Early identification of potential recovery of postanoxic coma is a major challenge. We studied the additional predictive value of EEG.
Two hundred seventy-seven consecutive comatose patients after ...cardiac arrest were included in a prospective cohort study on 2 intensive care units. Continuous EEG was measured during the first 3 days. EEGs were classified as unfavorable (isoelectric, low-voltage, burst-suppression with identical bursts), intermediate, or favorable (continuous patterns), at 12, 24, 48, and 72 hours. Outcome was dichotomized as good or poor. Resuscitation, demographic, clinical, somatosensory evoked potential, and EEG measures were related to outcome at 6 months using logistic regression analysis. Analyses of diagnostic accuracy included receiver operating characteristics and calculation of predictive values.
Poor outcome occurred in 149 patients (54%). Single measures unequivocally predicting poor outcome were an unfavorable EEG pattern at 24 hours, absent pupillary light responses at 48 hours, and absent somatosensory evoked potentials at 72 hours. Together, these had a specificity of 100% and a sensitivity of 50%. For the remaining 203 patients, who were still in the "gray zone" at 72 hours, a predictive model including unfavorable EEG patterns at 12 hours, absent or extensor motor response to pain at 72 hours, and higher age had an area under the curve of 0.90 (95% confidence interval 0.84-0.96). Favorable EEG patterns at 12 hours were strongly associated with good outcome. EEG beyond 24 hours had no additional predictive value.
EEG within 24 hours is a robust contributor to prediction of poor or good outcome of comatose patients after cardiac arrest.
Highlights ► Quantitative electroencephalographic (QEEG) abnormality indices sensitive to power of slow relative to faster activity, or to interhemispheric asymmetry, can uniquely inform clinical ...prognoses and management during (sub-)acute ischaemic stroke. ► Continuous bedside monitoring of these QEEG indices during thrombolytic therapy may instantaneously inform clinicians about the efficacy of same and thereby inform decisions about “bridging” protocols involving intra-arterial interventions. ► Current clinical EEG systems compute and display various QEEG indices, facilitating non-expert EEG interpretation. Hence wider utilisation of such technology appears warranted and would address a key stroke management objective proposed by numerous stroke opinion leaders.
Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model ...combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI.
Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1-2) or good (GOSE 3-8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor.
Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively.
Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.
To evaluate the value of continuous electroencephalography in early prognostication in patients treated with hypothermia after cardiac arrest.
Prospective cohort study.
Medical intensive care unit.
...Sixty patients admitted to the intensive care unit for therapeutic hypothermia after cardiac arrest.
None.
In all patients, continuous electroencephalogram and daily somatosensory evoked potentials were recorded during the first 5 days of admission or until intensive care unit discharge. Neurological outcomes were based on each patient's best achieved Cerebral Performance Category score within 6 months. Twenty-seven of 56 patients (48%) achieved good neurological outcome (Cerebral Performance Category score 1-2).At 12 hrs after resuscitation, 43% of the patients with good neurological outcome showed continuous, diffuse slow electroencephalogram rhythms, whereas this was never observed in patients with poor outcome.The sensitivity for predicting poor neurological outcome of low-voltage and isoelectric electroencephalogram patterns 24 hrs after resuscitation was 40% (95% confidence interval 19%-64%) with a 100% specificity (confidence interval 86%-100%), whereas the sensitivity and specificity of absent somatosensory evoked potential responses during the first 24 hrs were 24% (confidence interval 10%-44%) and 100% (confidence interval: 87%-100%), respectively. The negative predictive value for poor outcome of low-voltage and isoelectric electroencephalogram patterns was 68% (confidence interval 50%-81%) compared to 55% (confidence interval 40%-60%) for bilateral somatosensory evoked potential absence, both with a positive predictive value of 100% (confidence interval 63%-100% and 59%-100% respectively). Burst-suppression patterns after 24 hrs were also associated with poor neurological outcome, but not inevitably so.
In patients treated with hypothermia, electroencephalogram monitoring during the first 24 hrs after resuscitation can contribute to the prediction of both good and poor neurological outcome. Continuous patterns within 12 hrs predicted good outcome. Isoelectric or low-voltage electroencephalograms after 24 hrs predicted poor outcome with a sensitivity almost two times larger than bilateral absent somatosensory evoked potential responses.
