Risk factors that predict delayed seizure detection on continuous electroencephalogram (cEEG) in a large sample size of critically ill patients

Contribution of the EEG in the Diagnostic Workup of Patients with Transient Neurological Deficit and Acute Confusional State at the Emergency Department: The EMINENCE Study

Background/Objectives: To investigate the usefulness of an emergency electroencephalogram (emEEG) in the differential diagnosis of transient neurological deficits (TND) and acute confusional state (ACS). Methods: An analysis was performed on a subset of patients included in EMINENCE, a retrospective study of subjects admitted to the Emergency Department (ED) of our tertiary hospital over a 1-year period. The analysis was limited to patients with neurological symptoms/signs compatible with cerebral hemispheric origin or with an ACS of <24 h duration. We evaluated the usefulness of the emEEG in the diagnostic workup of TND and ACS. Results: Speech disorder (75.3%), hyposthenia (68.1%), and ACS (62.9%) were the signs/symptoms with the highest percentage of abnormal emEEGs, especially concerning epileptic discharges. Seizures (85.7%) and encephalopathy (74.3%) were the final diagnoses with the highest percentage of abnormal emEEGs, particularly epileptic discharges and focal slow waves in patients discharged with a diagnosis of seizures, and bilateral slow waves and generalized periodic discharges with triphasic morphology (GPDTM) in patients discharged with a diagnosis of encephalopathy. The presence/absence of epileptic discharges associated with focal slow waves could discriminate between seizures and vascular disease, especially in hyposthenia (100% of seizures when epileptic discharges were present, vs. 50% when absent). Migraine with aura (66%) and an unknown diagnosis (56%) were the final diagnoses with the most normal emEEG. The rapid timing of the emEEG recording compared to the patient's admission allowed us to perform the test in 29.5% of patients who were still symptomatic, of whom 79% had an abnormal emEEG. Conclusions: The emEEG mainly contributed to the diagnosis when speech disorder, hyposthenia, and ACS were the admission signs/symptoms, especially for the final diagnosis of seizures and encephalopathy.

The role of EEG in the emergency department: Its contribution to the patient's diagnostic-therapeutic pathway. The EMINENCE study

Objectives

To investigate the utility of the emergency electroencephalogram(emEEG) in the diagnostic work-up of patients admitted to the Emergency Department(ED).

Methods

Data from consecutive patients admitted to the ED during a 1-year period. We evaluated the usefulness of emEEGs based on the subsequent patient clinical management.

Results

1125 emEEGs from 1018 patients were analyzed. The overall usefulness of an emEEG was 86.7%, mainly influenced by its contribution to diagnosis(75.0%), often excluding initial working diagnosis(50.0%), and to patient management(78.0%). EmEEGs showed their best overall usefulness in Status Epilepticus(SE) and altered level of consciousness both in contributing to the final diagnosis and in patient management and therapeutic pathway. In speech and cognitive/behavioural disorders, emEEGs contributed to the diagnosis(80.6% and 79.8%, respectively), often excluding the initial suspicion of seizures/SE. Normal emEEGs contributed to diagnosis(79.0%), patient management(87.0%) and discharge to home(82.0%).

Conclusions

In ED, attending physicians have to make quick decisions about the diagnostic-therapeutic management of patients, and also the ruling out of the initial diagnosis and safely discharging the patient to home are also important goals.

Significance

This study provides valuable guidance to ED clinicians in selecting patients for an emEEG and evaluates its contribution to their diagnostic-therapeutic management.

The Role of Routine Electroencephalography in the Diagnosis of Seizures in Medical Intensive Care Units

Seizures should be diagnosed and treated to ensure optimal health outcomes in critically ill patients admitted in the medical intensive care unit (MICU). Continuous electroencephalography is still infrequently used in the MICU. We investigated the effectiveness of routine EEG (rEEG) in detecting seizures in the MICU. A total of 560 patients admitted to the MICU between October 2018 and March 2023 and who underwent rEEG were reviewed. Seizure-related rEEG constituted 47% of all rEEG studies. Totally, 39% of the patients experienced clinical seizures during hospitalization; among them, 48% experienced the seizure, and 13% experienced their first seizure after undergoing an rEEG study. Seventy-seven percent of the patients had unfavorable short-term outcomes. Patients with cardiovascular diseases were the most likely to have the suppression/burst suppression (SBS) EEG pattern and the highest mortality rate. The rhythmic and periodic patterns (RPPs) and electrographic seizure (ESz) EEG pattern were associated with seizures within 24 h after rEEG, which was also related to unfavorable outcomes. Significant predictors of death were age > 59 years, the male gender, the presence of cardiovascular disease, a Glasgow Coma Scale score ≤ 5, and the SBS EEG pattern, with a predictive performance of 0.737 for death. rEEG can help identify patients at higher risk of seizures. We recommend repeated rEEG in patients with ESz or RPP EEG patterns to enable a more effective monitoring of seizure activities.

