Diagnostic accuracy of quantitative EEG to detect delayed cerebral ischemia after subarachnoid hemorrhage: A preliminary study

Predictive Value of Quantitative Electroencephalogram Combined with Transcranial Doppler Ultrasound in Delayed Cerebral Ischemia after Subarachnoid Hemorrhage

OBJECTIVE

To explore the predictive value of transcranial Doppler ultrasound (TCD) combined with quantitative electroencephalogram (QEEG) in delayed cerebral ischemia (DCI) caused by aneurysmal subarachnoid hemorrhage (aSAH).

METHODS

The participants were 105 patients with aSAH treated from June 2020 to December 2022. Patients were divided into DCI group (n = 34) and non-DCI group (n = 71) according to the presence of DCI 14 days after onset. Further comparison was conducted on the baseline data as well as the parameters of QEEG and TCD within 24 hours after admission. Multivariate logistic analysis was performed to investigate risk factors related to DCI within 14 days of admission in aSAH patients.

RESULTS

There were significant differences in the comparison of the proportion of Hunt-Hess grading, relative δ power (RDP), relative α power (RAP), relative α/β power ratio (ADR), as well as peak systolic velocity (Vs), mean blood flow velocity (MBFV) and pulsatility index (PI) of middle cerebral artery between the two groups (P < 0.05). Furthermore, Logistic regression analysis revealed that ADR (odds ratio 1.668, 95% CI 1.369-4.345) and MBFV of middle cerebral artery (odds ratio 3.279, 95% CI 2.332-6.720) were risk factors for the occurrence of DCI in aSAH patients (P < 0.05). In addition, evaluation of the predictive value revealed that combined use of the 2 indicators showed the highest predictive value (area under the curve 0.959, 95% CI 0.896-0.993).

CONCLUSIONS

Patients with aSAH complicated by DCI have relatively higher MBFV of middle cerebral artery and ADR. Combined use of the 2 indicators can provide reference for early prediction of DCI in aSAH patients.

Electroencephalography as a Biomarker of Prognosis in Acute Brain Injury

Electroencephalography (EEG) is a noninvasive tool that allows the monitoring of cerebral brain function in critically ill patients, aiding with diagnosis, management, and prognostication. Specific EEG features have shown utility in the prediction of outcomes in critically ill patients with status epilepticus, acute brain injury (ischemic stroke, intracranial hemorrhage, subarachnoid hemorrhage, and traumatic brain injury), anoxic brain injury, and toxic-metabolic encephalopathy. Studies have also found an association between particular EEG patterns and long-term functional and cognitive outcomes as well as prediction of recovery of consciousness following acute brain injury. This review summarizes these findings and demonstrates the value of utilizing EEG findings in the determination of prognosis.

The Spectrum of Quantitative EEG Utilization Across North America: A Cross-Sectional Survey

BACKGROUND

Continuous electroencephalography (cEEG) is commonly used for neuromonitoring in pediatric intensive care units (PICU); however, there are barriers to real-time interpretation of EEG data. Quantitative EEG (qEEG) transforms the EEG signal into time-compressed graphs, which can be displayed at the bedside. A survey was designed to understand current PICU qEEG use.

METHODS

An electronic survey was sent to the Pediatric Neurocritical Care Research Group and Pediatric Status Epilepticus Research Group, and intensivists in 16 Canadian PICUs. Questions addressed demographics, qEEG acquisition and storage, clinical use, and education.

RESULTS

Fifty respondents from 39 institutions completed the survey (response rate 53% [39 of 74 institutions]), 76% (37 of 50) from the United States and 24% (12 of 50) from Canada. Over half of the institutions (22 of 39 [56%]) utilize qEEG in their ICUs. qEEG use was associated with having a neurocritical care (NCC) service, ≥200 NCC consults/year, ≥1500 ICU admissions/year, and ≥4 ICU EEGs/day (P < 0.05 for all). Nearly all users (92% [24 of 26]) endorsed that qEEG enhanced care of children with acute neurological injury. Lack of training in qEEG was identified as a common barrier [85% (22 of 26)]. Reviewing and reporting of qEEG was not standard at most institutions. Training was required by 14% (three of 22) of institutions, and 32% (seven of 22) had established curricula.

CONCLUSIONS

ICU qEEG was used at more than half of the institutions surveyed, but review, reporting, and application of this tool remained highly variable. Although providers identify qEEG as a useful tool in patient management, further studies are needed to define clinically meaningful pediatric trends, standardize reporting, and enhance educate bedside providers.

Bispectral index monitoring to detect delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage

PURPOSE

Evaluate the bispectral index (BIS) monitoring to detect delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH).

