Low apparent diffusion coefficient cluster-based analysis of diffusion-weighted MRI for prognostication of out-of-hospital cardiac arrest survivors

Objective: Recent studies suggested quantitative analysis of diffusion-weighted magnetic resonance imaging as a promising tool for early prognostication of cardiac arrest patients. However, most of their methods involve significant manual image handling often subjective and difficult to reproduce. Therefore developing a computerized analysis method using easy-to-define characteristics would be useful.

Methods: Comatose out-of-hospital cardiac arrest (OHCA) patients who underwent brain MRI between January 2008 and July 2012 were identified from an OHCA registry. Apparent diffusion coefficient (ADC) axial images were analyzed using a program to detect and characterize clusters of low ADC pixels from six brain regions including frontal, occipital, parietal, rolandic and temporal and basal ganglia region. Identified clusters were ranked according to size, mean ADC and minimum ADC to assess the regional maximum cluster size (MCS), lowest mean ADC (LMEAN) and lowest minimum ADC (LMIN). Their power to predict poor outcome, defined as 6-month CPC 3 or higher, was assessed by contingency table analyses.

Results: 51 OHCA patients were eligible during the study period. The sensitivities of MCS, LMEAN and LMIN to detect poor outcome varied according to brain region from 62.5 to 90.0%, 50.0 to 72.5% and 42.5 to 82.5% with their specificities set to 100%, respectively. The MCS of occipital region showed most favorable test profile (sensitivity 90%, specificity 100%; AUROC 0.940, 95% confidence interval 0.874-1.000).

Conclusion: The cluster-based computerized image analysis might be a simple but useful method for prediction of poor neurologic outcome. Future studies validating its prognostic performance are required.