Neurophysiological and neuroradiological multimodal approach for early poor outcome prediction after cardiac arrest

Abstract

Introduction

Prognosticating outcome after cardiac arrest(CA) requires a multimodal approach. However, evidence regarding combinations of methods is limited. We evaluated whether the combination of electroencephalography(EEG), somatosensory evoked potentials(SEPs) and brain computed tomography(CT) could predict poor outcome.

Methods

We screened our database regarding patients successfully resuscitated after CA, for whom EEG, SEPs and brain CT were available within 24 h. EEG patterns were classified according to American Clinical Neurophysiological Society terminology; SEPs were graded accounting for the cortical responses of each hemisphere; and the grey matter/white matter(GM/WM) ratio was evaluated by brain CT. EEG patterns, SEP findings and GM/WM ratio (with a specificity of 100%) were, individually and in combination, related to poor outcome (death/unresponsive wakefulness state) at 6-month follow-up, using the cerebral performance categories(CPC).

Results

EEG, SEPs and brain CT were available in 183/273(67%) patients. Bilateral absent/absent-pathologic(AA/AP) cortical SEPs predicted a poor outcome with a sensitivity of 58.5%. A GM/WM ratio <1.21 predicted a poor outcome with a sensitivity of 50.4%. Isoelectric/burst-suppression EEG patterns predicted a poor outcome with a sensitivity of 43%. If at least one of these poor prognostic patterns was present, sensitivity for an ominous outcome increased to 71.5%. If, in the same subject, two poor prognostic patterns were simultaneously present, sensitivity was 48%. If all three poor prognostic patterns were present, sensitivity decreased by up to 23%.

Conclusion

In this population, in which life-sustaining treatments were never suspended, the combination of EEG, SEPs and brain CT improved the sensitivity, maintaining the specificity of poor outcome prediction.

Introduction

Assessment of the neurologic prognosis of patients surviving cardiac arrest(CA) has grown in importance, and many studies have been published regarding both poor and good long-term functional outcome prediction [1]. Prognosticating outcome after CA is challenging and requires a multimodal approach [2], [3], [4], [5], [6], [7], [8]. Thus, authors have abandoned the previous unimodal approach [9], moving to recent guidelines [8] based on a multimodal prognostication algorithm. In these guidelines, authors suggested that ocular reflexes and somatosensory evoked potentials(SEPs) should be used first, followed by a combination of other predictors: status myoclonus, electroencephalography(EEG), neuron-specific enolase(NSE) and neuroimaging. Despite these recommendations, only a single parameter at a time has been evaluated for neurological prognosis in most of the studies. Only some authors adopted a multimodal strategy [10], [11], [12], [13], [14], [15], [16], [17], with limitations concerning the different aggregation of cerebral performance categories(CPC) scores, for outcome prognostication, and the prognostic tools used. In fact, all these papers analysed different combinations of the suggested clinical and instrumental parameters, and they did not apply a multimodal algorithm. So, given these limitations, we used a different multimodal approach for the prediction of poor long-term functional outcome of comatose CA patients, analysing the grey matter/white matter(GM/WM) ratio by brain computed tomography(CT), SEPs and EEG, all performed in the same patient and within the first 24 h after CA. We evaluated whether the availability of multiple tests on the same patient could better establish poor long-term outcome prediction with higher sensitivity, compared with that of each test taken individually, and whether the simultaneous presence of more than one parameter predicting poor outcome could improve the degree of certainty in a single patient.