MATERIALS AND METHODS

A single-center prospective study in patients with aSAH. BIS monitoring was recorded during 25-120 min in two periods, within the initial 72 h (BIS1) and between days 4 and 6 (BIS2) from admission. The median for each exported BIS parameter was analyzed. Transcranial Doppler (TCD) sonography was simultaneously performed with BIS1 (TCD1) and BIS2 (TCD2) monitoring. A multivariate logistic regression model was built to identify the variables associated with DCI.

RESULTS

Sixty-four patients were included and 16 (25%) developed DCI. During BIS2 monitoring, significant differences were found in BIS value (left, p = 0.01; right, p = 0.009), 95% spectral edge frequency (left and right, p = 0.04), and total power (left and right, p = 0.04). In multivariable analysis, vasospasm on TCD2 (OR 42.8 [95% CI 3.1-573]; p = 0.005), a median BIS2 value <85 in one or both sides (OR 6.2 [95% CI 1.28-30]; p = 0.023), and age (OR 1.08 [95% CI 1.00-1.17]; p = 0.04) were associated with the development of DCI.

CONCLUSIONS

BIS value is the most useful BIS parameter for detecting DCI after aSAH. Pending further validation, BIS monitoring might be even more accurate than TCD.

The Utility of Quantitative EEG in Detecting Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

SUMMARY

In this review, we discuss the utility of quantitative EEG parameters for the detection of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage in the context of the complex pathophysiology of DCI and the limitations of current diagnostic methods. Because of the multifactorial pathophysiology of DCI, methodologies solely assessing blood vessel narrowing (vasospasm) are insufficient to detect all DCI. Quantitative EEG has facilitated the exploration of EEG as a diagnostic modality of DCI. Multiple quantitative EEG parameters such as alpha power, relative alpha variability, and alpha/delta ratio show reliable detection of DCI in multiple studies. Recent studies on epileptiform abnormalities suggest that their potential for the detection of DCI. Quantitative EEG is a promising, continuous, noninvasive, monitoring modality of DCI implementable in daily practice. Future work should validate these parameters in larger populations, facilitated by the development of automated detection algorithms and multimodal data integration.

Electroencephalography for detection of vasospasm and delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: a retrospective analysis and systematic review

OBJECTIVE

Good functional outcomes after aneurysmal subarachnoid hemorrhage (aSAH) are often dependent on early detection and treatment of cerebral vasospasm (CVS) and delayed cerebral ischemia (DCI). There is growing evidence that continuous monitoring with cranial electroencephalography (cEEG) can predict CVS and DCI. Therefore, the authors sought to assess the value of continuous cEEG monitoring for the detection of CVS and DCI in aSAH.

METHODS

The cerebrovascular database of a quaternary center was reviewed for patients with aSAH and cEEG monitoring between January 1, 2017, and July 31, 2019. Demographic data, cardiovascular risk factors, Glasgow Coma Scale score at admission, aneurysm characteristics, and outcomes were abstracted from the medical record. Patient data were retrospectively analyzed for DCI and angiographically assessed CVS. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and odds ratio for cEEG, transcranial Doppler ultrasonography (TCDS), CTA, and DSA in detecting DCI and angiographic CVS were calculated. A systematic literature review was conducted in accordance with PRISMA guidelines querying the PubMed, Cochrane Controlled Trials Register, Web of Science, and Embase databases.

RESULTS

A total of 77 patients (mean age 60 years [SD 15 years]; female sex, n = 54) were included in the study. Continuous cEEG monitoring detected DCI and angiographically assessed CVS with specificities of 82.9% (95% CI 66.4%–93.4%) and 94.4% (95% CI 72.7%–99.9%), respectively. The sensitivities were 11.1% (95% CI 3.1%–26.1%) for DCI (n = 71) and 18.8% (95% CI 7.2%–36.4%) for angiographically assessed CVS (n = 50). Furthermore, TCDS detected angiographically determined CVS with a sensitivity of 87.5% (95% CI 71.0%–96.5%) and specificity of 25.0% (95% CI 7.3%–52.4%). In patients with DCI, TCDS detected vasospasm with a sensitivity of 85.7% (95% CI 69.7%–95.2%) and a specificity of 18.8% (95% CI 7.2%–36.4%). DSA detected vasospasm with a sensitivity of 73.9% (95% CI 51.6%–89.8%) and a specificity of 47.8% (95% CI 26.8%–69.4%).

CONCLUSIONS

The study results suggest that continuous cEEG monitoring is highly specific in detecting DCI as well as angiographically assessed CVS. More prospective studies with predetermined thresholds and endpoints are needed to assess the predictive role of cEEG in aSAH.

