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

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.

Prediction of cerebral venous thrombosis with a new clinical score and D-dimer levels.

OBJECTIVE: To investigate prediction of cerebral venous thrombosis (CVT) by clinical variables and D-dimer levels. METHODS: This prospective multicenter study included consecutive patients with clinically possible CVT. On admission, patients underwent clinical examination, blood sampling for D-dimers measuring (ELISA test), and magnetic resonance/CT venography. Predictive value of clinical variables and D-dimers for CVT was calculated. A clinical score to stratify patients into groups with low, moderate, or high CVT risk was established with multivariate logistic regression. RESULTS: CVT was confirmed in 26.2% (94 of 359) of patients by neuroimaging. The optimal estimate of clinical probability was based on 6 variables: seizure(s) at presentation (4 points), known thrombophilia (4 points), oral contraception (2 points), duration of symptoms >6 days (2 points), worst headache ever (1 point), and focal neurologic deficit at presentation (1 point) (area under the curve [AUC] 0.889). We defined 0 to 2 points as low CVT probability (negative predictive value [NPV] 94.1%). Of the 186 (51.8%) patients who had a low probability score, 11 (5.9%) had CVT. The frequency of CVT was 28.3% (34 of 120) in patients with a moderate (3-5 points) and 92.5% (49 of 53) in patients with a high (6-12 points) probability score. All low CVT probability patients with CVT had D-dimers >500 µg/L. Predictive value of D-dimers for CVT for >675 µg/L (best cutoff) vs >500 µg/L was as follows: sensitivity 77.7%, specificity, 77%, NPV 90.7%, and accuracy 77.2% vs sensitivity 89.4%, specificity 66.4%, NPV 94.6%, and accuracy 72.4%, respectively. Adding the clinical score to D-dimers >500 µg/L resulted in the best CVT prediction score explored (at the cutoff ≥6 points: sensitivity 83%/specificity 86.8%/NPV 93.5%/accuracy 84.4%/AUC 0.937). CONCLUSION: The proposed new clinical score in combination with D-dimers may be helpful for predicting CVT as a pretest score; none of the patients with CVT showed low clinical probability for CVT and D-dimers <500 µg/L. CLINICALTRIALSGOV IDENTIFIER: NCT00924859.

Electromyographic reactivity measured with scalp-EEG contributes to prognostication after cardiac arrest.

AIM: To assess whether stimulus-induced modifications of electromyographic activity observed on scalp EEG have a prognostic value in comatose patients after cardiac arrest. METHODS: 184 adult patients from a multi-centric prospective register who underwent an early EEG after cardiac arrest were included. Auditory and somatosensory stimulation was performed during EEG-recording. EEG reactivity (EEG-R) and EMG reactivity (EMG-R) were retrospectively assessed visually by board-certified electroencephalographers, and compared with clinical outcome (cerebral performance category, CPC) at three months. A favorable functional outcome was defined as CPC 1-2, an unfavorable outcome as CPC 3-5. RESULTS: Both EEG-R and EMG-R were predictors for good outcome (EEG-R accuracy 72% (95%-CI 66-79), sensitivity 86% (78-93), specificity 60% (50-69); EMG-R accuracy 65% (58-72), sensitivity 61% (51-75), specificity 69% (60-78)). When reactivity was defined as EEG-R and/or EMG-R, the accuracy was 73% (67-70), the sensitivity 94% (90-99), and the specificity 53% (43-63). CONCLUSION: Taking EMG into account when assessing reactivity of EEG seems to reduce false negative predictions for identifying patients with favorable outcome after cardiac arrest.