We have excellent skills to extract sex from visual assessment of human faces, but assessing sex from human brain rhythms seems impossible. Using deep convolutional neural networks, with unique ...potential to find subtle differences in apparent similar patterns, we explore if brain rhythms from either sex contain sex specific information. Here we show, in a ground truth scenario, that a deep neural net can predict sex from scalp electroencephalograms with an accuracy of >80% (p < 10
), revealing that brain rhythms are sex specific. Further, we extracted sex-specific features from the deep net filter layers, showing that fast beta activity (20-25 Hz) and its spatial distribution is a main distinctive attribute. This demonstrates the ability of deep nets to detect features in spatiotemporal data unnoticed by visual assessment, and to assist in knowledge discovery. We anticipate that this approach may also be successfully applied to other specialties where spatiotemporal data is abundant, including neurology, cardiology and neuropsychology.
Summary
Objective
Electrographic status epilepticus is observed in 10–35% of patients with postanoxic encephalopathy. It remains unclear which electrographic seizure patterns indicate possible ...recovery, and which are a mere reflection of severe ischemic encephalopathy, where treatment would be futile. We aimed to identify quantitative electroencephalography (EEG) features with prognostic significance.
Methods
From continuous EEG recordings of 47 patients with generalized electrographic status epilepticus after cardiac arrest, 5‐min epochs were selected every hour. Epochs were visually assessed and categorized into seven categories, including epileptiform discharges. Five quantitative measures were extracted, reflecting background continuity, discharge frequency, discharge periodicity, relative discharge power, and interdischarge waveform correlation. The best achieved outcome within 6 months after cardiac arrest was categorized as “good” (Cerebral Performance Category 1–2, i.e., no or moderate neurologic disability) or “poor” (CPC 3–5, i.e., severe disability, coma, or death).
Results
Ten patients (22%) had a good outcome. Status epilepticus in patients with good outcome started later (45 vs. 29 h after cardiac arrest, p < 0.001), more often ceased for at least 12 h (90% vs. 16%, p = 0.02), and was less often treated with antiepileptic drugs (30% vs. 73%, p = 0.02). Status epilepticus in patients with a good outcome always evolved from a continuous background pattern, as opposed to evolution from a discontinuous background pattern in 14 patients (38%) with a poor outcome. Epileptiform patterns of patients with good outcome had higher background continuity (1.00 vs. 0.83, p < 0.001), higher discharge frequency (1.63 vs. 0.90 Hz, p = 0.002), lower relative discharge power (0.29 vs. 0.40, p = 0.01), and lower discharge periodicity (0.32 vs. 0.45, p = 0.04).
Significance
Our results can be used to identify patients with possible recovery. We speculate that quantitative features associated with poor outcome reflect low neural network complexity, resulting from extensive ischemic damage.
Objective
To provide evidence that early electroencephalography (EEG) allows for reliable prediction of poor or good outcome after cardiac arrest.
Methods
In a 5‐center prospective cohort study, we ...included consecutive, comatose survivors of cardiac arrest. Continuous EEG recordings were started as soon as possible and continued up to 5 days. Five‐minute EEG epochs were assessed by 2 reviewers, independently, at 8 predefined time points from 6 hours to 5 days after cardiac arrest, blinded for patients’ actual condition, treatment, and outcome. EEG patterns were categorized as generalized suppression (<10 μV), synchronous patterns with ≥50% suppression, continuous, or other. Outcome at 6 months was categorized as good (Cerebral Performance Category CPC = 1–2) or poor (CPC = 3–5).
Results
We included 850 patients, of whom 46% had a good outcome. Generalized suppression and synchronous patterns with ≥50% suppression predicted poor outcome without false positives at ≥6 hours after cardiac arrest. Their summed sensitivity was 0.47 (95% confidence interval CI = 0.42–0.51) at 12 hours and 0.30 (95% CI = 0.26–0.33) at 24 hours after cardiac arrest, with specificity of 1.00 (95% CI = 0.99–1.00) at both time points. At 36 hours or later, sensitivity for poor outcome was ≤0.22. Continuous EEG patterns at 12 hours predicted good outcome, with sensitivity of 0.50 (95% CI = 0.46–0.55) and specificity of 0.91 (95% CI = 0.88–0.93); at 24 hours or later, specificity for the prediction of good outcome was <0.90.
