Giulia Fadda1, Robert A Brown1, Giulia Longoni2, Denise A Castro3, Julia O'Mahony4, Leonard H Verhey5, Helen M Branson3, Patrick Waters6, Amit Bar-Or7, Ruth Ann Marrie8, E Ann Yeh2, Sridar Narayanan1, Douglas L Arnold1, Brenda Banwell9. 1. Montreal Neurological Institute, McGill University, Montreal, QC, Canada. 2. Department of Pediatrics, University of Toronto, Toronto, ON, USA; Division of Neurology, Department of Pediatrics, University of Toronto, ON, Canada; Department of Diagnostic Imaging, Neurosciences and Mental Health, SickKids Research Institute, University of Toronto, ON, Canada. 3. Division of Neuroradiology, University of Toronto, ON, Canada. 4. Department of Diagnostic Imaging, Neurosciences and Mental Health, SickKids Research Institute, University of Toronto, ON, Canada; The Hospital for Sick Children, and the Institute of Health Policy, Management and Evaluation, University of Toronto, ON, Canada. 5. College of Medicine, Central Michigan University, Mount Pleasant, MI, USA. 6. Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, Oxford, UK. 7. Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. 8. Departments of Internal Medicine and Community Health Sciences, Max Rady College of Medicine, Rady Faculty of Health Sciences, University of Manitoba, Winnipeg, MB, Canada. 9. Division of Child Neurology, Department of Neurology, The Children's Hospital of Philadelphia, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA. Electronic address: banwellb@email.chop.edu.
Abstract
BACKGROUND: MRI and laboratory features have been incorporated into international diagnostic criteria for multiple sclerosis. We assessed the pattern of MRI lesions and contributions of cerebrospinal fluid (CSF) and serum antibody findings that best identifies children with multiple sclerosis, and the applicability of international diagnostic criteria in the paediatric context. METHODS: In this prospective cohort study, detailed clinical assessments, serum and CSF studies, and MRI scans were done in youth (aged 0·46-17·87 years) with incidental acquired demyelinating syndrome. Participants were examined prospectively to identify relapsing disease. All MRI scans were assessed using a validated scoring method. A random forest classifier identified imaging and laboratory features that best predicted a multiple sclerosis or monophasic outcome. Performance of the 2001, 2010, and 2017 international McDonald criteria for the diagnosis of multiple sclerosis, the 2016 MRI in multiple sclerosis (MAGNIMS) criteria, and our 2011 proposed (Verhey) criteria were determined; performance was adjudicated with generalised linear models. FINDINGS: Between Sept 1, 2004, and June 30, 2017, we included 324 participants with median follow-up of 72 months (range 6-150), 71 (22%) participants with multiple sclerosis, 237 (73%) with monophasic acquired demyelinating syndrome, 14 (4%) with relapsing non-multiple sclerosis, and two (1%) with alternative diagnoses. We scored 2391 brain, 444 spinal, and 67 dedicated orbital MRI scans. One or more T1 hypointense lesions plus one or more periventricular lesions (Verhey criteria) best predicted multiple sclerosis outcome. Performance of the 2017 McDonald criteria was comparable to the 2010 McDonald criteria and was easier to adjudicate. The ability of CSF oligoclonal bands to substitute for the requirement for both enhancing and non-enhancing lesions in the 2017 McDonald criteria improved its performance compared with the 2010 criteria. Myelin oligodendrocyte testing at baseline did not improve performance of the 2017 McDonald criteria. INTERPRETATION: The 2017 McDonald criteria for the diagnosis of multiple sclerosis, as applied at the time of incident attack, perform well in identifying children and youth with multiple sclerosis, indicating that the same diagnostic criteria for multiple sclerosis apply across the age span. The presence of so-called black holes on MRI and periventricular lesions at baseline (Verhey criteria) also effectively distinguish children with multiple sclerosis from children with monophasic demyelination. The presence of CSF oligoclonal bands improve diagnostic accuracy. Myelin oligodendrocyte glycoprotein antibodies identify children with acute disseminated encephalomyelitis, and those with relapsing non-multiple sclerosis, most of whom do not meet 2017 McDonald criteria at onset. FUNDING: The Multiple Sclerosis Scientific Research Foundation and The Children's Hospital of Philadelphia.
