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Added Value of Quantitative Apparent Diffusion Coefficient Values for Neuroprognostication After Cardiac Arrest
Anke Wouters1, Lauranne Scheldeman2, Sam Plessers2
1From the Departments of Neurology (A.W., L.S., S.P., R.L.), Radiology (R.P., S.C., P.D.), and Cardiology (S.J., K.A.), University Hospitals Leuven; Laboratory of Neurobiology (A.W., L.S., R.L.), Center for Brain & Disease Research, VIB; Department of Neurosciences (A.W., L.S., R.L.), Experimental Neurology and Leuven Brain Institute, University of Leuven, Belgium; Department of Neurology (A.W.), Academic Medical Center, University of Amsterdam, the Netherlands; Translational MRI, Department of Imaging and Pathology (R.P., S.C., P.D.), and Laboratory for Epilepsy Research, Department of Neurosciences (W.V.P.), KU Leuven, Belgium; Department of Cardiology (B.F., M.D., J.D., K.A.), Ziekenhuis Oost-Limburg, Genk; and Faculty of Medicine and Life Sciences (J.D., K.A.), Faculty of Medicine and Life Sciences, University Hasselt, Diepenbeek, Belgium. anke.wouters@med.kuleuven.be.
Brain MRI, specifically average apparent diffusion coefficient (ADC) values, significantly improves the prediction of good neurologic recovery in cardiac arrest survivors. Adding MRI data to clinical factors enhances prognostic accuracy for these patients.
Area of Science:
- Neuroscience
- Radiology
- Critical Care Medicine
Background:
- Accurate prognostication of neurologic recovery after cardiac arrest (CA) is crucial for patient management.
- Clinical and electrophysiologic variables are established predictors, but their prognostic value may be limited.
- Brain magnetic resonance imaging (MRI) offers detailed structural and functional information.
Purpose of the Study:
- To evaluate the prognostic value of brain MRI, particularly apparent diffusion coefficient (ADC) values, in patients post-cardiac arrest.
- To compare the predictive accuracy of MRI-based models with models using only clinical and electrophysiologic data.
Main Methods:
- Prospective collection of brain MRI data from patients in the Neuroprotect Post-CA trial.
- Calculation of receiver operating characteristic (ROC) curves for average ADC values and voxel-specific ADC thresholds.
- Development of multivariable logistic regression models incorporating clinical characteristics, EEG, SSEP, and ADC values to predict neurologic recovery.
Main Results:
- Average ADC value in the postcentral cortex showed the highest univariable accuracy (AUC 0.78) for predicting good neurologic recovery.
- A multivariable model including corneal reflexes, EEG, and average ADC value achieved superior prediction (AUC 0.96, false-positive rate 27%).
- This MRI-enhanced model demonstrated significantly better prediction compared to models using only EEG, corneal reflexes, and SSEP (p=0.03).
Conclusions:
- Brain MRI, specifically ADC measures, serves as an independent predictor of neurologic recovery in post-cardiac arrest patients.
- Incorporating brain MRI data into multivariable models significantly improves the accuracy of prognostication for neurologic recovery post-CA.
- This study provides Class III evidence supporting the use of MRI ADC features for predicting neurologic outcomes.
