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

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.

Continuous Versus Routine Standardized Electroencephalogram for Outcome Prediction in Critically Ill Adults: Analysis From a Randomized Trial

“…The prognostic role of EEG background confirms previous reports, mostly after CA or TBI (1,6), but our data add information in other causes of DoC. This is in line with a recent study from our group (investigating the same cohort) who found that EEG could predict survival in all etiology groups (7).…”

EEG Assessment in Patients With Disorders of Consciousness: Aims, Advantages, Limits, and Pitfalls

“…These represent the first attempts to standardize EEG evaluation in this field; however, they are subject to limitations in the definition of intermediate patterns. Although both the American Clinical Neurophysiology Society ( 7 ) and, more recently, the European Academy of Neurology ( 10 ) have proposed guidelines and recommendations that have been successfully applied to long-lasting EEG recordings in selected populations ( 11 ), an extensive application of this rule in different clinical contexts is still lacking. This limits useful applications in clinical settings and reduces the homogeneity of the classification criteria necessary for scientific production.…”

Quantitative EEG parameters can improve the predictive value of the non-traumatic neurological ICU patient prognosis through the machine learning method

“…We also need QEEG parameters for comparing severity among different illnesses. Müller et al ( 34 ) found that visual EEG can predict the prognosis of coma patients with different causes. Third, HIE accounts for a small proportion in both training and validation sets.…”