Consciousness Indexing and Outcome Prediction with Resting-State EEG in Severe Disorders of Consciousness.

We applied the following methods to resting-state EEG data from patients with disorders of consciousness (DOC) for consciousness indexing and outcome prediction: microstates, entropy (i.e. approximate, permutation), power in alpha and delta frequency bands, and connectivity (i.e. weighted symbolic mutual information, symbolic transfer entropy, complex network analysis). Patients with unresponsive wakefulness syndrome (UWS) and patients in a minimally conscious state (MCS) were classified into these two categories by fitting and testing a generalised linear model. We aimed subsequently to develop an automated system for outcome prediction in severe DOC by selecting an optimal subset of features using sequential floating forward selection (SFFS). The two outcome categories were defined as UWS or dead, and MCS or emerged from MCS. Percentage of time spent in microstate D in the alpha frequency band performed best at distinguishing MCS from UWS patients. The average clustering coefficient obtained from thresholding beta coherence performed best at predicting outcome. The optimal subset of features selected with SFFS consisted of the frequency of microstate A in the 2-20 Hz frequency band, path length obtained from thresholding alpha coherence, and average path length obtained from thresholding alpha coherence. Combining these features seemed to afford high prediction power. Python and MATLAB toolboxes for the above calculations are freely available under the GNU public license for non-commercial use ( https://qeeg.wordpress.com ).

Hypoxia and Outcome Prediction in Early-Stage Coma (Project HOPE): an observational prospective cohort study.

BACKGROUND: The number of resuscitated cardiac arrest patients suffering from anoxic-ischemic encephalopathy is considerable. However, outcome prediction parameters such as somatosensory evoked potentials need revision because they are based on data predating the implementation of mild therapeutical hypothermia and because data from our own laboratory suggest that they may fail to predict prognosis accurately. The present research project "Hypoxia and Outcome Prediction in Early-Stage Coma" is an ongoing observational prospective cohort study that aims to improve outcome prediction in anoxic coma by limiting the effects of falsely pessimistic predictions at the intensive care unit. METHODS: Our outcome analysis is based on functional and behavioural definitions. This implies the analysis of the positive predictive value of prognostic markers yielding either positive or negative results. We also analyse the effect of covariates adjusted for age and sex such as sociodemographic variables, prognostic variables and treatment factors on functional and behavioural outcomes, with mixed effects regression models (i.e. fixed and random effects). We expect to enrol 172 patients based on the result of previous research. The null hypothesis is that there is a probability of <10 % that a positive outcome will be observed despite the presence of any of the predictors of a poor/negative outcome. We test the null hypothesis against a one-sided alternative using a Simon's two-stage design to determine whether it is warranted to recruit the full number of patients suggested by a power analysis. The second stage has a design with a Type I error rate of 0.05 and 80 % power if the true response rate is 25 %. DISCUSSION: We aim to make a significant contribution to the revision and improvement of current outcome prediction methods in anoxic-ischemic encephalopathy patients. As a result, neurocritical care specialists worldwide will have considerably more accurate methods for prognosticating the outcome of anoxic-ischemic encephalopathy following cardiac arrest. This will facilitate the provision of treatment tailored to individual patients and the attainment of an optimal quality of life. It will also inform the decision to withdraw treatment with a level of accuracy never seen before in the field. TRIAL REGISTRATION: ClinicalTrials.gov NCT02231060 (registered 29 August 2014).

Nonverbal memory tasks in early differential diagnosis of Alzheimer's disease and unipolar depression.

We examined whether there are selective deficits in early-stage Alzheimer's disease (AD; n = 27) and in unipolar depression (UD; n = 17) patients on recall and recognition of spatial and visual components of nonverbal memory (NVM) and whether the two groups can be differentiated based on their performance on such tasks. We also investigated which NVM measures had the best discrimination power. We tested spatial, visuospatial, and visuoconstructive abilities in AD and UD patients. AD patients' scores on NVM tasks were significantly lower than those of healthy subjects (HS; n = 30) and consistently lower than those of the UD group. Z-scores suggested that AD patients suffered from a generalized impairment. Clear differences between AD and UD patients were found on abstract design tasks.

Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness.

