The aim was to determine the prevalence and risk factors for electrographic seizures and other electroencephalographic (EEG) patterns in patients with Coronavirus disease 2019 (COVID-19) undergoing clinically indicated continuous electroencephalogram (cEEG) monitoring and to assess whether EEG findings are associated with outcomes. Methods: We identified 197 patients with COVID-19 referred for cEEG at 9 participating centers. Medical records and EEG reports were reviewed retrospectively to determine the incidence of and clinical risk factors for seizures and other epileptiform patterns. Multivariate Cox proportional hazards analysis assessed the relationship between EEG patterns and clinical outcomes. Results: Electrographic seizures were detected in 19 (9.6%) patients, including nonconvulsive status epilepticus (NCSE) in 11 (5.6%). Epileptiform abnormalities (either ictal or interictal) were present in 96 (48.7%). Preceding clinical seizures during hospitalization were associated with both electrographic seizures (36.4% in those with vs 8.1% in those without prior clinical seizures, odds ratio [OR] 6.51, p = 0.01) and NCSE (27.3% vs 4.3%, OR 8.34, p = 0.01). A pre-existing intracranial lesion on neuroimaging was associated with NCSE (14.3% vs 3.7%; OR 4.33, p = 0.02). In multivariate analysis of outcomes, electrographic seizures were an independent predictor of in-hospital mortality (hazard ratio ], p < 0.01). In competing risks analysis, hospital length of stay increased in the presence of NCSE (30 day proportion discharged with vs without NCSE: HR 0.21 [0.03-0.33] vs 0.43 [0.36-0.49]). Interpretation: This multicenter retrospective cohort study demonstrates that seizures and other epileptiform abnormalities are common in patients with COVID-19 undergoing clinically indicated cEEG and are associated with adverse clinical outcomes.
Background: Alteration of the mechanisms of cerebral blood flow (CBF) regulation might contribute to the pathophysiology of sepsis-associated encephalopathy (SAE). However, previous clinical studies on dynamic cerebral autoregulation (dCA) in sepsis had several cofounders. Furthermore, little is known on the potential impairment of neurovascular coupling (NVC) in sepsis. The aim of our study was to determine the presence and time course of dCA and NVC alterations in a clinically relevant animal model and their potential impact on the development of SAE. Methods: Thirty-six anesthetized, mechanically ventilated female sheep were randomized to sham procedures (sham, n = 15), sepsis (n = 14), or septic shock (n = 7). Blood pressure, CBF, and electrocorticography were continuously recorded. Pearson's correlation coefficient Lxa and transfer function analysis were used to estimate dCA. NVC was assessed by the analysis of CBF variations induced by cortical gamma activity (Eγ) peaks and by the magnitude-squared coherence (MSC) between the spontaneous fluctuations of CBF and Eγ. Cortical function was estimated by the alpha-delta ratio. Wilcoxon signed rank and rank sum tests, Friedman tests, and RMANOVA test were used as appropriate. Results: Sepsis and sham animals did not differ neither in dCA nor in NVC parameters. A significant impairment of dCA occurred only after septic shock (Lxa, p = 0.03, TFA gain p = 0.03, phase p = 0.01). Similarly, NVC was altered during septic shock, as indicated by a lower MSC in the frequency band 0.03-0.06 Hz (p < 0.001). dCA and NVC impairments were associated with cortical dysfunction (reduction in the alpha-delta ratio (p = 0.03)). Conclusions: A progressive loss of dCA and NVC occurs during septic shock and is associated with cortical dysfunction. These findings indicate that the alteration of mechanisms controlling cortical perfusion plays a late role in the pathophysiology of SAE and suggest that alterations of CBF regulation mechanisms in less severe phases of sepsis reported in clinical studies might be due to patients' comorbidities or other confounders. Furthermore, a mean arterial pressure targeting therapy aiming to optimize dCA might not be sufficient to prevent neuronal dysfunction in sepsis since it would not improve NVC.
The increase in neuronal activity induced by a single seizure is supported by a rise in the cerebral blood flow and tissue oxygenation, a mechanism called neurovascular coupling (NVC). Whether cerebral and systemic hemodynamics are able to match neuronal activity during recurring seizures is unclear, as data from rodent models are at odds with human studies. In order to clarify this issue, we used an invasive brain and systemic monitoring to study the effects of chemically induced non-convulsive seizures in sheep. Despite an increase in neuronal activity as seizures repeat (Spearman’s ρ coefficient 0.31, P < 0.001), ictal variations of cerebral blood flow remained stable while it progressively increased in the inter-ictal intervals (ρ = 0.06, P = 0.44 and ρ = 0.22; P = 0.008). We also observed a progressive reduction in the inter-ictal brain tissue oxygenation (ρ = − 0.18; P = 0.04), suggesting that NVC was unable to compensate for the metabolic demand of these closely repeating seizures. At the systemic level, there was a progressive reduction in blood pressure and a progressive rise in cardiac output (ρ = − 0.22; P = 0.01 and ρ = 0.22; P = 0.01, respectively), suggesting seizure-induced autonomic dysfunction.
