Stimulus-induced EEG-patterns and outcome after cardiac arrest

A novel nomogram for predicting the prognosis of critically ill patients with EEG patterns exhibiting stimulus-induced rhythmic, periodic, or ictal discharges

OBJECTIVES

To explore the factors associated with poor prognosis in critically ill patients with Electroencephalogram (EEG) patterns exhibiting stimulus-induced rhythmic, periodic, or ictal discharges (SIRPIDs), and to construct a prognostic prediction model.

METHODS

This study included a total of 53 critically ill patients with EEG patterns exhibiting SIRPIDs who were admitted to the First Affiliated Hospital of Chongqing Medical University from May 2023 to March 2024. Patients were divided into two groups based on their Modified Rankin Scale (mRS) scores at discharge: good prognosis group (0-3 points) and poor prognosis group (4-6 points). Retrospective analyses were performed on the clinical and EEG parameters of patients in both groups. Logistic regression analysis was applied to identify the risk factors related to poor prognosis in critically ill patients with EEG patterns exhibiting SIRPIDs; a risk prediction model for poor prognosis was constructed, along with an individualized predictive nomogram model, and the predictive performance and consistency of the model were evaluated.

RESULTS

Multivariate logistic regression analysis revealed that APACHE II score (OR=1.217, 95 %CI=1.030∼1.438), slow frequency bands or no obvious brain electrical activity (OR=8.720, 95 %CI=1.220∼62.313), and no sleep waveforms (OR=9.813, 95 %CI=1.371∼70.223) were independent risk factors for poor prognosis in patients. A regression model established based on multivariate logistic regression analysis had an area under the curve of 0.902. The model's accuracy was 90.60 %, with a sensitivity of 92.86 % and a specificity of 89.70 %. The nomogram model, after internal validation, showed a concordance index of 0.904.

CONCLUSIONS

A high APACHE II score, EEG patterns with slow frequency bands or no obvious brain electrical activity, and no sleep waveforms were independent risk factors for poor prognosis in patients with SIRPIDs. The nomogram model constructed based on these factors had a favorably high level of accuracy in predicting the risk of poor prognosis and held certain reference and application value for clinical neurofunctional assessment and prognostic determination.

The predictive value of highly malignant EEG patterns after cardiac arrest: evaluation of the ERC-ESICM recommendations

PURPOSE

The 2021 guidelines endorsed by the European Resuscitation Council (ERC) and the European Society of Intensive Care Medicine (ESICM) recommend using highly malignant electroencephalogram (EEG) patterns (HMEP; suppression or burst-suppression) at > 24 h after cardiac arrest (CA) in combination with at least one other concordant predictor to prognosticate poor neurological outcome. We evaluated the prognostic accuracy of HMEP in a large multicentre cohort and investigated the added value of absent EEG reactivity.

METHODS

This is a pre-planned prognostic substudy of the Targeted Temperature Management trial 2. The presence of HMEP and background reactivity to external stimuli on EEG recorded > 24 h after CA was prospectively reported. Poor outcome was measured at 6 months and defined as a modified Rankin Scale score of 4-6. Prognostication was multimodal, and withdrawal of life-sustaining therapy (WLST) was not allowed before 96 h after CA.

RESULTS

845 patients at 59 sites were included. Of these, 579 (69%) had poor outcome, including 304 (36%) with WLST due to poor neurological prognosis. EEG was recorded at a median of 71 h (interquartile range [IQR] 52-93) after CA. HMEP at > 24 h from CA had 50% [95% confidence interval [CI] 46-54] sensitivity and 93% [90-96] specificity to predict poor outcome. Specificity was similar (93%) in 541 patients without WLST. When HMEP were unreactive, specificity improved to 97% [94-99] (p = 0.008).

CONCLUSION

The specificity of the ERC-ESICM-recommended EEG patterns for predicting poor outcome after CA exceeds 90% but is lower than in previous studies, suggesting that large-scale implementation may reduce their accuracy. Combining HMEP with an unreactive EEG background significantly improved specificity. As in other prognostication studies, a self-fulfilling prophecy bias may have contributed to observed results.

Implications of stimulus-induced, rhythmic, periodic, or ictal discharges (SIRPIDs) in hospitalized patients

Background

Stimulus-induced electroencephalographic (EEG) patterns are commonly seen in acutely ill patients undergoing continuous EEG monitoring. Despite ongoing investigations, the pathophysiology, therapeutic and prognostic significance of stimulus-induced rhythmic, periodic or ictal discharges (SIRPIDs) and how it applies to specific pathologies remain unclear. We aimed to investigate the clinical implications of SIRPIDs in hospitalized patients.

Methods

This is a retrospective single-center study of hospitalized patients from May 2016 to August 2017. We included patients above the age of 18 years who underwent >16 h of EEG monitoring during a single admission. We excluded patients with cardiac arrest and anoxic brain injury. Demographic data were obtained as well as admission GCS, and discharge modified Rankin Score (mRS). EEGs were reviewed for background activity in addition to epileptiform, periodic, and rhythmic patterns. The presence or absence of SIRPIDs was recorded. Our outcome was discharge mRS defined as good outcome, mRS 0-4, and poor outcome mRS, 5-6.

Results

A total of 351 patients were included in the final analysis. The median age was 63 years and 175 (50%) were women. SIRPIDs were identified in 82 patients (23.4%). Patients with SIRPIDs had a median initial GCS of 12 (IQR, 6-15) and a length of stay of 12 days (IQR, 6-15). They were more likely to have absent posterior dominant rhythm, decreased reactivity, and more likely to have spontaneous periodic and rhythmic patterns and higher frequency of burst suppression. After adjusting for baseline clinical variables, underlying disease type and severity, and EEG background features, the presence of SIRPIDs was also associated with poor outcomes classified as MRS 5 or 6 (OR 4.75 [2.74-8.24] p ≤ 0.0001).

Conclusion

In our cohort of hospitalized patients excluding anoxic brain injury, SIRPIDs were identified in 23.4% and were seen most commonly in patients with primary systemic illness. We found SIRPIDs were independently associated with poor neurologic outcomes. Several studies are indicated to validate these findings and determine the risks vs. benefits of anti-seizure treatment.