Continuous electroencephalography monitoring for early prediction of neurological outcome in postanoxic patients after cardiac arrest: a prospective cohort study

A Cerebral Recovery Index (CRI) for early prognosis in patients after cardiac arrest

Introduction

Electroencephalogram (EEG) monitoring in patients treated with therapeutic hypothermia after cardiac arrest may assist in early outcome prediction. Quantitative EEG (qEEG) analysis can reduce the time needed to review long-term EEG and makes the analysis more objective. In this study, we evaluated the predictive value of qEEG analysis for neurologic outcome in postanoxic patients.

Methods

In total, 109 patients admitted to the ICU for therapeutic hypothermia after cardiac arrest were included, divided over a training and a test set. Continuous EEG was recorded during the first 5 days or until ICU discharge. Neurologic outcomes were based on the best achieved Cerebral Performance Category (CPC) score within 6 months. Of the training set, 27 of 56 patients (48%) and 26 of 53 patients (49%) of the test set achieved good outcome (CPC 1 to 2). In all patients, a 5 minute epoch was selected each hour, and five qEEG features were extracted. We introduced the Cerebral Recovery Index (CRI), which combines these features into a single number.

Results

At 24 hours after cardiac arrest, a CRI <0.29 was always associated with poor neurologic outcome, with a sensitivity of 0.55 (95% confidence interval (CI): 0.32 to 0.76) at a specificity of 1.00 (CI, 0.86 to 1.00) in the test set. This results in a positive predictive value (PPV) of 1.00 (CI, 0.73 to 1.00) and a negative predictive value (NPV) of 0.71 (CI, 0.53 to 0.85). At the same time, a CRI >0.69 predicted good outcome, with a sensitivity of 0.25 (CI, 0.10 to 0.14) at a specificity of 1.00 (CI, 0.85 to 1.00) in the test set, and a corresponding NPV of 1.00 (CI, 0.54 to 1.00) and a PPV of 0.55 (CI, 0.38 to 0.70).

Conclusions

We introduced a combination of qEEG measures expressed in a single number, the CRI, which can assist in prediction of both poor and good outcomes in postanoxic patients, within 24 hours after cardiac arrest.

Real-time segmentation of burst suppression patterns in critical care EEG monitoring

OBJECTIVE

Develop a real-time algorithm to automatically discriminate suppressions from non-suppressions (bursts) in electroencephalograms of critically ill adult patients.

METHODS

A real-time method for segmenting adult ICU EEG data into bursts and suppressions is presented based on thresholding local voltage variance. Results are validated against manual segmentations by two experienced human electroencephalographers. We compare inter-rater agreement between manual EEG segmentations by experts with inter-rater agreement between human vs automatic segmentations, and investigate the robustness of segmentation quality to variations in algorithm parameter settings. We further compare the results of using these segmentations as input for calculating the burst suppression probability (BSP), a continuous measure of depth-of-suppression.

RESULTS

Automated segmentation was comparable to manual segmentation, i.e. algorithm-vs-human agreement was comparable to human-vs-human agreement, as judged by comparing raw EEG segmentations or the derived BSP signals. Results were robust to modest variations in algorithm parameter settings.

CONCLUSIONS

Our automated method satisfactorily segments burst suppression data across a wide range adult ICU EEG patterns. Performance is comparable to or exceeds that of manual segmentation by human electroencephalographers.

SIGNIFICANCE

Automated segmentation of burst suppression EEG patterns is an essential component of quantitative brain activity monitoring in critically ill and anesthetized adults. The segmentations produced by our algorithm provide a basis for accurate tracking of suppression depth.

[Usefulness of Bispectral Index (BIS) monitoring for early detection of cerebral hypoperfusions]

OBJECTIVES

The goal of the study was to assess whether clinically significant cerebral hypoperfusion in awake patients would be associated with some alterations in the values of the bispectral index (BIS) monitoring.

STUDY DESIGN

Observational study.

POPULATION AND METHODS

We monitored the BIS during endovascular carotid artery occlusion testing in awake patients.

RESULTS

Twenty-eight patients were included. Twenty-one adequately tolerated the procedure. Their BIS value remained stable throughout the procedure. Four patients had poor angiographic tolerance, but no clinical symptoms. Their BIS value slightly decreased during the test (minimal BIS: 83 [79-87]). Three patients had poor clinical and angiographic tolerance of the occlusion. They all experienced an immediate and dramatic decrease in their BIS value (minimal BIS: ipsilateral to clamping: 50 [45-60]; contralateral to clamping: 48 [45-52]). In all patients, the clinical symptoms and the BIS normalized after deflating the occlusion balloon.

