Background Frequency Patterns in Standard Electroencephalography as an Early Prognostic Tool in Out-of-Hospital Cardiac Arrest Survivors Treated with Targeted Temperature Management

Repetitive Electroencephalography as Biomarker for the Prediction of Survival in Patients with Post-Hypoxic Encephalopathy

Predicting survival in patients with post-hypoxic encephalopathy (HE) after cardiopulmonary resuscitation is a challenging aspect of modern neurocritical care. Here, continuous electroencephalography (cEEG) has been established as the gold standard for neurophysiological outcome prediction. Unfortunately, cEEG is not comprehensively available, especially in rural regions and developing countries. The objective of this monocentric study was to investigate the predictive properties of repetitive EEGs (rEEGs) with respect to 12-month survival based on data for 199 adult patients with HE, using log-rank and multivariate Cox regression analysis (MCRA). A total number of 59 patients (29.6%) received more than one EEG during the first 14 days of acute neurocritical care. These patients were analyzed for the presence of and changes in specific EEG patterns that have been shown to be associated with favorable or poor outcomes in HE. Based on MCRA, an initially normal amplitude with secondary low-voltage EEG remained as the only significant predictor for an unfavorable outcome, whereas all other relevant parameters identified by univariate analysis remained non-significant in the model. In conclusion, rEEG during early neurocritical care may help to assess the prognosis of HE patients if cEEG is not available.

Ion Shift Index at the Immediate Post-Cardiac Arrest Period as an Early Prognostic Marker in Out-of-Hospital Cardiac Arrest Survivors

The ion shift index (ISI) is a suggested marker to reflect the magnitude of ischemic damage. This study aimed to investigate the prognostic value of the ISI for predicting poor neurological outcomes at 6 months in comatose out-of-hospital cardiac arrest (OHCA) survivors by comparing it with the OHCA and Cardiac Arrest Hospital Prognosis (CAHP) scores. This observational registry-based cohort study included adult comatose OHCA survivors admitted to a tertiary care hospital in Korea between 2015 and 2021. The ISI was calculated using the serum electrolyte levels obtained within one hour of resuscitation. The primary outcome was poor neurological function (Cerebral Performance Category score of 3−5) at 6 months. Of the 250 OHCA survivors, 164 (65.6%) had poor neurological outcomes. These patients had a higher median ISI than those with good neurological outcomes (4.95 vs. 3.26, p < 0.001). ISI (adjusted odds ratio, 2.107; 95% confidence interval, 1.350−3.288, p = 0.001) was associated with poor neurological outcomes. The prognostic performance of ISI (area under the curve [AUC], 0.859) was similar to that of the OHCA score (AUC, 0.858; p = 0.968) and the CAHP score (AUC, 0.894; p = 0.183). ISI would be a prognostic biomarker for comatose OHCA survivors that is available during the immediate post-cardiac arrest period.

Background frequency can enhance the prognostication power of EEG patterns categories in comatose cardiac arrest survivors: a prospective, multicenter, observational cohort study

Background

We assessed the prognostic accuracy of the standardized electroencephalography (EEG) patterns (“highly malignant,” “malignant,” and “benign”) according to the EEG timing (early vs. late) and investigated the EEG features to enhance the predictive power for poor neurologic outcome at 1 month after cardiac arrest.

Methods

This prospective, multicenter, observational, cohort study using data from Korean Hypothermia Network prospective registry included adult patients with out-of-hospital cardiac arrest (OHCA) treated with targeted temperature management (TTM) and underwent standard EEG within 7 days after cardiac arrest from 14 university-affiliated teaching hospitals in South Korea between October 2015 and December 2018. Early EEG was defined as EEG performed within 72 h after cardiac arrest. The primary outcome was poor neurological outcome (Cerebral Performance Category score 3–5) at 1 month.

Results

Among 489 comatose OHCA survivors with a median EEG time of 46.6 h, the “highly malignant” pattern (40.7%) was most prevalent, followed by the “benign” (33.9%) and “malignant” (25.4%) patterns. All patients with the highly malignant EEG pattern had poor neurologic outcomes, with 100% specificity in both groups but 59.3% and 56.1% sensitivity in the early and late EEG groups, respectively. However, for patients with “malignant” patterns, 84.8% sensitivity, 77.0% specificity, and 89.5% positive predictive value for poor neurologic outcome were observed. Only 3.5% (9/256) of patients with background EEG frequency of predominant delta waves or undetermined had good neurologic survival. The combination of “highly malignant” or “malignant” EEG pattern with background frequency of delta waves or undetermined increased specificity and positive predictive value, respectively, to up to 98.0% and 98.7%.

Conclusions

The “highly malignant” patterns predicted poor neurologic outcome with a high specificity regardless of EEG measurement time. The assessment of predominant background frequency in addition to EEG patterns can increase the prognostic value of OHCA survivors.

Trial registration KORHN-PRO, NCT02827422. Registered 11 September 2016—Retrospectively registered.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13054-021-03823-y.

