Evidence map›Paper›PMID 37492673›Full record

ArticleFrontiers in radiology2022

Confounder-adjusted MRI-based predictors of multiple sclerosis disability.

Yujin Kim, Mihael Varosanec, Peter Kosa, Bibiana Bielekova

Registry-linked trialAbstract read
In one paragraph

Article in Frontiers in radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT00794352 (Comprehensive Multimodal Analysis of Patients With Neuroimmunological Diseases of the CNS), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
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.

NCT00794352 recruitingnot on this map

Comprehensive Multimodal Analysis of Patients With Neuroimmunological Diseases of the CNS

TypeobservationalSponsorNational Institute of Allergy and Infectious Diseases (NIAID)Ran2008Enrolled2,400ConditionsCentral Nervous System Disease, Multiple Sclerosis
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
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

4 authors.

Yujin KimLaboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institutes of Health, National Institute of Allergy and Infectious Diseases, Bethesda, MD, United States.
Mihael VarosanecLaboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institutes of Health, National Institute of Allergy and Infectious Diseases, Bethesda, MD, United States.
Peter KosaLaboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institutes of Health, National Institute of Allergy and Infectious Diseases, Bethesda, MD, United States.
Bibiana BielekovaLaboratory of Clinical Immunology and Microbiology, Neuroimmunological Diseases Section, National Institutes of Health, National Institute of Allergy and Infectious Diseases, Bethesda, MD, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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 ( 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 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.

Indexed as

disability outcomesgradient boosting machinemachine learningmagnetic resonance imagingmultiple sclerosisphysiological confounders

Identifiers

PMID37492673
PMCPMC10365278

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Registered trials

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.