what is the role of post acute eeg in prediction of late neurological outcome in severe disorders of consciousness

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What is new about somatosensory evoked potentials as neurological predictors of comatose survivors after cardiac arrest

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Brain death following ingestion of E‐cigarette liquid nicotine refill solution

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Does a combination of ≥2 abnormal tests vs. the ERC-ESICM stepwise algorithm improve prediction of poor neurological outcome after cardiac arrest? A post-hoc analysis of the ProNeCA multicentre study.

BACKGROUND Bilaterally absent pupillary light reflexes (PLR) or N20 waves of short-latency evoked potentials (SSEPs) are recommended by the 2015 ERC-ESICM guidelines as robust, first-line predictors of poor neurological outcome after cardiac arrest. However, recent evidence shows that the false positive rates (FPRs) of these tests may be higher than previously reported. We investigated if testing accuracy is improved when combining PLR/SSEPs with malignant electroencephalogram (EEG), oedema on brain computed tomography (CT), or early status myoclonus (SM). METHODS Post-hoc analysis of ProNeCA multicentre prognostication study. We compared the prognostic accuracy of the ERC-ESICM prognostication strategy vs. that of a new strategy combining ≥2 abnormal results from any of PLR, SSEPs, EEG, CT and SM. We also investigated if using alternative classifications for abnormal SSEPs (absent-pathological vs. bilaterally-absent N20) or malignant EEG (ACNS-defined suppression or burst-suppression vs. unreactive burst-suppression or status epilepticus) improved test sensitivity. RESULTS We assessed 210 adult comatose resuscitated patients of whom 164 (78%) had poor neurological outcome (CPC 3-5) at six months. FPRs and sensitivities of the ≥2 abnormal test strategy vs. the ERC-ESICM algorithm were 0[0-8]% vs. 7 [1-18]% and 49[41-57]% vs. 63[56-71]%, respectively (p < .0001). Using alternative SSEP/EEG definitions increased the number of patients with ≥2 concordant test results and the sensitivity of both strategies (67[59-74]% and 54[46-61]% respectively), with no loss of specificity. CONCLUSIONS In comatose resuscitated patients, a prognostication strategy combining ≥2 among PLR, SSEPs, EEG, CT and SM was more specific than the 2015 ERC-ESICM prognostication algorithm for predicting 6-month poor neurological outcome

Degradation of EEG microstates patterns in subjective cognitive decline and mild cognitive impairment: Early biomarkers along the Alzheimer's Disease continuum?

Alzheimer's disease (AD) pathological changes may begin up to decades earlier than the appearance of the first symptoms of cognitive decline. Subjective cognitive decline (SCD) could be the first pre-clinical sign of possible AD, which might be followed by mild cognitive impairment (MCI), the initial stage of clinical cognitive decline. However, the neural correlates of these prodromic stages are not completely clear yet. Recent studies suggest that EEG analysis tools characterizing the cortical activity as a whole, such as microstates and cortical regions connectivity, might support a characterization of SCD and MCI conditions. Here we test this approach by performing a broad set of analyses to identify the prominent EEG markers differentiating SCD (n&nbsp;=&nbsp;57), MCI (n&nbsp;=&nbsp;46) and healthy control subjects (HC, n&nbsp;=&nbsp;19). We found that the salient differences were in the temporal structure of the microstates patterns, with MCI being associated with less complex sequences due to the altered transition probability, frequency and duration of canonic microstate C. Spectral content of EEG, network connectivity, and spatial arrangement of microstates were instead largely similar in the three groups. Interestingly, comparing properties of EEG microstates in different cerebrospinal fluid (CSF) biomarkers profiles, we found that canonic microstate C displayed significant differences in topography in AD-like profile. These results show that the progression of dementia might be associated with a degradation of the cortical organization captured by microstates analysis, and that this leads to altered transitions between cortical states. Overall, our approach paves the way for the use of non-invasive EEG recordings in the identification of possible biomarkers of progression to AD from its prodromal states

Merging Clinical and EEG Biomarkers in an Elastic-Net Regression for Disorder of Consciousness Prognosis Prediction

Patients with Disorder of Consciousness (DoC) entering Intensive Rehabilitation Units after a severe Acquired Brain Injury have a highly variable evolution of the state of consciousness which is a complex aspect to predict. Besides clinical factors, electroencephalography has clearly shown its potential into the identification of prognostic biomarkers of consciousness recovery. In this retrospective study, with a dataset of 271 patients with DoC, we proposed three different Elastic-Net regressors trained on different datasets to predict the Coma Recovery Scale-Revised value at discharge based on data collected at admission. One dataset was completely EEG-based, one solely clinical data-based and the last was composed by the union of the two. Each model was optimized, validated and tested with a robust nested cross-validation pipeline. The best models resulted in a median absolute test error of 4.54 [IQR = 4.56], 3.39 [IQR = 4.36], 3.16 [IQR = 4.13] for respectively the EEG, clinical and hybrid model. Furthermore, the hybrid model for what concerns overcoming an unresponsive wakefulness state and exiting a DoC results in an AUC of 0.91 and 0.88 respectively. Small but useful improvements are added by the EEG dataset to the clinical model for what concerns overcoming an unresponsive wakefulness state. Data-driven techniques and namely, machine learning models are hereby shown to be capable of supporting the complex decision-making process the practitioners must face