Evidence map›Paper›PMID 38896232›Full record

ArticleEuropean radiology2024

The prognostic importance of traumatic axonal injury on early MRI: the Trondheim TAI-MRI grading and quantitative models.

Kent Gøran Moen, Anne-Mari Holte Flusund, Hans Kristian Moe, Nada Andelic, Toril Skandsen, Asta Håberg, Kjell Arne Kvistad, Øystein Olsen, Elin Hildrum Saksvoll, Sebastian Abel-Grüner and 3 more

Abstract read
In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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.

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

11 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Kent Gøran MoenDepartment of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway. kent.g.moen@ntnu.no.ORCID http://orcid.org/0000-0003-0937-1986
Anne-Mari Holte FlusundDepartment of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.
Hans Kristian MoeDepartment of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.
Nada AndelicInstitute of Health and Society, Research Centre for Habilitation and Rehabilitation Models and Services (CHARM), Faculty of Medicine, University of Oslo, P.O. Box 1130 Blindern, 0318, Oslo, Norway.
Toril SkandsenDepartment of Neuromedicine and Movement Science, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology (NTNU), 7491, Trondheim, Norway.
Asta HåbergDepartment of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway.
Kjell Arne KvistadDepartment of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway.
Øystein OlsenDepartment of Radiology, Nord-Trøndelag Hospital Trust, Levanger Hospital, 7600, Levanger, Norway.
Elin Hildrum SaksvollDepartment of Radiology, Nord-Trøndelag Hospital Trust, Levanger Hospital, 7600, Levanger, Norway.
Sebastian Abel-GrünerDepartment of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway.
Audny AnkeDepartment of Rehabilitation, University Hospital of North Norway, 9038, Tromsø, Norway.
Turid Follestad *Department of Clinical and Molecular Medicine, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7491, Trondheim, Norway.
Anne Vik *Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, 7006, Trondheim, Norway.

Funding

EraNet Neuron TAI-MRI project
6 · The paper itself

Abstract

objectivesWe analysed magnetic resonance imaging (MRI) findings after traumatic brain injury (TBI) aiming to improve the grading of traumatic axonal injury (TAI) to better reflect the outcome.

methodsFour-hundred sixty-three patients (8-70 years) with mild (n = 158), moderate (n = 129), or severe (n = 176) TBI and early MRI were prospectively included. TAI presence, numbers, and volumes at predefined locations were registered on fluid-attenuated inversion recovery (FLAIR) and diffusion-weighted imaging, and presence and numbers on T2*GRE/SWI. Presence and volumes of contusions were registered on FLAIR. We assessed the outcome with the Glasgow Outcome Scale Extended. Multivariable logistic and elastic-net regression analyses were performed.

resultsThe presence of TAI differed between mild (6%), moderate (70%), and severe TBI (95%). In severe TBI, bilateral TAI in mesencephalon or thalami and bilateral TAI in pons predicted worse outcomes and were defined as the worst grades (4 and 5, respectively) in the Trondheim TAI-MRI grading. The Trondheim TAI-MRI grading performed better than the standard TAI grading in severe TBI (pseudo-R

conclusionsWe propose the Trondheim TAI-MRI grading (grades 1-5) with bilateral TAI in mesencephalon or thalami, and bilateral TAI in pons as the worst grades. The predictive value was highest for the quantitative models including FLAIR volume of TAI and contusions (GCS score <13) or FLAIR volume of contusions (GCS score ≥ 13), which emphasise artificial intelligence as a potentially important future tool. CLINICAL RELEVANCE STATEMENT: The Trondheim TAI-MRI grading reflects patient outcomes better in severe TBI than today's standard TAI grading and can be implemented after external validation. The prognostic importance of volumetric models is promising for future use of artificial intelligence technologies. KEY POINTS: Traumatic axonal injury (TAI) is an important injury type in all TBI severities. Studies demonstrating which MRI findings that can serve as future biomarkers are highly warranted. This study proposes the most optimal MRI models for predicting patient outcome at 6 months after TBI; one updated pragmatic model and a volumetric model. The Trondheim TAI-MRI grading, in severe TBI, reflects patient outcome better than today's standard grading of TAI and the prognostic importance of volumetric models in all severities of TBI is promising for future use of AI.

Indexed as

Brain Injuries, TraumaticMagnetic Resonance ImagingAdolescentAdultAgedChildDiffuse Axonal InjuryFemaleHumansMaleMiddle AgedPrognosisProspective StudiesYoung AdultArtificial intelligenceCraniocerebral traumaDiffuse axonal injuryNeuroimagingTrauma severity indices

Identifiers

PMID38896232
PMCPMC11557676

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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.