Dynamic functional connectivity of the EEG in relation to outcome of postanoxic coma.

OBJECTIVE: Early EEG contains reliable information for outcome prediction of comatose patients after cardiac arrest. We introduce dynamic functional connectivity measures and estimate additional predictive values. METHODS: We performed a prospective multicenter cohort study on continuous EEG for outcome prediction of comatose patients after cardiac arrest. We calculated Link Rates (LR) and Link Durations (LD) in the α, δ, and θ band, based on similarity of instantaneous frequencies in five-minute EEG epochs, hourly, during 3 days after cardiac arrest. We studied associations of LR and LD with good (Cerebral Performance Category (CPC) 1-2) or poor outcome (CPC 3-5) with univariate analyses. With random forest classification, we established EEG-based predictive models. We used receiver operating characteristics to estimate additional values of dynamic connectivity measures for outcome prediction. RESULTS: Of 683 patients, 369 (54%) had poor outcome. Patients with poor outcome had significantly lower LR and longer LD, with largest differences 12 h after cardiac arrest (LRθ 1.87 vs. 1.95 Hz and LDα 91 vs. 82 ms). Adding these measures to a model with classical EEG features increased sensitivity for reliable prediction of poor outcome from 34% to 38% at 12 h after cardiac arrest. CONCLUSION: Poor outcome is associated with lower dynamics of connectivity after cardiac arrest. SIGNIFICANCE: Dynamic functional connectivity analysis may improve EEG based outcome prediction.

Brain imaging in comatose survivors of cardiac arrest: Pathophysiological correlates and prognostic properties.

INTRODUCTION: Hypoxic-ischemic brain injury is the main cause of death and disability of comatose patients after cardiac arrest. Early and reliable prognostication is challenging. Common prognostic tools include clinical neurological examination and electrophysiological measures. Brain imaging is well established for diagnosis of focal cerebral ischemia but has so far not found worldwide application in this patient group. OBJECTIVE: To review the value of Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and Positron Emission Tomography (PET) for early prediction of neurological outcome of comatose survivors of cardiac arrest. METHODS: A literature search was performed to identify publications on CT, MRI or PET in comatose patients after cardiac arrest. RESULTS: We included evidence from 51 articles, 21 on CT, 27 on MRI, 1 on CT and MRI, and 2 on PET imaging. Studies varied regarding timing of measurements, choice of determinants, and cut-off values predicting poor outcome. Most studies were small (n = 6-398) and retrospective (60%). In general, cytotoxic oedema, defined by a grey-white matter ratio <1.10, derived from CT, or MRI-diffusion weighted imaging <650 × 10-6 mm2/s in >10% of the brain could differentiate between patients with favourable and unfavourable outcomes on a group level within 1-3 days after cardiac arrest. Advanced imaging techniques such as functional MRI or diffusion tensor imaging show promising results, but need further evaluation. CONCLUSION: CT derived grey-white matter ratio and MRI based measures of diffusivity and connectivity hold promise to improve outcome prediction after cardiac arrest. Prospective validation studies in a multivariable approach are needed to determine the additional value for the individual patient.