Protocol for the electroencephalography guidance of anesthesia to alleviate geriatric syndromes (ENGAGES-Canada) study: A pragmatic, randomized clinical trial

Background:  There is some evidence that electroencephalography guidance of general anesthesia can decrease postoperative delirium after non-cardiac surgery.  There is limited evidence in this regard for cardiac surgery.  A suppressed electroencephalogram pattern, occurring with deep anesthesia, is associated with increased incidence of postoperative delirium (POD) and death.  However, it is not yet clear whether this electroencephalographic pattern reflects an underlying vulnerability associated with increased incidence of delirium and mortality, or whether it is a modifiable risk factor for these adverse outcomes. Methods:  The Electroe ncephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes ( ENGAGES-Canada) is an ongoing pragmatic 1200 patient trial at four Canadian sites.  The study compares the effect of two anesthetic management approaches on the incidence of POD after cardiac surgery.  One approach is based on current standard anesthetic practice and the other on electroencephalography guidance to reduce POD. In the guided arm, clinicians are encouraged to decrease anesthetic administration, primarily if there is electroencephalogram suppression and secondarily if the EEG index is lower than the manufacturers recommended value (bispectral index (BIS) or WAVcns below 40 or Patient State Index below 25).  The aim in the guided group is to administer the minimum concentration of anesthetic considered safe for individual patients.  The primary outcome of the study is the incidence of POD, detected using the confusion assessment method or the confusion assessment method for the intensive care unit; coupled with structured delirium chart review.  Secondary outcomes include unexpected intraoperative movement, awareness, length of intensive care unit and hospital stay, delirium severity and duration, quality of life, falls, and predictors and outcomes of perioperative distress and dissociation. Discussion:  The ENGAGES-Canada trial will help to clarify whether or not using the electroencephalogram to guide anesthetic administration during cardiac surgery decreases the incidence, severity, and duration of POD. Registration: ClinicalTrials.gov ( NCT02692300) 26/02/2016.

Protocol for the electroencephalography guidance of anesthesia to alleviate geriatric syndromes (ENGAGES-Canada) study: A pragmatic, randomized clinical trial

Background:  There is some evidence that electroencephalography guidance of general anesthesia can decrease postoperative delirium after non-cardiac surgery.  There is limited evidence in this regard for cardiac surgery.  A suppressed electroencephalogram pattern, occurring with deep anesthesia, is associated with increased incidence of postoperative delirium (POD) and death.  However, it is not yet clear whether this electroencephalographic pattern reflects an underlying vulnerability associated with increased incidence of delirium and mortality, or whether it is a modifiable risk factor for these adverse outcomes. Methods:  The Electroe ncephalography Guidance of Anesthesia to Alleviate Geriatric Syndromes ( ENGAGES-Canada) is an ongoing pragmatic 1200 patient trial at four Canadian sites.  The study compares the effect of two anesthetic management approaches on the incidence of POD after cardiac surgery.  One approach is based on current standard anesthetic practice and the other on electroencephalography guidance to reduce POD. In the guided arm, clinicians are encouraged to decrease anesthetic administration, primarily if there is electroencephalogram suppression and secondarily if the EEG index is lower than the manufacturers recommended value (bispectral index (BIS) or WAVcns below 40 or Patient State Index below 25).  The aim in the guided group is to administer the minimum concentration of anesthetic considered safe for individual patients.  The primary outcome of the study is the incidence of POD, detected using the confusion assessment method or the confusion assessment method for the intensive care unit; coupled with structured delirium chart review.  Secondary outcomes include unexpected intraoperative movement, awareness, length of intensive care unit and hospital stay, delirium severity and duration, quality of life, falls, and predictors and outcomes of perioperative distress and dissociation. Discussion:  The ENGAGES-Canada trial will help to clarify whether or not using the electroencephalogram to guide anesthetic administration during cardiac surgery decreases the incidence, severity, and duration of POD. Registration: ClinicalTrials.gov ( NCT02692300) 26/02/2016.