BACKGROUND: MRI and laboratory features have been incorporated into international diagnostic criteria for multiple sclerosis. We assessed the pattern of MRI lesions and contributions of cerebrospinal fluid (CSF) and serum antibody findings that best identifies children with multiple sclerosis, and the applicability of international diagnostic criteria in the paediatric context. METHODS: In this prospective cohort study, detailed clinical assessments, serum and CSF studies, and MRI scans were done in youth (aged 0·46-17·87 years) with incidental acquired demyelinating syndrome. Participants were examined prospectively to identify relapsing disease. All MRI scans were assessed using a validated scoring method. A random forest classifier identified imaging and laboratory features that best predicted a multiple sclerosis or monophasic outcome. Performance of the 2001, 2010, and 2017 international McDonald criteria for the diagnosis of multiple sclerosis, the 2016 MRI in multiple sclerosis (MAGNIMS) criteria, and our 2011 proposed (Verhey) criteria were determined; performance was adjudicated with generalised linear models. FINDINGS: Between Sept 1, 2004, and June 30, 2017, we included 324 participants with median follow-up of 72 months (range 6-150), 71 (22%) participants with multiple sclerosis, 237 (73%) with monophasic acquired demyelinating syndrome, 14 (4%) with relapsing non-multiple sclerosis, and two (1%) with alternative diagnoses. We scored 2391 brain, 444 spinal, and 67 dedicated orbital MRI scans. One or more T1 hypointense lesions plus one or more periventricular lesions (Verhey criteria) best predicted multiple sclerosis outcome. Performance of the 2017 McDonald criteria was comparable to the 2010 McDonald criteria and was easier to adjudicate. The ability of CSF oligoclonal bands to substitute for the requirement for both enhancing and non-enhancing lesions in the 2017 McDonald criteria improved its performance compared with the 2010 criteria. Myelin oligodendrocyte testing at baseline did not improve performance of the 2017 McDonald criteria. INTERPRETATION: The 2017 McDonald criteria for the diagnosis of multiple sclerosis, as applied at the time of incident attack, perform well in identifying children and youth with multiple sclerosis, indicating that the same diagnostic criteria for multiple sclerosis apply across the age span. The presence of so-called black holes on MRI and periventricular lesions at baseline (Verhey criteria) also effectively distinguish children with multiple sclerosis from children with monophasic demyelination. The presence of CSF oligoclonal bands improve diagnostic accuracy. Myelin oligodendrocyte glycoprotein antibodies identify children with acute disseminated encephalomyelitis, and those with relapsing non-multiple sclerosis, most of whom do not meet 2017 McDonald criteria at onset. FUNDING: The Multiple Sclerosis Scientific Research Foundation and The Children's Hospital of Philadelphia.
Authors: Yu Yi M Wong; C Louk de Mol; Roos M van der Vuurst de Vries; E Daniëlle van Pelt; Immy A Ketelslegers; Coriene E Catsman-Berrevoets; Rinze F Neuteboom; Rogier Q Hintzen Journal: Neurol Neuroimmunol Neuroinflamm Date: 2018-12-14
Authors: Massimo Filippi; Paolo Preziosa; Brenda L Banwell; Frederik Barkhof; Olga Ciccarelli; Nicola De Stefano; Jeroen J G Geurts; Friedemann Paul; Daniel S Reich; Ahmed T Toosy; Anthony Traboulsee; Mike P Wattjes; Tarek A Yousry; Achim Gass; Catherine Lubetzki; Brian G Weinshenker; Maria A Rocca Journal: Brain Date: 2019-07-01 Impact factor: 13.501
Authors: Laura Papetti; Lorenzo Figà Talamanca; Alberto Spalice; Federico Vigevano; Diego Centonze; Massimiliano Valeriani Journal: Front Neurol Date: 2019-01-15 Impact factor: 4.003
Authors: Ziyan Li; Hong Sun; Xiao Fan; Ping Yuan; Yan Jiang; Peng Wu; Min Zhong; Jiannan Ma; Li Jiang; Xiujuan Li Journal: Front Neurol Date: 2021-03-26 Impact factor: 4.003
Authors: Patrick Waters; Giulia Fadda; Mark Woodhall; Julia O'Mahony; Robert A Brown; Denise A Castro; Giulia Longoni; Sarosh R Irani; Bo Sun; E Ann Yeh; Ruth Ann Marrie; Douglas L Arnold; Brenda Banwell; Amit Bar-Or Journal: JAMA Neurol Date: 2020-01-01 Impact factor: 18.302