TY - JOUR
T1 - Diffusion tensor imaging to predict long-term outcome after cardiac arrest
T2 - A bicentric pilot study
AU - Luyt, Charles Edouard
AU - Galanaud, Damien
AU - Perlbarg, Vincent
AU - Vanhaudenhuyse, Audrey
AU - Stevens, Robert D.
AU - Gupta, Rajiv
AU - Besancenot, Hortense
AU - Krainik, Alexandre
AU - Audibert, Gérard
AU - Combes, Alain
AU - Chastre, Jean
AU - Benali, Habib
AU - Laureys, Steven
AU - Puybasset, Louis
PY - 2012/12
Y1 - 2012/12
N2 - 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. CONCLUSION:: 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.
AB - 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. CONCLUSION:: 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.
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U2 - 10.1097/ALN.0b013e318275148c
DO - 10.1097/ALN.0b013e318275148c
M3 - Article
C2 - 23135257
AN - SCOPUS:84870250745
SN - 0003-3022
VL - 117
SP - 1311
EP - 1321
JO - Anesthesiology
JF - Anesthesiology
IS - 6
ER -