Automated detection of activity onset after postictal generalized EEG suppression

BACKGROUND

Sudden unexpected death in epilepsy (SUDEP) is a leading cause of premature death in patients with epilepsy. If timely assessment of SUDEP risk can be made, early interventions for optimized treatments might be provided. One of the biomarkers being investigated for SUDEP risk assessment is postictal generalized EEG suppression [postictal generalized EEG suppression (PGES)]. For example, prolonged PGES has been found to be associated with a higher risk for SUDEP. Accurate characterization of PGES requires correct identification of the end of PGES, which is often complicated due to signal noise and artifacts, and has been reported to be a difficult task even for trained clinical professionals. In this work we present a method for automatic detection of the end of PGES using multi-channel EEG recordings, thus enabling the downstream task of SUDEP risk assessment by PGES characterization.

METHODS

We address the detection of the end of PGES as a classification problem. Given a short EEG snippet, a trained model classifies whether it consists of the end of PGES or not. Scalp EEG recordings from a total of 134 patients with epilepsy are used for training a random forest based classification model. Various time-series based features are used to characterize the EEG signal for the classification task. The features that we have used are computationally inexpensive, making it suitable for real-time implementations and low-power solutions. The reference labels for classification are based on annotations by trained clinicians identifying the end of PGES in an EEG recording.

RESULTS

We evaluated our classification model on an independent test dataset from 34 epileptic patients and obtained an AUreceiver operating characteristic (ROC) (area under the curve) of 0.84. We found that inclusion of multiple EEG channels is important for better classification results, possibly owing to the generalized nature of PGES. Of among the channels included in our analysis, the central EEG channels were found to provide the best discriminative representation for the detection of the end of PGES.

CONCLUSION

Accurate detection of the end of PGES is important for PGES characterization and SUDEP risk assessment. In this work, we showed that it is feasible to automatically detect the end of PGES-otherwise difficult to detect due to EEG noise and artifacts-using time-series features derived from multi-channel EEG recordings. In future work, we will explore deep learning based models for improved detection and investigate the downstream task of PGES characterization for SUDEP risk assessment.

A lightweight convolutional neural network for assessing an EEG risk marker for sudden unexpected death in epilepsy

BACKGROUND

Convolutional neural network (CNN) has achieved state-of-art performance in many electroencephalogram (EEG) related studies. However, the application of CNN in prediction of risk factors for sudden unexpected death in epilepsy (SUDEP) remains as an underexplored area. It is unclear how the trade-off between computation cost and prediction power varies with changes in the complexity and depth of neural nets.

METHODS

The purpose of this study was to explore the feasibility of using a lightweight CNN to predict SUDEP. A total of 170 patients were included in the analyses. The CNN model was trained using clips with 10-s signals sampled from the original EEG. We implemented Hann function to smooth the raw EEG signal and evaluated its effect by choosing different strength of denoising filter. In addition, we experimented two variations of the proposed model: (1) converting EEG input into an "RGB" format to address EEG channels underlying spatial correlation and (2) incorporating residual network (ResNet) into the bottle neck position of the proposed structure of baseline CNN.

RESULTS

The proposed baseline CNN model with lightweight architecture achieved the best AUC of 0.72. A moderate noise removal step facilitated the training of CNN model by ensuring stability of performance. We did not observe further improvement in model's accuracy by increasing the strength of denoising filter.

CONCLUSION

Post-seizure slow activity in EEG is a potential marker for SUDEP, our proposed lightweight architecture of CNN achieved satisfying trade-off between efficiently identifying such biomarker and computational cost. It also has a flexible interface to be integrated with different variations in structure leaving room for further improvement of the model's performance in automating EEG signal annotation.

Equivocal significance of post-ictal generalized EEG suppression as a marker of SUDEP risk

PURPOSE

Our objective was to determine the significance of PGES as a possible EEG marker of increased risk for SUDEP and explore factors that influence PGES.