BACKGROUND: The continuation of life-sustaining therapy in critical care patients with anoxic-ischemic disorders of consciousness (AI-DOC) depends on prognostic tests such as serum neuron-specific enolase (NSE) concentration levels. OBJECTIVES: To apply predictive models using machine learning methods to examine, one year after onset, the prognostic power of serial measurements of NSE in patients with AI-DOC. To compare the discriminative accuracy of this method to both standard single-day, absolute, and difference-between-days, relative NSE levels. METHODS: Classification algorithms were implemented and K-nearest neighbours (KNN) imputation was used to avoid complete case elimination of patients with missing NSE values. Non-imputed measurements from Day 0 to Day 6 were used for single day and difference-between-days. RESULTS: The naive Bayes classifier on imputed serial NSE measurements returned an AUC of (0.81±0.07) for n=126 patients (100 poor outcome). This was greater than logistic regression (0.73±0.08) and all other classifiers. Naive Bayes gave a specificity and sensitivity of 96% and 49%, respectively, for an (uncalibrated) probability decision threshold of 90%. The maximum AUC for a single day was Day 3 (0.75) for a subset of n=79 (61 poor outcome) patients, and for differences between Day 1 and Day 4 (0.81) for a subset of n=46 (39 poor outcome) patients. CONCLUSION: Imputation avoided the elimination of patients with missing data and naive Bayes outperformed all other classifiers. Machine learning algorithms could detect automatically discriminatory features and the overall predictive power increased from standard methods due to the larger data set. CODE AVAILABILITY: Data analysis code is available under GNU at: https://github.com/emilymuller1991/outcome_prediction_nse.

Severe disorders of consciousness after acquired brain injury: A single-centre long-term follow-up study.

OBJECTIVES: To assess long-term clinical outcome, functional independence and health-related quality of life (HRQOL) in acquired brain injury (ABI) patients with a disorder of consciousness at admission to inpatient rehabilitation. METHODS: We selected patients from a cohort of ABI patients from a single centre. In addition to mortality, we measured level of consciousness with the Coma Remission Scale, functional independence with the Barthel Index, as well as generic and condition-specific HRQOL with the EQ5D and the "Quality of Life after Brain Injury" (QOLIBRI) respectively. RESULTS: Half of the obtained sample had died by follow-up. Survivors were younger at onset, in a minimally conscious state (MCS) at admission and had spent longer time in rehabilitation. Patients in a MCS were more likely to survive, and be in a state better than MCS over the follow-up time than patients with an unresponsive wakefulness syndrome (UWS). A small proportion of patients with UWS at admission emerged from MCS at follow-up. Emergence from MCS was associated with traumatic brain injury (TBI) and higher functional independence. CONCLUSION: Clinical outcome is mostly concordant with previous findings. Survivors' rehabilitation duration suggest revision of current standards. HRQOL results indicate a correlation with functional independence and that condition-specific HRQOL should not be neglected.

Simultaneous EEG-PET-fMRI measurements in disorders of consciousness: an exploratory study on diagnosis and prognosis.

Previous studies could demonstrate that functional magnetic resonance imaging (fMRI), fludeoxyglucose positron emission tomography (FDG-PET), and electroencephalography (EEG) measures contain information about patients suffering from disorders of consciousness (DOC) and thus improve the clinical diagnosis. Additionally, the technical modalities were able to predict the outcome of patients. However, most studies lack proven reproducibility in a clinical setting. We here applied a standardized combined EEG/fMRI/FDG-PET measurement to a cohort of 20 patients suffering from DOC and focused on parameters that have been demonstrated to contain information about diagnosis and prognosis of these patients. We evaluated EEG band power, fMRI connectivity in networks associated with consciousness and sensory networks, as well as absolute glucose uptake in the brain as potential markers of preserved consciousness or favorable outcome. Acquired data were analyzed by a principal component analysis to identify the most important markers in a hypothesis-free manner. These were then analyzed with statistical group comparisons. Absolute FDG-PET could prove that glucose metabolism in the occipital lobe is significantly higher in minimally conscious than in vegetative state patients. Delta band power showed to be prognostic marker for a favorable outcome. We conclude that absolute FDG-PET is a suitable tool to evaluate the level consciousness in DOC patients. Additionally, we propose delta band power as marker of a favorable outcome in DOC patients. We suggest that these findings promote a standardized technical evaluation of DOC patients to improve diagnosis and prognosis.

Stability of auditory event-related potentials in coma research.

Patients with unresponsive wakefulness syndrome (UWS) or in minimally conscious state (MCS) after brain injury show significant fluctuations in their behavioural abilities over time. As the importance of event-related potentials (ERPs) in the detection of traces of consciousness increases, we investigated the retest reliability of ERPs with repeated tests at four different time points. Twelve healthy controls and 12 inpatients (8 UWS, 4 MCS; 6 traumatic, 6 non-traumatic) were tested twice a day (morning, afternoon) for 2 days with an auditory oddball task. ERPs were recorded with a 256-channel-EEG system, and correlated with behavioural test scores in the Coma Recovery Scale-revised (CRS-R). The number of identifiable P300 responses varied between zero and four in both groups. Reliabilities varied between Krippendorff's α = 0.43 for within-day comparison, and α = 0.25 for between-day comparison in the patient group. Retest reliability was strong for the CRS-R scores for all comparisons (α = 0.83-0.95). The stability of auditory information processing in patients with disorders of consciousness is the basis for other, even more demanding tasks and cognitive potentials. The relatively low ERP-retest reliability suggests that it is necessary to perform repeated tests, especially when probing for consciousness with ERPs. A single negative ERP test result may be mistaken for proof that a UWS patient truly is unresponsive.