Interpretation
EEG allows for reliable prediction of poor outcome after cardiac arrest, with maximum sensitivity in the first 24 hours. Continuous EEG patterns at 12 hours after cardiac arrest are associated with good recovery. ANN NEUROL 2019;86:203–214
In the human brain, ≈30% of the energy is spent on synaptic transmission. Disappearance of synaptic activity is the earliest consequence of cerebral ischemia. The changes of synaptic function are ...generally assumed to be reversible and persistent damage is associated with membrane failure and neuronal death. However, there is overwhelming experimental evidence of isolated, but persistent, synaptic failure resulting from mild or moderate cerebral ischemia. Early failure results from presynaptic damage with impaired transmitter release. Proposed mechanisms include dysfunction of adenosine triphosphate-dependent calcium channels and a disturbed docking of glutamate-containing vesicles resulting from impaired phosphorylation. We review energy distribution among neuronal functions, focusing on energy usage of synaptic transmission. We summarize the effect of ischemia on neurotransmission and the evidence of long-lasting synaptic failure as a cause of persistent symptoms in patients with cerebral ischemia. Finally, we discuss the implications of synaptic failure in the diagnosis of cerebral ischemia, including the limited sensitivity of diffusion-weighted MRI in those cases in which damage is presumably limited to the synapses.
Mechanical ventilation causes cyclic changes in the heart's preload and afterload, thereby influencing the circulation. However, our understanding of the exact physiology of this cardiopulmonary ...interaction is limited. We aimed to thoroughly determine airway pressure distribution, how this is influenced by tidal volume and chest compliance, and its interaction with the circulation in humans during mechanical ventilation.
Intervention study.
ICU of a university hospital.
Twenty mechanically ventilated patients following coronary artery bypass grafting surgery.
Patients were monitored during controlled mechanical ventilation at tidal volumes of 4, 6, 8, and 10 mL/kg with normal and decreased chest compliance (by elastic binding of the thorax).
Central venous pressure, airway pressure, pericardial pressure, and pleural pressure; pulse pressure variations, systolic pressure variations, and stroke volume variations; and cardiac output were obtained during controlled mechanical ventilation at tidal volume of 4, 6, 8, and 10 mL/kg with normal and decreased chest compliance. With increasing tidal volume (4, 6, 8, and 10 mL/kg), the change in intrathoracic pressures increased linearly with 0.9 ± 0.2, 0.5 ± 0.3, 0.3 ± 0.1, and 0.3 ± 0.1 mm Hg/mL/kg for airway pressure, pleural pressure, pericardial pressure, and central venous pressure, respectively. At 8 mL/kg, a decrease in chest compliance (from 0.12 ± 0.07 to 0.09 ± 0.03 L/cm H2O) resulted in an increase in change in airway pressure, change in pleural pressure, change in pericardial pressure, and change in central venous pressure of 1.1 ± 0.7, 1.1 ± 0.8, 0.7 ± 0.4, and 0.8 ± 0.4 mm Hg, respectively. Furthermore, increased tidal volume and decreased chest compliance decreased stroke volume and increased arterial pressure variations. Transmural pressure of the superior vena cava decreased during inspiration, whereas the transmural pressure of the right atrium did not change.
Increased tidal volume and decreased chest wall compliance both increase the change in intrathoracic pressures and the value of the dynamic indices during mechanical ventilation. Additionally, the transmural pressure of the vena cava is decreased, whereas the transmural pressure of the right atrium is not changed.
The postictal state — What do we know? Pottkämper, Julia C. M.; Hofmeijer, Jeannette; van Waarde, Jeroen A. ...
Epilepsia,
June 2020, Volume:
61, Issue:
6
Journal Article
Peer reviewed
Open access
This narrative review provides a broad and comprehensive overview of the most important discoveries on the postictal state over the past decades as well as recent developments. After a description ...and definition of the postictal state, we discuss postictal sypmtoms, their clinical manifestations, and related findings. Moreover, pathophysiological advances are reviewed, followed by current treatment options.
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