Complementary roles of neural synchrony and complexity for indexing consciousness and chances of surviving in acute coma

An open challenge in consciousness research is understanding how neural functions are altered by pathological loss of consciousness. To maintain consciousness, the brain needs synchronized communication of information across brain regions, and sufficient complexity in neural activity. Coordination of brain activity, typically indexed through measures of neural synchrony, has been shown to decrease when consciousness is lost and to reflect the clinical state of patients with disorders of consciousness. Moreover, when consciousness is lost, neural activity loses complexity, while the levels of neural noise, indexed by the slope of the electroencephalography (EEG) spectral exponent decrease. Although these properties have been well investigated in resting state activity, it remains unknown whether the sensory processing network, which has been shown to be preserved in coma, suffers from a loss of synchronization or information content. Here, we focused on acute coma and hypothesized that neural synchrony in response to auditory stimuli would reflect coma severity, while complexity, or neural noise, would reflect the presence or loss of consciousness. Results showed that neural synchrony of EEG signals was stronger for survivors than non-survivors and predictive of patients' outcome, but indistinguishable between survivors and healthy controls. Measures of neural complexity and neural noise were not informative of patients' outcome and had high or low values for patients compared to controls. Our results suggest different roles for neural synchrony and complexity in acute coma. Synchrony represents a precondition for consciousness, while complexity needs an equilibrium between high or low values to support conscious cognition.

EEG patterns and their correlations with short- and long-term mortality in patients with hypoxic encephalopathy

OBJECTIVE

To analyze the association between electroencephalographic (EEG) patterns and overall, short- and long-term mortality in patients with hypoxic encephalopathy (HE).

METHODS

Retrospective, mono-center analysis of 199 patients using univariate log-rank tests (LR) and multivariate cox regression (MCR).

RESULTS

Short-term mortality, defined as death within 30-days post-discharge was 54.8%. Long-term mortality rates were 69.8%, 71.9%, and 72.9%, at 12-, 24-, and 36-months post-HE, respectively. LR revealed a significant association between EEG suppression (SUP) and short-term mortality, and identified low voltage EEG (LV), burst suppression (BSP), periodic discharges (PD) and post-hypoxic status epilepticus (PSE) as well as missing (aBA) or non-reactive background activity (nrBA) as predictors for overall, short- and long-term mortality. MCR indicated SUP, LV, BSP, PD, aBA and nrBA as significantly associated with overall and short-term mortality to varying extents. LV and BSP were significant predictors for long-term mortality in short-term survivors. Rhythmic delta activity, stimulus induced rhythmic, periodic or ictal discharges and sharp waves were not significantly associated with a higher mortality.

CONCLUSION

The presence of several specific EEG patterns can help to predict overall, short- and long-term mortality in HE patients.

SIGNIFICANCE

The present findings may help to improve the challenging prognosis estimation in HE patients.

Prediction of Neurologically Intact Survival in Cardiac Arrest Patients without Pre-Hospital Return of Spontaneous Circulation: Machine Learning Approach

Current multimodal approaches for the prognostication of out-of-hospital cardiac arrest (OHCA) are based mainly on the prediction of poor neurological outcomes; however, it is challenging to identify patients expected to have a favorable outcome, especially before the return of spontaneous circulation (ROSC). We developed and validated a machine learning-based system to predict good outcome in OHCA patients before ROSC. This prospective, multicenter, registry-based study analyzed non-traumatic OHCA data collected between October 2015 and June 2017. We used information available before ROSC as predictor variables, and the primary outcome was neurologically intact survival at discharge, defined as cerebral performance category 1 or 2. The developed models’ robustness were evaluated and compared with various score metrics to confirm their performance. The model using a voting classifier had the best performance in predicting good neurological outcome (area under the curve = 0.926). We confirmed that the six top-weighted variables predicting neurological outcomes, such as several duration variables after the instant of OHCA and several electrocardiogram variables in the voting classifier model, showed significant differences between the two neurological outcome groups. These findings demonstrate the potential utility of a machine learning model to predict good neurological outcome of OHCA patients before ROSC.

Cardiac Magnetic Resonance Imaging for Nonischemic Cardiac Disease in Out-of-Hospital Cardiac Arrest Survivors Treated with Targeted Temperature Management: A Multicenter Retrospective Analysis

(1) Background: Cardiac magnetic resonance (CMR) imaging is an emerging tool for investigating nonischemic cardiomyopathies and cardiac systemic disease. However, data on the cardiac arrest population are limited. This study aimed to evaluate the usefulness of CMR imaging in out-of-hospital cardiac arrest (OHCA) survivors treated with targeted temperature management (TTM). (2) Methods: We conducted the retrospective observational study using a multicenter registry of adult non-traumatic comatose OHCA survivors who underwent TTM between January 2010 and December 2019. Of the 949 patients, 389 with OHCA of non-cardiac cause, 145 with significant lesions in the coronary artery, 151 who died during TTM, 81 without further evaluation due to anticipated poor neurological outcome, and 51 whose etiology is underlying disease were excluded. In 36 of the 132 remaining patients, the etiologies included variant angina, long QT syndrome, and complete atrioventricular block in ancillary studies. Fifty-six patients were diagnosed idiopathic ventricular fibrillation without CMR. (3) Results: CMR imaging was performed in the remaining 40 patients with cardiac arrest of unknown cause. The median time from cardiac arrest to CMR imaging was 10.1 days. The CMR finding was normal in 23 patients, non-diagnostic in 12, and abnormal in 5, which suggested non-ischemic cardiomyopathy but did not support the final diagnosis. (4) Conclusions: CMR imaging may not be useful for identifying unknown causes of cardiac arrest in OHCA survivors treated with targeted temperature management without definitive diagnosis even after coronary angiography, echocardiography, and electrophysiology studies. However, further large-scale studies will be needed to confirm these findings.