Prevalence of non-convulsive seizures and electroencephalographic abnormalities in critically ill patients-A retrospective observational study

OBJECTIVE

Electroencephalographic (EEG) abnormalities especially non-convulsive status epilepticus (NCSE) have been found to be associated with worse outcomes in critically ill patients. We aimed to assess the prevalence of non-convulsive seizures and electroencephalographic abnormalities in critically ill patients. Furthermore, we aimed to investigate any association between the type of EEG abnormality and outcomes including ICU mortality and successful ICU discharge.

METHODS

This was a cross-sectional observational study carried out among critically ill patients in a mixed medical-surgical ICU from January 1, 2018 to May 15, 2020. A total of 178 records of 30 min bedside EEG records were found. EEG findings were grouped as normal, non-convulsive seizures (NCS), non-convulsive status epilepticus (NCSE), and other abnormalities. Descriptive analytical tools were used to characterize the case details in terms of the type of EEG abnormalities. Chi square test was used to describe the EEG abnormalities in terms of mortality. The status epilepticus severity scores (STESS) were further calculated for records with NCSE. These data were then analyzed for any association between STESS and mortality for cases with NCSE.

RESULTS

The prevalence of EEG abnormality in our cohort of all critically ill patients was found to be 7.3% (170/2234). Among the patients with altered sensorium in whom EEG was done, 42.9% had non-conclusive seizure activity with 25.2% in NCSE. Though the study was not adequately powered, there was a definite trend towards a lower proportion of successful ICU discharge rates seen among patients with higher STESS (>2) with only 33.3% being discharged for patients with a STESS of 6 versus 92.9% for those with STESS 3.

SIGNIFICANCE

When combined with a strong clinical suspicion, even a 30-min bedside EEG can result in detection of EEG abnormalities including NCS and NCSE. Hence, EEG should be regularly included in the evaluation of critically ill patients with altered sensorium.

PLAIN LANGUAGE SUMMARY

Electroencephalographic (EEG) abnormalities and seizures can have high prevalence in critically ill patients. These abnormalities notably, non-convulsive status epilepticus (NCSE) has been found to be associated with poor patient outcomes. This was a retrospective observational study analyzing 178 EEG records, from a mixed medical-surgical ICU. The indication for obtaining an EEG was based solely on the clinical suspicion of the treating physician. The study found a high prevalence of EEG abnormalities in 96.5% in whom it was obtained with 42.9% having any seizure activity and 28.8% having NCSE. The study was not powered for detection of association of the EEG abnormalities with clinical outcomes. However, a definite trend towards decreased chances of successful discharge from the ICU was seen. This study used strong clinical suspicion in patients with altered sensorium to obtain an EEG. High detection rates of EEG abnormalities were recorded in this study. Hence, combination of clinical judgement and EEG can improve detection of EEG abnormalities and NCSE.

Indications for continuous electroencephalographic (cEEG) monitoring: What do they tell us?

OBJECTIVE

While studies have explored clinical and EEG predictors of seizures on continuous EEG (cEEG), the role of cEEG indications as predictors of seizures has not been studied. Our study aims to fill this knowledge gap.

METHODS

We used the prospective cEEG database at Cleveland Clinic for the 2016 calendar year. Patients ≥ 18 years who underwent cEEG for the indication of altered mental status (AMS) and seizure-like events (SLE: motor or patient-reported events) were included. Baseline characteristics and EEG findings were compared between the two groups. Multivariable regression was used to compare the two groups and identify seizure detection risk factors.

RESULTS

Of 2227 patients (mean age 59.4 years) who met the inclusion criteria, 882 (50% females) underwent cEEG for AMS and 1345(51% females) for SLE. SLE patients were younger(OR: 0.988, CI: 0.98-0.99, p < 0.001), had longer monitoring(OR:1.04, CI:1.00-1.07, p = 0.033), were more likely to have epilepsy-related-breakthrough seizures(OR:25.9, CI:0.5.89-115, p < 0.001), psychogenic non-epileptic spells (OR:6.85, CI:1.60-29.3, p = 0.008), were more awake (p < 0.001) and more likely to be on anti-seizure medications(OR:1.60, CI:1.29-1.98, p < 0.001). On multivariable analysis, SLE was an independent predictor of seizure detection (OR: 2.60, CI: 1.77-3.88, p < 0.001).

SIGNIFICANCE

Our findings highlight the differences in patients undergoing cEEG for AMS vs. SLE. SLE as a cEEG indication represents an independent predictor of seizures on cEEG and, therefore, deserves special attention. Future multicenter studies are needed to validate our findings.

EEG-Update

Durch die rasante Entwicklung digitaler Computertechniken und neuer Analysemethoden hat sich ein neuer Ansatz zur Analyse der Hirnströme (quantitatives EEG) ergeben, die in verschiedenen klinischen Bereichen der Neurologie und Psychiatrie bereits Ergebnisse zeigen. Die neuen Möglichkeiten der Analyse des EEG durch Einsatz künstlicher Intelligenz (Deep Learning) und großer Datenmengen (Big Data) sowie telemedizinischer Datenübermittlung und Interaktion wird den Einsatz der Methode vermutlich in den nächsten Jahren erweitern.