Evidence map›Paper›PMID 41782003›Full record

ArticleCritical care (London, England)2026

Deep white matter MRI predicts outcomes in coma of various etiologies: a cohort study.

Louis Puybasset, Pierre Siméone, Martin Grange, Didier Cassereau, Damien Galanaud, Rémy Bernard, Lionel Velly, Vincent Perlbarg, Clément Capdeville, Valentine Battisti and 13 more

Abstract readMulticenter Study
In one paragraph

Article in Critical care (London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

23 authors.

Louis PuybassetAPHP, Hôpital Pitié-Salpêtrière, Neurosurgical Intensive Care Unit, Sorbonne Université, Paris, France. louis.puybasset@aphp.fr.
Pierre Siméone *CNRS, INSERM, Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, Paris, France.
Martin Grange *CNRS, INSERM, Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, Paris, France.
Didier CassereauCNRS, INSERM, Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, Paris, France.
Damien GalanaudCNRS, INSERM, Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, Paris, France.
Rémy BernardAPHP, Hôpital Pitié-Salpêtrière, Neurosurgical Intensive Care Unit, Sorbonne Université, Paris, France.
Lionel VellyDepartment of Anesthesiology and Intensive Care, APHM, Aix-Marseille Université, CHU Timone, Marseille, France.
Vincent PerlbargBrainTale SAS, Strasbourg, France.
Clément CapdevilleAPHP, Hôpital Pitié-Salpêtrière, Neurosurgical Intensive Care Unit, Sorbonne Université, Paris, France.
Valentine BattistiAPHP, Hôpital Pitié-Salpêtrière, Neurosurgical Intensive Care Unit, Sorbonne Université, Paris, France.
Lamine AbdennourAPHP, Hôpital Pitié-Salpêtrière, Neurosurgical Intensive Care Unit, Sorbonne Université, Paris, France.
Adrien BougléDepartment of Cardiovascular and Thoracic Surgery, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, Institute of Cardiology, Paris, France.
Jean-Michel ConstantinDepartment of Anaesthesiology and Critical Care, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, GRC 29, DMU DREAM, Paris, France.
Antoine MonselDepartment of Anaesthesiology and Critical Care, APHP, Sorbonne Université, Hôpital Pitié-Salpêtrière, GRC 29, DMU DREAM, Paris, France.
Juliette ChommelouxAPHP, Hôpital Pitié-Salpêtrière, Institut de Cardiologie, Medical Intensive Care Unit, Sorbonne Université, Paris, France.
Julien MayauxAPHP, Hôpital Pitié-Salpêtrière, Médecine Intensive Réanimation (Département "R3S"), Sorbonne Université, Paris, France.
Benjamin RohautINSERM, CNRS, Institut du Cerveau-Paris Brain Institute-ICM, Sorbonne Université, Paris, France.
Lionel NaccacheINSERM, CNRS, Institut du Cerveau-Paris Brain Institute-ICM, Sorbonne Université, Paris, France.
Alice JacquensAPHP, Hôpital Pitié-Salpêtrière, Neurosurgical Intensive Care Unit, Sorbonne Université, Paris, France.
Vincent DegosAPHP, Hôpital Pitié-Salpêtrière, Neurosurgical Intensive Care Unit, Sorbonne Université, Paris, France.
Mélanie Pélégrini-IssacCNRS, INSERM, Laboratoire d'Imagerie Biomédicale (LIB), Sorbonne Université, Paris, France.
MRI-COMA participants and investigators
CENTER-TBI principal investigators

Funding

Bourse à la mobilité de Phoceo 2024Bourse à la mobilité de SFAR 2024"Investissements d'avenir" ANR-10-IAIHU-06Italian Ministry of health and Regione Lombardia Ricerca Finalizzata 2010 - RF-2010-2319503Programme Hospitalier de Recherche Clinique 2005 #051061
6 · The paper itself

Abstract

backgroundA reliable outcome prognostication tool for patients in coma of various etiologies would facilitate ICU treatment by providing objective information to caregivers and patients' relatives. This study aimed to predict outcome based on supervised machine learning and magnetic resonance diffusion tensor imaging (DTI) metrics.

methodsIn this multicenter international study, a training set of 531 patients not responding to simple orders at day 5 after coma onset underwent diffusion-weighted MRI between day 5 and 45. A classifier was developed using DTI metrics, patient age, and delay between admission and MRI as features. Unfavorable outcome (UFO) was defined as GOSE 1-4 at one year. Three prognosis areas were defined: a "red" zone (specificity for UFO above 95%), a "green" zone (specificity for favorable outcome, FO, above 90%), and a "no determination zone" (NDZ) for patients classified in neither the red or green zone. The classifier was validated on an external test set of 211 patients.

resultsThe training set included 531 patients (age 48 ± 16 years; MRI at 19 ± 8 days post-injury), with 75.9% GOSE 1-4 and 24.1% GOSE 5-8 at one year. Normalized DTI metrics were FA 0.82 ± 0.12 and MD 1.10 ± 0.13. The external test set (n = 211; age 47 ± 16 years; MRI at 21 ± 12 days) showed similar outcome distribution (75.4% GOSE 1-4, 24.6% GOSE 5-8) and DTI values (FA 0.83 ± 0.09, MD 1.07 ± 0.12). Both sets were comparable in age, sex, initial GCS, and outcome ratios. In the external test set, ROC AUC was 0.89. For UFO classification, specificity was 98.1%, PPV 99.1%, and sensitivity 68.6%. For FO classification, specificity was 95.0%, PPV 77.8%, and NPV 86.3% whereas 30.8% of the patients were in the NDZ. After excluding patients for whom life sustaining therapies were withdrawn (n = 104), specificity was 96.6% and 82.4% for UFO and FO classification, respectively.

conclusionThis classifier demonstrates a high specificity to predict coma outcome while patients are still in the ICU, irrespective of coma etiology. These results may assist practitioners in making informed decisions.

Indexed as

ComaMagnetic Resonance ImagingWhite MatterAdultAgedCohort StudiesDiffusion Tensor ImagingFemaleHumansMaleMiddle AgedPredictive Value of TestsPrognosisROC CurveComaDeep white matterDiffusion tensor imagingOutcomePrognosis

Identifiers

PMID41782003
PMCPMC13227785

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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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 Socratic graph.