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Background <p>A 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.</p> Methods <p>In 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.</p> Results <p>The training set included 531 patients (age 48 ± 16&#xa0;years; MRI at 19 ± 8&#xa0;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&#xa0;years; MRI at 21 ± 12&#xa0;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 After excluding patients for whom life sustaining therapies were withdrawn (n = 104), specificity was 96.6% and 82.4% for UFO and FO classification, respectively.</p> Conclusion <p>This 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. </p>

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

  • 期刊论文

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  • 发表日期：

    2026年3月4日

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  • Louis Puybasset,
  • Pierre Siméone,
  • Martin Grange,
  • Didier Cassereau,,
  • Damien Galanaud,
  • Rémy Bernard,
  • Lionel Velly,
  • Vincent Perlbarg,
  • Clément Capdeville,
  • Valentine Battisti,
  • Lamine Abdennour,
  • Adrien Bouglé,
  • Jean-Michel Constantin,
  • Antoine Monsel,
  • Juliette Chommeloux,
  • Julien Mayaux,
  • Benjamin Rohaut,
  • Lionel Naccache,
  • Alice Jacquens,
  • Vincent Degos,
  • Mélanie Pélégrini-Issac,
  • Louis Puybasset,
  • Steven Laureys,
  • Carol Di Perri,
  • Andrea Soddu,
  • Audrey Vanhaudenhuyse,
  • Manon Carriere,
  • Charlène Aubinet,
  • Sandrine Molinier,
  • Thomas Tourdias,
  • Olivier Verdonck,
  • Vincent Cottenceau,
  • François Sztark,
  • Betty Jean,
  • Russel Chabanne,
  • Jean-Michel Constantin,
  • Nadine Girard,
  • Nicolas Bruder,
  • Damien Galanaud,
  • Charles-Edouard Luyt,
  • Jean Chastre,
  • Julien Amour,
  • Charlotte Arbelot,
  • Corine Vezinet,
  • Jean-Jacques Rouby,
  • Mathieu Raux,
  • Olivier Langeron,
  • Vincent Degos,
  • Francis Bolgert,
  • Nicolas Weiss,
  • Sophie Demeret,
  • Benjamin Rohaut,
  • Thomas Similowski,
  • Alexandre Demoule,
  • Alexandre Duguet,
  • Jean-Albert Lotterie,
  • Stein Silva,
  • Michèle Génestal,
  • Gustavo Sotoares,
  • Emmanuel Vega,
  • Leila Chamard,
  • Thomas Ritzenthaler,
  • Frederic Dailler,
  • Nicolas Menjot de Champfleur,
  • Jean-Paul Roustan,
  • Laurent Barral,
  • Eléonore Tollard,
  • Benoît Veber,
  • Stéphane Kremer,
  • Julien Pottecher,
  • Pierre-Guy Durand,
  • Catherine Oppenheim,
  • Nathalie Laquay,
  • Sandrine Mons,
  • David Couret,
  • Mirko Patassini,
  • Giuseppe Citerio,
  • Alessia Vargioglu,
  • Cristina Agostinis,
  • Paolo Gritti,
  • Mariagiulia Anglani,
  • Marina Munari,
  • Maggiore della Carita-Emergenza,
  • Moreno Curti,
  • Francesco Della Corte,
  • Francesca Grossi,
  • Livia Errico,
  • Roberto Alberto De Blasi,
  • Andrew IR Maas,
  • David Menon,
  • Linda Lanyon,
  • Ewout Steyerberg,
  • Nicole von Steinbüchel,
  • Alexandra Brazinova,
  • Andras Buki,
  • Olli Tenovuo,
  • Wim Van Hecke,
  • Louis Puybasset,
  • Giuseppe Citerio,
  • Sylvia Richardson,
  • Marc Maegele,
  • Wilco Peul,
  • Fiona Lecky,
  • Steven Laureys,
  • Martin Dietz,
  • William Stewart,
  • Oliver Sakowitz,
  • Daniel Rueckert,
  • Jyrki Lötjönen,
  • Lindsay Wilson,
  • Martin Fabricius,
  • Nada Andelic,
  • Helen Dawes,
  • Simon Stanworth,
  • Nino Stocchetti,
  • Samuli Ripatti,
  • Geoff Manley,
  • Kevin K.W. Wang,
  • Jed Hartings,
  • Jamie Cooper,
  • Valery Feigin,
  • Ji-yao Jiang,
  • Silke Schmidt,
  • Alexandra Brazinova,
  • Jens Dreier

摘要

Background

A 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.

Methods

In 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.

Results

The 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.

Conclusion

This 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.