Prediction and Risk Assessment Models for Subarachnoid Hemorrhage: A Systematic Review on Case Studies

Subarachnoid hemorrhage (SAH) is one of the major health issues known to society and has a higher mortality rate. The clinical factors with computed tomography (CT), magnetic resonance image (MRI), and electroencephalography (EEG) data were used to evaluate the performance of the developed method. In this paper, various methods such as statistical analysis, logistic regression, machine learning, and deep learning methods were used in the prediction and detection of SAH which are reviewed. The advantages and limitations of SAH prediction and risk assessment methods are also being reviewed. Most of the existing methods were evaluated on the collected dataset for the SAH prediction. In some researches, deep learning methods were applied, which resulted in higher performance in the prediction process. EEG data were applied in the existing methods for the prediction process, and these methods demonstrated higher performance. However, the existing methods have the limitations of overfitting problems, imbalance data problems, and lower efficiency in feature analysis. The artificial neural network (ANN) and support vector machine (SVM) methods have been applied for the prediction process, and considerably higher performance is achieved by using this method.

The management of delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage

Aneurysmal subarachnoid hemorrhage (SAH) is a rare event affecting relatively young patients therefore leading to a high social impact. The management of SAH follows a biphasic course with early brain injuries in the first 72 hours followed by a phase at risk of secondary deterioration due to delayed cerebral ischemia (DCI) in 20 to 30% patients. Cerebral infarction from DCI is the most preventable cause of mortality and morbidity after SAH. DCI prevention, early detection and treatment is therefore advocated. Formerly limited to the occurrence of vasospasm, DCI is now associated with multiple pathophysiological processes involving for instance the macrocirculation, the microcirculation, neurovascular units, and inflammation. Therefore, the therapeutic targets and management strategies are also evolving and are not only focused on proximal vasospasm. In this review, we describe the current knowledge of DCI pathophysiology. We then discuss the diagnosis strategies that may guide physicians at the bedside with a multimodal approach in the unconscious patient. We will present the prevention strategies that have proven efficient as well as future targets and present the therapeutic approach that is currently being developed when a DCI occurs.

Predictive Accuracy of Alpha-Delta Ratio on Quantitative Electroencephalography for Delayed Cerebral Ischemia in Patients with Aneurysmal Subarachnoid Hemorrhage: Meta-Analysis

OBJECTIVE

Delayed cerebral ischemia (DCI) is significantly related to death and unfavorable functional outcome in patients with aneurysmal subarachnoid hemorrhage (SAH). The association between alpha-delta ratio (ADR) on quantitative electroencephalography (EEG) and DCI has been reported in several previous studies, but their results are conflicting. This meta-analysis was conducted to assess the accuracy of ADR for DCI prediction in patients with aneurysmal SAH.

METHODS

PubMed and Embase were systematically searched for related records. Study selection and data collection were completed by 2 investigators. Sensitivity, specificity, and their 95% confidence intervals (CIs) were pooled. A summary receiver operating characteristic curve was plotted to show the pooled accuracy. Deeks funnel plot was used to evaluate publication bias.

RESULTS

Five studies were included in this meta-analysis. The pooled sensitivity and specificity of worsening ADR for DCI prediction in patients with aneurysmal SAH were 0.83 (95% CI 0.44-0.97) and 0.74 (95% CI 0.50-0.89), respectively. In addition, the area under the summary receiver operating characteristic curve was 0.84 (95% CI 0.81-0.87). No obvious publication bias was found using Deeks funnel plot (P = 0.29).

CONCLUSIONS

Worsening ADR on quantitative EEG is a reliable predictor of DCI in patients with aneurysmal SAH. Further studies are still needed to confirm the role of quantitative EEG in DCI prediction.

"Vasospasm mimic" after aneurysmal subarachnoid hemorrhage

BACKGROUND

Secondary brain injuries, such as delayed cerebral infarction (DCI), are the leading causes of disability after subarachnoid hemorrhage (SAH). Detecting DCI may be challenging, especially for patients presenting an altered level of consciousness.

CASE DESCRIPTION

We describe herein the case of a patient who developed acute hemiplegia four days after SAH, with raised blood flow velocities on transcranial Doppler (TCD), compatible with vasospasm. Finally, full work-up, using CT scan with perfusion CT, and continuous EEG, was consistent with non-convulsive seizures.

CONCLUSION

Multiple secondary complications (DCI, seizures, hydrocephalus) may occur after SAH but are clinically difficult to diagnose. A multimodal evaluation (TCD, CT or MRI, EEG) is useful in order to detect and treat late complications.