• Stein Silva, Patrice Peran, Lionel Kerhuel, Briguita Malagurski, Nicolas Chauveau, Benoit Bataille, Jean Albert Lotterie, Pierre Celsis, Florent Aubry, Giuseppe Citerio, Betty Jean, Russel Chabanne, Vincent Perlbarg, Lionel Velly, Damien Galanaud, Audrey Vanhaudenhuyse, Olivier Fourcade, Steven Laureys, and Louis Puybasset.
• 1Department of Anaesthesiology and Critical Care, Critical Care Unit, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse Cedex 9, France.2Critical Care and Anaesthesiology Department, University Teaching Hospital of Purpan, Place du Dr Baylac, Toulouse Cedex 9, France.3Toulouse NeuroImaging Center, Toulouse University, Inserm, UPS, France.4Department of Anaesthesiology and Critical Care, Critical Care Unit, Hopital Dieu Hospital, Narbonne, France.5Department of Anaesthesiology and Critical Care, School of medicine and Surgery, University Milano Bicocca and Hospital San Gerardo, Monza, Italy.6Department of Neuroradiology, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France.7Department of Anaesthesiology and Critical Care, University Hospital of Clermont-Ferrand, Clermont-Ferrand, France.8Laboratoire d'Imagerie Biomédicale (UMR S 1146/UMR 7371), Université Pierre-et-Marie-Curie-Paris 06, Paris, France.9Critical Care and Anaesthesiology Department, Groupe Hospitalier Pitié-Salpétrière, APHP, Paris, France.10Department of Neuroradiology, Groupe Hospitalier Pitié-Salpétrière, APHP, Paris, France.11Cyclotron Research Center and Department of Neurology, University Hospital and University of Liège, Liège, Belgium.12Algology and Palliative Care Department, University Hospital and University of Liège, Liège, Belgium.
• Crit. Care Med. 2017 Aug 1; 45 (8): e763e771e763-e771.

ObjectivesWe hypothesize that the combined use of MRI cortical thickness measurement and subcortical gray matter volumetry could provide an early and accurate in vivo assessment of the structural impact of cardiac arrest and therefore could be used for long-term neuroprognostication in this setting.DesignProspective cohort study.SettingFive Intensive Critical Care Units affiliated to the University in Toulouse (France), Paris (France), Clermont-Ferrand (France), Liège (Belgium), and Monza (Italy).PatientsHigh-resolution anatomical T1-weighted images were acquired in 126 anoxic coma patients ("learning" sample) 16 ± 8 days after cardiac arrest and 70 matched controls. An additional sample of 18 anoxic coma patients, recruited in Toulouse, was used to test predictive model generalization ("test" sample). All patients were followed up 1 year after cardiac arrest.InterventionsNone.Measurements And Main ResultsCortical thickness was computed on the whole cortical ribbon, and deep gray matter volumetry was performed after automatic segmentation. Brain morphometric data were employed to create multivariate predictive models using learning machine techniques. Patients displayed significantly extensive cortical and subcortical brain volumes atrophy compared with controls. The accuracy of a predictive classifier, encompassing cortical and subcortical components, has a significant discriminative power (learning area under the curve = 0.87; test area under the curve = 0.96). The anatomical regions which volume changes were significantly related to patient's outcome were frontal cortex, posterior cingulate cortex, thalamus, putamen, pallidum, caudate, hippocampus, and brain stem.ConclusionsThese findings are consistent with the hypothesis of pathologic disruption of a striatopallidal-thalamo-cortical mesocircuit induced by cardiac arrest and pave the way for the use of combined brain quantitative morphometry in this setting.

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