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

被引:14
作者
Mueller, Michael [1 ]
Rossetti, Andrea O. [2 ,3 ]
Zimmermann, Rebekka [1 ]
Alvarez, Vincent [4 ]
Rueegg, Stephan [5 ]
Haenggi, Matthias [6 ]
Z'Graggen, Werner J. [7 ,8 ]
Schindler, Kaspar [1 ]
Zubler, Frederic [1 ]
机构
[1] Univ Bern, Univ Hosp Bern, Dept Neurol, Inselspital,Sleep Wake Epilepsy Ctr, Bern, Switzerland
[2] Lausanne Univ Hosp CHUV, Dept Clin Neurosci, Lausanne, Switzerland
[3] Univ Lausanne, Lausanne, Switzerland
[4] Hop Valais, Dept Neurol, Sion, Switzerland
[5] Univ Hosp Basel, Dept Neurol, Basel, Switzerland
[6] Univ Bern, Univ Hosp Bern, Dept Intens Care Med, Inselspital, Bern, Switzerland
[7] Univ Bern, Univ Hosp Bern, Dept Neurol, Inselspital, Bern, Switzerland
[8] Univ Bern, Univ Hosp Bern, Dept Neurosurg, Inselspital, Bern, Switzerland
基金
瑞士国家科学基金会;
关键词
Electroencephalography; Prognostication; Acute consciousness impairment; Hypoxic ischemic encephalopathy; Traumatic brain injury; Random forest; TRAUMATIC BRAIN-INJURY; C-REACTIVE PROTEIN; CARDIAC-ARREST; SUBARACHNOID HEMORRHAGE; COMATOSE PATIENTS; QUANTITATIVE EEG; PROGNOSIS; PROGNOSTICATION;
D O I
10.1186/s13054-020-03407-2
中图分类号
R4 [临床医学];
学科分类号
1002 ; 100602 ;
摘要
BackgroundEarly 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).MethodData 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).ResultsThe 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.ConclusionsWhile 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.
引用
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页数:13
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