CONCLUSION

In awake patients, the observed values of the BIS monitoring seem to be associated with clinically relevant cerebral hypoperfusion.

Clinical review: Continuous and simplified electroencephalography to monitor brain recovery after cardiac arrest

There has been a dramatic change in hospital care of cardiac arrest survivors in recent years, including the use of target temperature management (hypothermia). Clinical signs of recovery or deterioration, which previously could be observed, are now concealed by sedation, analgesia, and muscle paralysis. Seizures are common after cardiac arrest, but few centers can offer high-quality electroencephalography (EEG) monitoring around the clock. This is due primarily to its complexity and lack of resources but also to uncertainty regarding the clinical value of monitoring EEG and of treating post-ischemic electrographic seizures. Thanks to technical advances in recent years, EEG monitoring has become more available. Large amounts of EEG data can be linked within a hospital or between neighboring hospitals for expert opinion. Continuous EEG (cEEG) monitoring provides dynamic information and can be used to assess the evolution of EEG patterns and to detect seizures. cEEG can be made more simple by reducing the number of electrodes and by adding trend analysis to the original EEG curves. In our version of simplified cEEG, we combine a reduced montage, displaying two channels of the original EEG, with amplitude-integrated EEG trend curves (aEEG). This is a convenient method to monitor cerebral function in comatose patients after cardiac arrest but has yet to be validated against the gold standard, a multichannel cEEG. We recently proposed a simplified system for interpreting EEG rhythms after cardiac arrest, defining four major EEG patterns. In this topical review, we will discuss cEEG to monitor brain function after cardiac arrest in general and how a simplified cEEG, with a reduced number of electrodes and trend analysis, may facilitate and improve care.

Recommendations on the use of EEG monitoring in critically ill patients: consensus statement from the neurointensive care section of the ESICM

OBJECTIVES

Recommendations for EEG monitoring in the ICU are lacking. The Neurointensive Care Section of the ESICM assembled a multidisciplinary group to establish consensus recommendations on the use of EEG in the ICU.

METHODS

A systematic review was performed and 42 studies were included. Data were extracted using the PICO approach, including: (a) population, i.e. ICU patients with at least one of the following: traumatic brain injury, subarachnoid hemorrhage, intracerebral hemorrhage, stroke, coma after cardiac arrest, septic and metabolic encephalopathy, encephalitis, and status epilepticus; (b) intervention, i.e. EEG monitoring of at least 30 min duration; (c) control, i.e. intermittent vs. continuous EEG, as no studies compared patients with a specific clinical condition, with and without EEG monitoring; (d) outcome endpoints, i.e. seizure detection, ischemia detection, and prognostication. After selection, evidence was classified and recommendations developed using the GRADE system.

RECOMMENDATIONS

The panel recommends EEG in generalized convulsive status epilepticus and to rule out nonconvulsive seizures in brain-injured patients and in comatose ICU patients without primary brain injury who have unexplained and persistent altered consciousness. We suggest EEG to detect ischemia in comatose patients with subarachnoid hemorrhage and to improve prognostication of coma after cardiac arrest. We recommend continuous over intermittent EEG for refractory status epilepticus and suggest it for patients with status epilepticus and suspected ongoing seizures and for comatose patients with unexplained and persistent altered consciousness.

CONCLUSIONS

EEG monitoring is an important diagnostic tool for specific indications. Further data are necessary to understand its potential for ischemia assessment and coma prognostication.

Electroencephalographic monitoring in comatose survivors of cardiac arrest

Electroencephalography (EEG) monitoring is an important tool in the management of comatose survivors of cardiac arrest. The results serve to predict the neurological outcome, identify postanoxic status epilepticus, and assess the effectiveness of antiepileptic treatments. Continuous EEG monitoring might seem the most attractive option but is costly and requires the continuous availability of an expert to interpret the findings. Alvarez and colleagues compared repeated standard EEG to continuous EEG monitoring in comatose survivors of cardiac arrest. They found close agreement between these two strategies. However, their results do not constitute evidence of similar performance. In comatose survivors of cardiac arrest, repeated standard EEG should be used only when continuous EEG monitoring is unavailable.