METHODS

We identified 17 patients who died due to definite or probable SUDEP and 52 living control patients with drug resistant focal epilepsy who underwent EEG monitoring and least one seizure recorded on EEG. We reviewed 305 seizures on EEG and when available, on video, for presence or absence of PGES, the duration of PGES immediately after seizure end, seizure type, state seizure occurred (sleep vs. wake), tonic duration and time from seizure onset to initial nursing intervention. We noted that majority (93% in SUDEP group and 83% living controls) with PGES had additional brief bursts of suppression. We measured the time from the end of seizure to end of last brief suppression to determine the time to final PGES.

RESULTS

SUDEP patients had statistically significant shorter PGES duration compared to living controls (unadjusted: -32.8s, 95%CI[-54.5, -11.2], adjusted: -39.5s, 95% CI[-59.4, -19.6]). SUDEP status was associated with longer time to final PGES compare to living controls, but this was not statistically significant. Earlier nursing intervention was associated with shorter seizure duration. PGES occurred only after GCS. Time to nursing intervention, tonic duration or state did not have a statistically significant effect on PGES.

CONCLUSIONS

PGES is an equivocal marker of increased SUDEP risk. Earlier nursing intervention is associated with shorter seizure duration and may play a role in reducing risk of SUDEP.

Nonseizure SUDEP: Sudden unexpected death in epilepsy without preceding epileptic seizures

OBJECTIVE

To describe the phenomenology of monitored sudden unexpected death in epilepsy (SUDEP) occurring in the interictal period where death occurs without a seizure preceding it.

METHODS

We report a case series of monitored definite and probable SUDEP where no electroclinical evidence of underlying seizures was found preceding death.

RESULTS

Three patients (two definite and one probable) had SUDEP. They had a typical high SUDEP risk profile with longstanding intractable epilepsy and frequent generalized tonic-clonic seizures (GTCS). All patients had varying patterns of respiratory and bradyarrhythmic cardiac dysfunction with profound electroencephalography (EEG) suppression. In two patients, patterns of cardiorespiratory failure were similar to those seen in some patients in the Mortality in Epilepsy Monitoring Units Study (MORTEMUS).

SIGNIFICANCE

SUDEP almost always occur postictally, after GTCS and less commonly after a partial seizure. Monitored SUDEP or near-SUDEP cases without a seizure have not yet been reported in literature. When nonmonitored SUDEP occurs in an ambulatory setting without an overt seizure, the absence of EEG information prevents the exclusion of a subtle seizure. These cases confirm the existence of nonseizure SUDEP; such deaths may not be prevented by seizure detection-based devices. SUDEP risk in patients with epilepsy may constitute a spectrum of susceptibility wherein some are relatively immune, death occurs in others with frequent GTCS with one episode of seizure ultimately proving fatal, while in others still, death may occur even in the absence of a seizure. We emphasize the heterogeneity of SUDEP phenomena.

Periictal autonomic dysfunction and generalized postictal EEG suppression in convulsive seizures arising from sleep and wakefulness

Sleep appears to be an independent risk factor of sudden unexpected death in epilepsy (SUDEP). We retrospectively determined the periictal electrophysiological characteristics of nocturnal and diurnal generalized convulsive seizures (GCSs) in 109 patients. Our data showed that preictal heart rate (HR) was significantly lower in 46 patients with nocturnal GCSs than in 63 patients with diurnal GCSs (p=0.002). However, there was no significant difference in postictal HR and respiratory rate (RR), total seizure duration, total convulsive phase, tonic phase, and clonic phase. Meanwhile, postictal generalized EEG suppression (PGES) was observed in 52.4% of the patients with diurnal GCSs and 67.4% of the patients with nocturnal GCSs. Duration of PGES was 38.2±17.3s in patients with diurnal GCSs and 49.5±21.7s in patients with nocturnal GCSs. There was also no significant difference in the prevalence (p=0.118) and duration (p=0.044, Bonferroni-corrected significant level: α=0.00625) of PGES in the two patient groups. Therefore, there is no clear evidence to attribute the SUDEP risk associated with sleep to postictal autonomic dysfunction and PGES, as compared to wakefulness.