Citation

Galanaud D, Perlbarg V, Gupta R, Stevens RD, Sanchez P, Tollard E, de Champfleur NM, Dinkel J, Faivre S, Soto-Ares G, Veber B, Cottenceau V, Masson F, Tourdias T, André E, Audibert G, Schmitt E, Ibarrola D, Dailler F, Vanhaudenhuyse A, Tshibanda L, Payen JF, Le Bas JF, Krainik A, Bruder N, Girard N, Laureys S, Benali H, Puybasset L. Anesthesiology 2012; 117(6): 1300-1310.

Affiliation

* Associate Professor, Department of Neuroradiology, Pitié Salpêtrière Hospital, & Université Pierre et Marie Curie (Paris VI), Boulevard de l'Hôpital, Paris, France. † Research Associate, INSERM, UMRS, Université Pierre et Marie Curie-Paris, Paris, France. ‡ Doctor, Department of Neuroradiology, Massachusetts General Hospital, Boston, Massachusetts. § Associate Professor, Division of Neuroscience Critical Care, Johns Hopkins University School of Medicine, Baltimore, Maryland. ‖ Doctor, Neurosurgical Intensive Care Unit, Pitié Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, Paris, France. # Doctor, Department of Neuroradiology, Centre Hospitalier Universitaire, Rouen, France. ** Doctor, Department of Neuroradiology, Guy de Chauliac Hospital, Montpellier, France. †† Doctor, Department of Anesthesia and Surgical Intensive Care Unit, ‡‡ Doctor, Department of Neuroradiology, Roger Salengro Hospital, Lille, France. §§ Professor, Surgical Intensive Care Unit, Centre Hospitalier Universitaire, Rouen, France. ‖‖ Doctor, Department of Neuroradiology, ## Doctor, Department of Anesthesia and Surgical Intensive Care Unit, Centre Hospitalier Universitaire, Bordeaux, France. *** Doctor, Department of Anesthesia & Intensive Care Unit, Guy de Chauliac Hospital, Montpellier, France. ††† Professor, Department of Anesthesia and Surgical Intensive Care Unit, ‡‡‡ Doctor, Department of Neuroradiology, Centre Hospitalier Universitaire, Nancy, France. §§§ Research Associate, CERMEP, Pierre Wertheimer Neurological & Neurosurgical Hospital, Bron, France. ‖‖‖ Surgical Intensive Care Unit, Pierre Wertheimer Neurological & Neurosurgical Hospital, Lyon, France. ### Research Associate, Cyclotron Research Center, **** Doctor, Department of Neuroradiology, University of Liège, Liège, Belgium. †††† Professor, Department of Anesthesia and Surgical Intensive Care Unit, ‡‡‡‡ Professor, Department of Neuroradiology, Michallon Hospital, La Tronche, France. §§§§ Professor, Department of Anesthesia & Intensive Care Unit, ‖‖‖‖ Professor, Department of Neuroradiology, La Timone Hospital, Marseille, France. #### Professor, Neurosurgical Intensive Care Unit, Pitié Salpêtrière Hospital, Assistance Publique-Hôpitaux de Paris, and Université Pierre et Marie Curie-Paris, Paris, France ***** See appendix for a supplementary list of investigators.

Copyright

(Copyright © 2012, American Society of Anesthesiologists, Publisher Lippincott Williams and Wilkins)

DOI

10.1097/ALN.0b013e3182755558

PMID

23135261

Abstract

BACKGROUND:: Existing methods to predict recovery after severe traumatic brain injury lack accuracy. The aim of this study is to determine the prognostic value of quantitative diffusion tensor imaging (DTI). METHODS:: In a multicenter study, the authors prospectively enrolled 105 patients who remained comatose at least 7 days after traumatic brain injury. Patients underwent brain magnetic resonance imaging, including DTI in 20 preselected white matter tracts. Patients were evaluated at 1 yr with a modified Glasgow Outcome Scale. A composite DTI score was constructed for outcome prognostication on this training database and then validated on an independent database (n = 38). DTI score was compared with the International Mission for Prognosis and Analysis of Clinical Trials Score. RESULTS:: Using the DTI score for prediction of unfavorable outcome on the training database, the area under the receiver operating characteristic curve was 0.84 (95% CI: 0.75-0.91). The DTI score had a sensitivity of 64% and a specificity of 95% for the prediction of unfavorable outcome. On the validation-independent database, the area under the receiver operating characteristic curve was 0.80 (95% CI: 0.54-0.94). On the training database, reclassification methods showed significant improvement of classification accuracy (P < 0.05) compared with the International Mission for Prognosis and Analysis of Clinical Trials score. Similar results were observed on the validation database. CONCLUSIONS:: White matter assessment with quantitative DTI increases the accuracy of long-term outcome prediction compared with the available clinical/radiographic prognostic score.

Language: en