Brain magnetic resonance imaging (MRI) data may improve the prediction of neurologic recovery after cardiac arrest (CA), according to study findings published in Neurology.
Study researchers in Belgium analyzed data from the Neuroprotect Post-CA trial (Clinicaltrials.gov Identifier: NCT02541591). Patients (N=102) who had an out-of-hospital CA and remained unconscious at hospital admission underwent an MRI 4 to 6 days after their CA and, at day 5, a neurologic examination. At day 180, patients were assessed for recovery by the Cerebral Performance Category scale.
A total of 79 patients underwent an MRI and 58 had available clinical data. A neurologist blinded to MRI data predicted a poor prognosis for 23 patients (39.7%). A total of 33 patients (57%) had a poor prognosis and 25 (43%) a good prognosis.
Patients with good and poor prognoses had a median age of 61 (interquartile range [IQR], 52-71) and 66 (IQR, 58-74) years and 64% and 76% were men, respectively. Patients with good prognoses were more likely to present with ventricular fibrillation (P <.01), higher Glasgow Coma Scale (P <.01), higher mean apparent diffusion coefficient (ADC) value (P <.01), and fewer brain voxels with ADC <450×10-6 mm2/s (P <.01).
The prediction of poor neurologic outcome on the basis of the percentage of brain voxels <650×10-6 mm2/s had an area under the receiving operator characteristic curve (AUC) of 0.59 (95% CI, 0.45-0.72). A cutoff of 23.4% corresponded with a 100% specificity and 38.5% sensitivity.
To predict good neurologic outcomes, the percentage of brain voxels <450×10-6 mm2/s had an AUC of 0.67 (95% CI, 0.55-0.79). The optimum cutoff was ADC >931 x10-6 mm2/s with a 100% sensitivity and 38% specificity.
The best predictors for good neurologic recovery were postcentral cortex average ADC (AUC, 0.78; 95% CI, 0.68-0.88) and percent voxels in the temporal cortex with an ADC <450×10-6 mm2/s (AUC, 0.73; 95% CI, 0.61-0.84).
The final predictive model included 3 clinical characteristics and 4 MRI features and had an AUC of 0.96 (95% CI, 0.91-1.00; P =.03) and false positive of 27%. The model without MRI data had an AUC of 0.89 (95% CI, 0.81-0.97; false positive, 39%). The addition of brain phenotypes improved the neurologic recovery prediction among 7% of patients.
This study may have included some selection bias due to the high amount of missing data.
Based on their findings, study researchers concluded that “brain MRI improved the prediction of good neurological recovery…after cardiac arrest and was feasible to perform in a large proportion of patients.”
Wouters A, Scheldeman L, Plessers S, et al. Added value of quantitative apparent diffusion coefficient values for neuroprognostication after cardiac arrest. Neurology. 2021;96(21):e2611-e2618. doi:10.1212/WNL.0000000000011991