Evidence map›Paper›PMID 34904191›Full record

ArticleIntensive care medicine2022

Prognostic value of global deep white matter DTI metrics for 1-year outcome prediction in ICU traumatic brain injury patients: an MRI-COMA and CENTER-TBI combined study.

Louis Puybasset, Vincent Perlbarg, Jean Unrug, Didier Cassereau, Damien Galanaud, Grégory Torkomian, Valentine Battisti, Muriel Lefort, Lionel Velly, Vincent Degos and 4 more

Erratum issued 2 registry-linked trialsOpen access · greenAbstract readMulticenter Study
PubMed Publisher
In one paragraph

Article in Intensive care medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to 2 registered trials, which are not on this map. Cited by 33 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 1 pooled it
3.6field-weighted citation impact, top 6% 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.

NCT00577954 completednot on this map

Multimodal Magnetic Resonance (MRI) Development in Comatose Patients for an Algorithm in the Prediction of Consciousness Recovery

TypeobservationalSponsorAssistance Publique - Hôpitaux de ParisRan2006 to 2014Enrolled417ConditionsComaArmsMultimodal MRI
NCT02210221 completednot on this map

Collaborative European NeuroTrauma Effectiveness Research in TBI (CENTER-TBI)

Typeobservational_patient_registrySponsorUniversity Hospital, AntwerpRan2014 to 2021Enrolled4,559ConditionsTraumatic Brain Injury
3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 1 synthesis or guideline pooled it, 50 citations in OpenAlex.

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  8. Preservation of anticorrelated brain networks predicts recovery after traumatic brain injury.Proceedings of the National Academy of Sciences of the United States of America · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors at 4 institutions in 2 countries.

Louis PuybassetNeurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France. louis.puybasset@aphp.fr.ORCID http://orcid.org/0000-0002-6849-1825
Vincent PerlbargBrainTale SAS, Paris, France.ORCID http://orcid.org/0000-0002-3438-2479
Jean UnrugNeurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France.
Didier CassereauLaboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France.ORCID http://orcid.org/0000-0003-2412-2144
Damien GalanaudLaboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France.ORCID http://orcid.org/0000-0002-9285-8121
Grégory TorkomianNeurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France.ORCID http://orcid.org/0000-0003-0175-945X
Valentine BattistiNeurosurgical Intensive Care Unit, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Paris, France.ORCID http://orcid.org/0000-0003-3041-7141
Muriel LefortLaboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France.ORCID http://orcid.org/0000-0001-7820-3754
Lionel VellyDepartment of Anesthesiology and Critical Care Medicine, University Hospital Timone, AP-HM, Aix Marseille University, Marseille, France.ORCID http://orcid.org/0000-0001-5009-1711
Vincent DegosClinical Research Group 29, Sorbonne Université, Paris, France.ORCID http://orcid.org/0000-0002-6540-5725
Guiseppe CiterioDepartment of Medicine and Surgery, University of Milano-Bicocca, Monza, Italy.ORCID http://orcid.org/0000-0002-5374-3161
Éléonore BayenLaboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France.ORCID http://orcid.org/0000-0002-0286-5521
Mélanie Pelegrini-IssacLaboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, CNRS, INSERM, Paris, France.ORCID http://orcid.org/0000-0003-3260-5795
MRI-COMA Investigators CENTER-TBI MRI Participants and MRI Only Investigators
Centre National de la Recherche Scientifique · FRSorbonne Université · FRAzienda Ospedaliera San Gerardo · ITInserm · FR

Funding

agence nationale de la recherche Investissements d'Avenir ANR-10-IAIHU-06ministère des affaires sociales, de la santé et des droits des femmes PHRC 2005#05061ministero della salute Ricerca Finalizzata 2010 - RF-2010-2319503regione lombardia Ricerca Finalizzata 2010 - RF-2010-2319503seventh framework programme EC grant 602150
6 · The paper itself

Abstract

purposeA reliable tool for outcome prognostication in severe traumatic brain injury (TBI) would improve intensive care unit (ICU) decision-making process by providing objective information to caregivers and family. This study aimed at designing a new classification score based on magnetic resonance (MR) diffusion metrics measured in the deep white matter between day 7 and day 35 after TBI to predict 1-year clinical outcome.

methodsTwo multicenter cohorts (29 centers) were used. MRI-COMA cohort (NCT00577954) was split into MRI-COMA-Train (50 patients enrolled between 2006 and mid-2014) and MRI-COMA-Test (140 patients followed up in clinical routine from 2014) sub-cohorts. These latter patients were pooled with 56 ICU patients (enrolled from 2014 to 2020) from CENTER-TBI cohort (NCT02210221). Patients were dichotomised depending on their 1-year Glasgow outcome scale extended (GOSE) score: GOSE 1-3, unfavorable outcome (UFO); GOSE 4-8, favorable outcome (FO). A support vector classifier incorporating fractional anisotropy and mean diffusivity measured in deep white matter, and age at the time of injury was developed to predict whether the patients would be either UFO or FO.

resultsThe model achieved an area under the ROC curve of 0.93 on MRI-COMA-Train training dataset, and 49% sensitivity for 96.8% specificity in predicting UFO and 58.5% sensitivity for 97.1% specificity in predicting FO on the pooled MRI-COMA-Test and CENTER-TBI validation datasets.

conclusionThe model successfully identified, with a specificity compatible with a personalized decision-making process in ICU, one in two patients who had an unfavorable outcome at 1 year after the injury, and two-thirds of the patients who experienced a favorable outcome.

Indexed as

Brain Injuries, TraumaticWhite MatterHumansIntensive Care UnitsMagnetic Resonance ImagingPrognosisDeep white matterDiffusion tensor imagingOutcomePrognosisTraumatic brain injury

Identifiers

PMID34904191
OpenAlexW4200554051

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.