Standardized visual EEG features predict outcome in patients with acute consciousness impairment of various etiologies

Critical Care

Andrea O. Rossetti¹  Matthias Haenggi²  Vincent Alvarez³  Werner J. Z’Graggen⁴  Frédéric Zubler⁵  Rebekka Zimmermann⁵  Kaspar Schindler⁵  Michael Müller⁵  Stephan Rüegg⁶ 

[1] Department of Clinical Neuroscience, Lausanne University Hospital (CHUV) and University of Lausanne;Department of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern;Department of Neurology, Hôpital du Valais;Department of Neurology, Inselspital, Bern University Hospital, University of Bern;Department of Neurology, Sleep-Wake-Epilepsy-Center, Inselspital, Bern University Hospital, University of Bern;Department of Neurology, University Hospital Basel;

关键词: Electroencephalography;    Prognostication;    Acute consciousness impairment;    Hypoxic ischemic encephalopathy;    Traumatic brain injury;    Random forest;   

DOI  :  10.1186/s13054-020-03407-2

来源: DOAJ

【 摘 要 】

Abstract Background Early prognostication in patients with acute consciousness impairment is a challenging but essential task. Current prognostic guidelines vary with the underlying etiology. In particular, electroencephalography (EEG) is the most important paraclinical examination tool in patients with hypoxic ischemic encephalopathy (HIE), whereas it is not routinely used for outcome prediction in patients with traumatic brain injury (TBI). Method Data from 364 critically ill patients with acute consciousness impairment (GCS ≤ 11 or FOUR ≤ 12) of various etiologies and without recent signs of seizures from a prospective randomized trial were retrospectively analyzed. Random forest classifiers were trained using 8 visual EEG features—first alone, then in combination with clinical features—to predict survival at 6 months or favorable functional outcome (defined as cerebral performance category 1–2). Results The area under the ROC curve was 0.812 for predicting survival and 0.790 for predicting favorable outcome using EEG features. Adding clinical features did not improve the overall performance of the classifier (for survival: AUC = 0.806, p = 0.926; for favorable outcome: AUC = 0.777, p = 0.844). Survival could be predicted in all etiology groups: the AUC was 0.958 for patients with HIE, 0.955 for patients with TBI and other neurosurgical diagnoses, 0.697 for patients with metabolic, inflammatory or infectious causes for consciousness impairment and 0.695 for patients with stroke. Training the classifier separately on subgroups of patients with a given etiology (and thus using less training data) leads to poorer classification performance. Conclusions While prognostication was best for patients with HIE and TBI, our study demonstrates that similar EEG criteria can be used in patients with various causes of consciousness impairment, and that the size of the training set is more important than homogeneity of ACI etiology.

【 授权许可】

Unknown