q., R.G., H.L.W., R.B.), Boston, MA
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11
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Rahn AC, Köpke S, Stellmann JP, Schiffmann I, Lukas C, Chard D, Heesen C. Magnetic resonance imaging as a prognostic disability marker in clinically isolated syndrome: A systematic review. Acta Neurol Scand 2019; 139:18-32. [PMID: 30091223 DOI: 10.1111/ane.13010] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2018] [Accepted: 07/30/2018] [Indexed: 11/30/2022]
Abstract
Magnetic resonance imaging (MRI) is the key prognostic tool in people with a clinically isolated syndrome (CIS). There is increasing interest in treating people following a CIS in the hope that conversion to multiple sclerosis (MS) will be prevented and future disability reduced. So far, the prognostic value of MRI for disability following a CIS has not been evaluated systematically. We systematically searched MEDLINE and EMBASE. Cohort studies were selected if they reported associations of MRI and disability following a CIS, included at least 50 people with a CIS at baseline, had at least 5 years of follow-up and obtained at least one structural MRI measurement (T1 lesions, T2 lesions, T1 contrast-enhancing lesions or brain atrophy). We assessed the studies for quality and rated the completeness of MRI reporting. In total, 13 studies were identified reporting on the following: T2 lesion number and volume, T2 infratentorial lesion number and volume, T1 contrast-enhancing lesions and grey matter fraction. T2 brain lesion number determined soon after the occurrence of a CIS was associated with disability progression after 5-7 years, with an increased risk when 10 or more lesions were present. Infratentorial lesions were also associated with a higher risk of subsequent disability. The number and distribution of MRI-visible lesions soon after a CIS are associated with disability later on, and may offer additional useful information when making treatment decisions in people with early MS. Further work is required to determine whether other measures have a higher predictive potential.
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Affiliation(s)
- Anne C. Rahn
- Institute of Neuroimmunology and Multiple Sclerosis; University Medical Center Hamburg-Eppendorf; Hamburg Germany
| | - Sascha Köpke
- Nursing Research Unit; University of Lübeck; Lübeck Germany
| | - Jan-Patrick Stellmann
- Institute of Neuroimmunology and Multiple Sclerosis; University Medical Center Hamburg-Eppendorf; Hamburg Germany
| | - Insa Schiffmann
- Institute of Neuroimmunology and Multiple Sclerosis; University Medical Center Hamburg-Eppendorf; Hamburg Germany
| | - Carsten Lukas
- Department of Radiology; St. Josef Hospital Bochum; Ruhr University; Bochum Germany
| | - Declan Chard
- NMR Research Unit; Queen Square Multiple Sclerosis Centre; University College London (UCL); Institute of Neurology; London UK
- National Institute for Health Research (NIHR); University College London Hospitals (UCLH); Biomedical Research Centre; London UK
| | - Christoph Heesen
- Institute of Neuroimmunology and Multiple Sclerosis; University Medical Center Hamburg-Eppendorf; Hamburg Germany
- Department of Neurology; University Medical Center Hamburg-Eppendorf; Hamburg Germany
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12
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Paz-Ballesteros WC, Monterrubio-Flores EA, de Jesús Flores-Rivera J, Corona-Vázquez T, Hernández-Girón C. Cigarette Smoking, Alcohol Consumption and Overweight in Multiple Sclerosis: Disability Progression. Arch Med Res 2017; 48:113-120. [DOI: 10.1016/j.arcmed.2017.03.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2016] [Accepted: 02/02/2017] [Indexed: 01/07/2023]
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