Background: Electroencephalography (EEG) is widely used in the monitoring of critically ill comatose patients, but its interpretation is not straightforward. The aim of this study was to evaluate whether there is a correlation between EEG background pattern/reactivity to stimuli and automated pupillometry in critically ill patients. Methods: Prospective assessment of pupillary changes to light stimulation was obtained using an automated pupillometry (NeuroLight Algiscan, ID-MED, Marseille, France) in 60 adult patients monitored with continuous EEG. The degree of encephalopathy and EEG reactivity were scored by 3 independent neurophysiologists blinded to the patient's history. The median values of baseline pupil size, pupillary constriction, constriction velocity, and latency were collected for both eyes. To assess sensitivity and specificity, we calculated areas under the receiver-operating characteristic curve. Results: The degree of encephalopathy assessed by EEG was categorized as mild (42%), moderate (37%), severe (10%) or suppression-burst/suppression (12%); a total of 47/60 EEG recordings were classified as "reactive." There was a significant difference in pupillary size, constriction rate, and constriction velocity, but not latency, among the different EEG categories of encephalopathy. Similarly, reactive EEG tracings were associated with greater pupil size, pupillary constriction rate, and constriction velocity compared with nonreactive recordings; there were no significant differences in latency. Pupillary constriction rate values had an area under the curve of 0.83 to predict the presence of severe encephalopathy or suppression-burst/suppression, with a pupillary constriction rate of < 20% having a sensitivity of 85% and a specificity of 79%. Conclusions: Automated pupillometry can contribute to the assessment of cerebral dysfunction in critically ill patients.
Background Sepsis-associated encephalopathy (SAE) is frequent in septic patients. Electroencephalography (EEG) is very sensitive to detect early epileptic abnormalities, such as seizures and periodic discharges (PDs), and to quantify their duration (the so-called burden). However, the prevalence of these EEG abnormalities in septic patients, as well as their effect on morbidity and mortality, are still unclear. The aims of this study were to assess whether the presence of electrographic abnormalities (i.e. the absence of reactivity, the presence and burden of seizures and PDs) was associated with functional outcome and mortality in septic patients and whether these abnormalities were associated with sepsis-associated encephalopathy (SAE). Methods We prospectively included septic patients, without known chronic or acute intracranial disease or pre-existing acute encephalopathy, requiring ICU admission in a tertiary academic centre. Continuous EEG monitoring was started within 72 h after inclusion and performed for up to 7 days. A comprehensive assessment of consciousness and delirium was performed twice daily by a trained neuropsychologist. Primary endpoints were unfavourable functional outcome (UO, defined as a Glasgow Outcome Scale-Extended—GOSE—score < 5), and mortality collected at hospital discharge and secondary endpoint was the association of PDs with SAE. Mann–Whitney, Fisher’s exact and χ2 tests were used to assess differences in variables between groups, as appropriate. Multivariable logistic regression analysis with in-hospital mortality, functional outcome, SAE or PDs as the dependent variables were performed. Results We included 92 patients. No seizures were identified. Nearly 25% of patients had PDs. The presence of PDs and PDs burden was associated with UO in univariate (n = 15 [41%], p = 0.005 and p = 0.008, respectively) and, for PDs presence, also in multivariate analysis after correcting for disease severity (OR 3.82, IC 95% [1.27–11.49], p = 0.02). The PDs burden negatively correlated with GOSE (Spearman’s coefficient ρ = − 0.2, p = 0.047). The presence of PDs was also independently associated with SAE (OR 8.98 [1.11–72.8], p = 0.04). Reactivity was observed in the majority of patients and was associated with outcomes (p = 0.044 for both functional outcome and mortality). Conclusion Our findings suggest that PDs and PDs burden are associated with SAE and might affect outcome in septic patients.
BackgroundElectroencephalography (EEG) is widely used to monitor critically ill patients. However, EEG interpretation requires the presence of an experienced neurophysiologist and is time-consuming. Aim of this study was to evaluate whether parameters derived from an automated pupillometer (AP) might help to assess the degree of cerebral dysfunction in critically ill patients.MethodsProspective study conducted in the Department of Intensive Care of Erasme University Hospital in Brussels, Belgium. Pupillary assessments were performed using the AP in three subgroups of patients, concomitantly monitored with continuous EEG: “anoxic brain injury”, “Non-anoxic brain injury” and “other diseases”. An independent neurologist blinded to patient's history and AP results scored the degree of encephalopathy and reactivity on EEG using a standardized scale. The mean value of Neurologic Pupil Index (NPi), pupillary size, constriction rate, constriction and dilation velocity (CV and DV) and latency for both eyes, obtained using the NPi®-200 (Neuroptics, Laguna Hills, CA, USA), were reported.ResultsWe included 214 patients (mean age 60 years, 55% male). EEG tracings were categorized as: mild (n = 111, 52%), moderate (n = 65, 30%) or severe (n = 16, 8%) encephalopathy; burst-suppression (n = 19, 9%) or suppression background (n = 3, 1%); a total of 38 (18%) EEG were classified as “unreactive”. We found a significant difference in all pupillometry variables among different EEG categories. Moreover, an unreactive EEG was associated with lower NPi, pupil size, pupillary reactivity, CV and DV and a higher latency than reactive recordings. Low DV (Odds ratio 0.020 [95% confidence intervals 0.002–0.163]; p < 0.01) was independently associated with an unreactive EEG, together with the use of analgesic/sedative drugs and high lactate concentrations. In particular, DV values had an area under the curve (AUC) of 0.86 [0.79–0.92; p < 0.01] to predict the presence of unreactive EEG. In subgroups analyses, AUC of DV to predict unreactive EEG was lower (0.72 [0.56–0.87]; p < 0.01) in anoxic brain injury than Non-anoxic brain injury (0.92 [0.85–1.00]; p < 0.01) and other diseases (0.96 [0.90–1.00]; p < 0.01).ConclusionsThis study suggests that low DV measured by the AP might effectively identify an unreactive EEG background, in particular in critically ill patients without anoxic brain injury.
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