title: Diffusion tensor imaging to predict long-term outcome after cardiac arrest: a bicentric pilot study.
abstract: BACKGROUND
  Prognostication in comatose survivors of cardiac arrest is a major clinical challenge. The authors' objective was to determine whether an assessment with diffusion tensor imaging, a brain magnetic resonance imaging sequence, increases the accuracy of 1 yr functional outcome prediction in cardiac arrest survivors.
  
  
  METHODS
  Prospective, observational study in two intensive care units. Fifty-seven comatose survivors of cardiac arrest underwent brain magnetic resonance imaging. Fractional anisotropy (FA), a diffusion tensor imaging value, was measured in predefined white matter regions, and apparent diffusion coefficient was assessed in predefined grey matter regions. Prediction of unfavorable outcome at 1 yr was compared using four prognostic models: FA global, FA selected, apparent diffusion coefficient, and clinical classifiers.
  
  
  RESULTS
  Of the 57 patients included in the study, 49 had an unfavorable outcome at 12 months. Areas under the receiver operating characteristic curve (95% CI) to predict unfavorable outcome for the FA global, FA selected, clinical, and apparent diffusion coefficient models were 0.92 (0.82-0.98), 0.96 (0.87-0.99), 0.78 (0.65-0.88), and 0.86 (0.74-0.94), respectively. The FA selected model had the best overall accuracy for predicting outcome, with a score above 0.44 having 94% (95% CI, 83-99%) sensitivity and 100% (95% CI, 63-100%) specificity for the prediction of unfavorable outcome.
  
  
  CONCLUSIONS
  Quantitative diffusion tensor imaging indicates that white matter damage is widespread after cardiac arrest. A prognostic model based on FA values in selected white matter tracts seems to predict accurately 1 yr functional outcome. These preliminary results need to be confirmed in a larger population.
authors.affiliation: Service de Réanimation, School of Medicine, Groupe Hospitalier Pitié-Salpêtrière, Assistance Publique-Hôpitaux de Paris, Université Paris-Pierre-et-Marie-Curie, Paris, France. charles-edouard.luyt@psl.aphp.fr
authors.forename: Charles-Edouard
authors.initials: CE
authors.lastname: Luyt
authors.forename: Damien
authors.initials: D
authors.lastname: Galanaud
authors.forename: Vincent
authors.initials: V
authors.lastname: Perlbarg
authors.forename: Audrey
authors.initials: A
authors.lastname: Vanhaudenhuyse
authors.forename: Robert D
authors.initials: RD
authors.lastname: Stevens
authors.forename: Rajiv
authors.initials: R
authors.lastname: Gupta
authors.forename: Hortense
authors.initials: H
authors.lastname: Besancenot
authors.forename: Alexandre
authors.initials: A
authors.lastname: Krainik
authors.forename: Gérard
authors.initials: G
authors.lastname: Audibert
authors.forename: Alain
authors.initials: A
authors.lastname: Combes
authors.forename: Jean
authors.initials: J
authors.lastname: Chastre
authors.forename: Habib
authors.initials: H
authors.lastname: Benali
authors.forename: Steven
authors.initials: S
authors.lastname: Laureys
authors.forename: Louis
authors.initials: L
authors.lastname: Puybasset
mesh_terms: D000328:Adult; D000368:Aged; D001921:Brain / Q000378:metabolism / Q000473:pathology; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D005500:Follow-Up Studies; D006323:Heart Arrest / Q000175:diagnosis* / Q000378:metabolism / Q000628:therapy*; D006801:Humans; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated / Q000378:metabolism / Q000473:pathology*; D010865:Pilot Projects; D011237:Predictive Value of Tests; D011446:Prospective Studies; D013997:Time Factors; D016896:Treatment Outcome