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Added Value of Quantitative Apparent Diffusion Coefficient Values for Neuroprognostication After Cardiac Arrest
Tijdschriftbijdrage - Tijdschriftartikel
OBJECTIVE: To test the prognostic value of brain MRI in addition to clinical and electrophysiologic variables in patients post-cardiac arrest (CA), we explored data from the randomized Neuroprotect Post-CA trial (NCT02541591). METHODS: In this trial, brain MRIs were prospectively obtained. We calculated receiver operating characteristic (ROC) curves for the average apparent diffusion coefficient (ADC) value and percentage of brain voxels with an ADC value <650 × 10-6 mm2/s and <450 × 10-6 mm2/s. We constructed multivariable logistic regression models with clinical characteristics, EEG, somatosensory evoked potentials (SSEP), and ADC value as independent variables to predict good neurologic recovery. RESULTS: In 79/102 patients, MRI data were available and in 58/79 patients all other data were available. At 180 days post-CA, 25/58 (43%) patients had good neurologic recovery. In univariable analysis of all tested MRI measures, average ADC value in the postcentral cortex had the highest accuracy to predict good neurologic recovery, with an area under the ROC curve (AUC) of 0.78. In the most optimal multivariable model, which also included corneal reflexes and EEG, this measure remained an independent predictor of good neurologic recovery (AUC 0.96, false-positive 27%). This model provided a more accurate prediction compared to the most optimal combination of EEG, corneal reflexes, and SSEP (p = 0.03). CONCLUSIONS: Adding information on brain MRI in a multivariable model may improve the prediction of good neurologic recovery in patients post-CA. CLASSIFICATION OF EVIDENCE: This study provides Class III evidence that MRI ADC features predict neurologic recovery in patients post-CA.
Pagina's: E2611 - E2618