Evidence map›Paper›PMID 32961403›Full record

ArticleNeuroImage. Clinical2020

Spinal cord atrophy in a primary progressive multiple sclerosis trial: Improved sample size using GBSI.

Marcello Moccia, Nicola Valsecchi, Olga Ciccarelli, Ronald Van Schijndel, Frederik Barkhof, Ferran Prados

Open access · goldAbstract read
In one paragraph

Article in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 3 institutions in 4 countries.

Marcello MocciaQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom; Multiple Sclerosis Clinical Care and Research Centre, Department of Neurosciences, Federico II University, Naples, Italy.
Nicola ValsecchiQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom.
Olga CiccarelliQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom; National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, United Kingdom.
Ronald Van SchijndelDepartment of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam, the Netherlands.
Frederik BarkhofQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom; National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, United Kingdom; Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, University College London, London, United Kingdom; Department of Radiology and Nuclear Medicine, VU University Medical Centre, Amsterdam, the Netherlands. Electronic address: f.barkhof@ucl.ac.uk.
Ferran PradosQueen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom; National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, United Kingdom; Centre for Medical Image Computing, Department of Medical Physics and Bioengineering, University College London, London, United Kingdom; Open University of Catalonia, Barcelona, Spain.
Queen Mary University of London · GBAmsterdam UMC Location VUmc · NLNational Hospital for Neurology and Neurosurgery · GB

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe aimed to evaluate the implications for clinical trial design of the generalised boundary-shift integral (GBSI) for spinal cord atrophy measurement.

methodsWe included 220 primary-progressive multiple sclerosis patients from a phase 2 clinical trial, with baseline and week-48 3DT1-weighted MRI of the brain and spinal cord (1 × 1 × 1 mm

resultsDepending on the spinal cord segment, we included 67.4-98.1% patients for CSA measurements, and 66.9-84.2% for GBSI. Spinal cord atrophy measurements obtained with GBSI had lower measurement variability, than corresponding CSA. Looking at the image noise floor, the lowest median standard deviation of the MRI signal within the cerebrospinal fluid surrounding the spinal cord was found on brain MRI at the C1-2 level. Spinal cord atrophy derived from brain MRI was related to the corresponding measures from dedicated spinal cord MRI, more strongly for GBSI than CSA. Spinal cord atrophy measurements using GBSI, but not CSA, were associated with upper and lower limb motor progression. DISCUSSION: Notwithstanding the reduced measurement variability, the clinical correlates, and the possibility of using brain acquisitions, spinal cord atrophy using GBSI should remain a secondary outcome measure in MS studies, until further advancements increase the quality of acquisition and reliability of processing.

Indexed as

Multiple SclerosisMultiple Sclerosis, Chronic ProgressiveAtrophyBrainCross-Sectional StudiesHumansMagnetic Resonance ImagingReproducibility of ResultsSample SizeSpinal CordAtrophyClinical trialGBSIMultiple sclerosisSpinal cord

Identifiers

PMID32961403
PMCPMC7509079
OpenAlexW3083861872

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.